CN110738331A - intelligent marine engine room system - Google Patents

Abstract

The invention provides intelligent ship cabin systems, which comprise a data acquisition subsystem, an auxiliary subsystem and an external subsystem, wherein the data acquisition subsystem is used for acquiring cabin equipment and working state perception data thereof, the auxiliary subsystem is used for providing a maintenance scheme of auxiliary decision and periodic maintenance of fault maintenance for the cabin equipment according to the acquired perception data, the overall architecture of the intelligent ship cabin system adopts a distributed mode to divide the intelligent ship cabin system into a plurality of subsystems, a communication interface protocol is used for reducing the coupling degree between the subsystems, different subsystems are controlled by different software, the subsystems are additionally arranged in a mode of calling the interfaces, the operation is more convenient and faster, the working environment and the running state of the cabin equipment are analyzed based on values obtained by monitoring of sensors, the possible faults or failures of the cabin equipment are diagnosed and predicted, the fault reasons and the corresponding maintenance scheme and evaluation result are timely output, and the service life of mechanical equipment is prolonged.

Description

intelligent marine engine room system

Technical Field

The invention relates to the field of intelligent ships, in particular to an intelligent ship cabin system.

Background

In view of the increasing operation cost, the complexity of ship operation and the increasingly strict requirements of environmental regulations, the shipping industry is increasing the technical investment on intelligent ships in recent years, and in the background of the big data era, the intellectualization of ships becomes the inevitable trend of the development of the fields of ship manufacture and shipping, meanwhile, the intelligent ships are also the field of clear and important development in the field of Chinese manufacturing 2025, represent the future direction of ships, and concern about the conversion upgrading of the shipping industry.

Disclosure of Invention

() problems to be solved

The invention provides intelligent marine engine room systems, and aims to solve the problems that faults of engine room equipment cannot be analyzed, corresponding auxiliary decisions are made for the faults, and potential faults and actual working cycles of the engine room equipment cannot be predicted, so that the service life of the engine room equipment is shortened in the prior art.

(II) technical scheme

In order to achieve the purpose, the invention adopts the main technical scheme that:

kinds of intelligent ship cabin systems, it includes data acquisition subsystem, auxiliary subsystem and external subsystem;

the data acquisition subsystem is used for acquiring sensing data of the cabin equipment in real time through a plurality of sensors, and converting and processing the sensing data to form standard data;

the auxiliary subsystem comprises a health evaluation module, an auxiliary decision module and a visual condition maintenance module;

the health evaluation module is used for analyzing and evaluating the operation state and health condition of the cabin equipment according to the standard data to form an analysis and evaluation report;

the auxiliary decision-making module is used for diagnosing the faults of the cabin equipment according to the analysis and evaluation report and the standard data, forming fault information, comparing the fault information with historical data stored in the external subsystem to form a comparison result, wherein the historical data comprises the standard data, the analysis and evaluation report, the maintenance scheme and the historical data of the time table, and making a maintenance scheme according to the comparison result;

the visual condition maintenance module is used for predicting the effective working period of the cabin equipment according to the analysis and evaluation report and the maintenance scheme and formulating a maintenance scheduling schedule;

the external subsystem is used for storing data generated by the data acquisition subsystem and the auxiliary subsystem and the historical data;

the comparing the fault information with historical data stored in the external subsystem to form a comparison result comprises:

combining the standard data, the analysis evaluation report, the maintenance schedule, and the schedule into a failure case, and storing the failure case in the external subsystem; the five tuple of the failure case is represented as:

case＝<T,E,M,D,P>

in the formula: t ═ T₁,t₂,...t_nIs a finite, non-empty set, representing the number of the failure instance; e ═ E₁,e₂,...e_nIs a finite set, representing descriptive information of the fault instance; m ═ M₁,m₂,...m_nIs a limited non-empty set, representing a symptom of the fault instance; d ═ D₁,d₂,...d_nIs a finite non-empty set, representing a fault conclusion caused by a fault symptom; p ═ P₁,p₂,...p_nIs a finite set, representing the maintenance scenario for a fault case;

the auxiliary decision module carries out similarity calculation on the fault information and the fault case, and a calculation formula of the similarity is represented as:

in the formula, sim_i(X_i,Y_i) Similarity between the fault information and the ith fault case is obtained; mu.s_iWeight coefficient of characteristic attribute of fault occurrence part, mu_iExpressed as:

in the formula, P_ijIs the value of the three-level comparison standard;

when mu is to be measured_iTo simplify to 1, the calculation formula of the similarity is expressed as:

in the formula, C_xThe number of fault phenomena is the fault information; c_yThe number of fault phenomena for the fault instance; c_x∩C_yThe number of fault phenomena matched with the fault case is taken as the fault information;

the comparison result is a similarity value, when the similarity value falls within a set range, the similarity value indicates that the fault information is similar to the fault case, and the auxiliary decision module makes the maintenance scheme which is the same as the fault case; otherwise, the assistant decision module acquires the maintenance scheme from the external subsystem.

Preferably, the health evaluation module analyzes the health condition of the marine main engine by adopting an instantaneous rotating speed method, and the fluctuation coefficient of the instantaneous rotating speed of the marine main engine is expressed as:

in the formula:

is the crankshaft instantaneous speed, omega is the average angular velocity;

the equilibrium equation for the motion of the marine vessel's main engine is expressed as:

in the formula: f is crankshaft moment of inertia, Q_XFor the work-producing output of diesel engines, Q_LIs the load average angular velocity;

thus, the equilibrium equation for the motion of the marine vessel's main engine is expressed as:

ω/F＝φ₁(θ)＝cons tan t

the instantaneous rotational speed of the marine main engine is expressed as:

in the formula: r is the crankshaft radius, lambda is the ratio of the crankshaft radius to the connecting rod length, L is the connecting rod length, m is the sum of the masses of the piston, the connecting rod, etc.,

is the initial phase;

thereby obtaining an expression of the instantaneous rotating speed fluctuation coefficient of the ship main engine:

preferably, the meaning of the tertiary comparison criteria is expressed as:

the auxiliary decision module calculates the characteristic attribute values of the fault case and the fault information and calculates a comparison value according to the characteristic attribute values; when r is_iGreater than r_jWhen is, p_ij1 is ═ 1; when r is_iIs equal to r_jWhen is, p_ij0.5; when r is_iLess than r_jWhen is, p_ij0; wherein r is_iFor the ith characteristic attribute value, r, of the fault case_jIs the jth characteristic attribute value, p, of the fault information_ijIs a comparison value of a three-level comparison standard;

the comparison value formation matrix N is represented as:

N＝(p_ij)_n*n

in the formula, n is the index number.

Preferably, the assistant decision module is further used for making an optimization scheme for operation and maintenance of the cabin equipment according to the analysis evaluation report and the standard data.

Preferably, the data acquisition subsystem transmits data by using a compressed sensing method, and the compressed sensing method includes:

carrying out sparse transformation on the standard data to obtain sparse data;

compressing the sparse data to remove zero-valued or nearly zero-valued coefficients to form transformed data;

sampling the transformation data at a low speed to obtain sampling data;

encoding and transmitting the sampling data;

and reconstructing the transmitted sampling data.

Preferably, the episode maintenance module is further configured to perform storage and access management on the schedule and manage configuration of the episode maintenance module.

Preferably, the aid decision module comprises a model library unit and an expert system;

the model library unit is used for storing quantitative models providing decision analysis capability;

the expert system comprises a knowledge acquisition subunit, a knowledge base and an inference machine;

the knowledge acquisition subunit is used for acquiring knowledge of the ship field from a knowledge source and converting the knowledge into a computer program;

the knowledge base is used for storing facts, heuristic knowledge of special or rules;

the inference machine is used for calling corresponding knowledge in the knowledge base, detecting the diagnosis object and analyzing and isolating the fault source according to the fault information.

Preferably, the data acquisition subsystem comprises a data module, and the data module comprises a data acquisition unit and a data processing unit;

the data acquisition unit is used for converting the perception data into digital data;

the data processing unit is used for performing operations including signal processing, synchronous or asynchronous averaging, algorithm calculation and feature extraction on the digital data and obtaining the result data.

Preferably, the data acquisition subsystem further comprises a sensing module, the sensing module acquires the working state and the operating environment of the cabin equipment through a sensor, and the cabin equipment comprises a main propulsion power device, an electric power device, a power generation power device, a boiler device and a steering engine device.

Preferably, the external subsystem comprises a database module and an information interaction module, the database module is used for storing and transmitting data generated by the data acquisition subsystem and the auxiliary subsystem, and the information interaction module is used for realizing connection of the ship end, the shore end and the mobile terminal in an information interaction mode.

(III) advantageous effects

The invention has the beneficial effects that: the intelligent ship cabin system is divided into a plurality of subsystems by adopting a distributed mode in the whole framework, a communication interface protocol is used, the coupling degree between the subsystems is reduced, different subsystems are controlled by adopting different software, and an interface calling mode is adopted when the subsystems are additionally arranged, so that the convenience is effectively improved;

based on the numerical values obtained by the monitoring of the sensors, the working environment and the running state of the cabin equipment are learned and analyzed, faults or failures which may occur to the cabin equipment are diagnosed and predicted, fault reasons, corresponding maintenance schemes and evaluation results are output in time, the maintenance cost of the cabin equipment is reduced, and the service life of the cabin equipment is prolonged.

Drawings

FIG. 1 is a basic architecture diagram of a smart marine engine room system of the present invention;

FIG. 2 is a system architecture diagram of the smart marine engine room system of the present invention;

fig. 3 is a flow chart of the compressed sensing signal encoding and decoding of the intelligent marine engine room system of the invention.

[ description of reference ]

1: a data acquisition subsystem; 11: a sensing module; 12: a data module;

2: an auxiliary subsystem; 21: a health assessment module; 22: an auxiliary decision module; 23: a visual condition maintenance module;

3: an external subsystem; 31: a database module; 32: an information interaction module;

4: and executing the subsystem.

Detailed Description

For the purpose of better explaining the present invention and to facilitate understanding, the present invention will be described in detail by way of specific embodiments with reference to the accompanying drawings.

The invention provides intelligent marine engine room systems, which comprise a data acquisition subsystem 1, an auxiliary subsystem 2 and an external subsystem 3, wherein the data acquisition subsystem 1 is used for acquiring sensing data of an engine room device in real time through a plurality of sensors and converting and processing the sensing data to form standard data, the auxiliary subsystem 2 comprises a health evaluation module 21, an auxiliary decision module 22 and an episodic maintenance module 23, the health evaluation module 21 is used for analyzing and evaluating the operation state and the health condition of the engine room device according to the standard data to form an analysis evaluation report, the auxiliary decision module 22 is used for diagnosing faults of the engine room device and forming fault information according to the analysis evaluation report and the standard data, a comparison result is formed according to the fault information and historical data stored in the external subsystem 3, the historical data comprises the standard data, the analysis evaluation report, the maintenance scheme and historical data of a time table, the episodic maintenance scheme is formed according to the comparison result, the episodic maintenance schedule is formed according to the analysis evaluation report and the historical data stored in the maintenance scheme, the external subsystem 3 is used for storing the historical data generated by the data acquisition subsystem 1 and the auxiliary subsystem 2, and the historical data comprises:

combining the standard data, the analysis and evaluation report, the maintenance scheme and the schedule into a fault case, and storing the fault case in an external subsystem 3; the quintuple for the failure case is represented as:

case＝<T,E,M,D,P>

in the formula: t ═ T₁,t₂,...t_nThe is a finite non-empty set, representing the number of the fault instance; e ═ E₁,e₂,...e_nThe description information of the fault case is represented by a limited set; m ═ M₁,m₂,...m_nIs a finite, non-empty set, representing the symptoms of a fault case; d ═ D₁,d₂,...d_nIs a finite non-empty set, representing a fault conclusion caused by a fault symptom; p ═ P₁,p₂,...p_nThe maintenance scheme is a limited set and represents a fault case;

the assistant decision module 22 calculates the similarity between the fault information and the fault case, and the calculation formula of the similarity is as follows:

in the formula, sim_i(X_i,Y_i) Similarity between the fault information and the ith fault case; mu.s_iWeight coefficient of characteristic attribute of fault occurrence part, mu_iExpressed as:

in the formula, P_ijIs the value of the three-level comparison standard;

when mu is to be measured_iTo simplify to 1, the calculation formula of the similarity is expressed as:

in the formula, C_xThe number of fault phenomena as fault information; c_yNumber of fault phenomena for a fault instance; c_x∩C_yThe number of fault phenomena of which the fault information is matched with the fault case is obtained;

the comparison result is a similarity value, when the similarity value falls within a set range, the fault information and the fault case are similar, and the auxiliary decision module 22 makes a maintenance scheme the same as that of the fault case; otherwise, the aid decision module 22 obtains a maintenance solution from the external subsystem 3.

Preferably, the health evaluation module 21 analyzes the health condition of the marine main engine by using an instantaneous rotating speed method, and the fluctuation coefficient of the instantaneous rotating speed of the marine main engine is expressed as:

in the formula:

is the crankshaft instantaneous speed, omega is the average angular velocity;

the equilibrium equation for the motion of the marine vessel's main engine is expressed as:

in the formula: f is crankshaft moment of inertia, Q_XFor the work-producing output of diesel engines, Q_LIs the load average angular velocity; thus, the equilibrium equation for the motion of the marine vessel's main engine is expressed as:

ω/F＝φ₁(θ)＝cons tan t

the instantaneous rotational speed of the marine main engine is expressed as:

in the formula: r is the crankshaft radius, lambda is the ratio of the crankshaft radius to the connecting rod length, L is the connecting rod length, m is the sum of the masses of the piston, the connecting rod, etc.,

is the initial phase; thereby obtaining an expression of the instantaneous rotating speed fluctuation coefficient of the ship main engine:

preferably, the meaning of the tertiary comparison criteria is expressed as: the assistant decision module 22 calculates the characteristic attribute values of the fault case and the fault information, and calculates a comparison value according to the characteristic attribute values; when r is_iGreater than r_jWhen is, p_ij1 is ═ 1; when r is_iIs equal to r_jWhen is, p_ij0.5; when r is_iLess than r_jWhen is, p_ij0; wherein r is_iFor the ith characteristic attribute value of the fault case, r_jFor the jth characteristic attribute value, p, of the fault information_ijIs a comparison value of a three-level comparison standard;

the comparison value formation matrix N is represented as:

N＝(p_ij)_n*n

in the formula, n is the index number.

Preferably, the aid decision module 22 is also used to make an optimization of the operation and maintenance of the cabin equipment based on the analysis evaluation reports and the standard data.

Preferably, the data acquisition subsystem 1 adopts a compressed sensing method to transmit data, and the compressed sensing method includes: carrying out sparse transformation on the standard data to obtain sparse data; compressing the sparse data to remove zero-valued or nearly zero-valued coefficients to form transformed data; sampling the transformed data at a low speed to obtain sampled data; encoding and transmitting the sampled data; and reconstructing the transmitted sampling data.

Preferably, the episode maintenance module 23 is further configured to manage storage and access of the schedule and to manage configuration of the episode maintenance module 23.

Preferably, the decision-making assisting module 22 comprises a model base unit for storing a quantitative model providing decision analysis capability and an expert system comprising a knowledge acquisition subunit, a knowledge base and an inference engine, wherein the knowledge acquisition subunit is used for acquiring knowledge of the ship field from a knowledge source and converting the knowledge into a computer program, the knowledge base is used for storing facts, heuristic knowledge or rules of the expert , and the inference engine is used for calling corresponding knowledge in the knowledge base, detecting a diagnosis object, analyzing and isolating a fault source according to fault information.

Preferably, the data acquisition subsystem 1 comprises a data module 12, and the data module 12 comprises a data acquisition unit and a data processing unit; the data acquisition unit is used for converting the sensing data into digital data; the data processing unit is used for performing operations including signal processing, synchronous or asynchronous averaging, algorithm calculation and feature extraction on the digital data and obtaining result data.

Preferably, the data acquisition subsystem 1 further includes a sensing module 11, the sensing module 11 acquires sensing data of the working state and the operating environment of the cabin equipment through a sensor, and the cabin equipment includes a main propulsion power device, an electric power device, a power generation power device, a boiler device, and a steering engine device.

Preferably, the external subsystem 3 includes a database module 31 and an information interaction module 32, the database module 31 is used for storing and transmitting data generated by the data acquisition subsystem 1 and the auxiliary subsystem 2, and the information interaction module 32 is used for realizing connection of the ship end, the shore end and the mobile terminal in an information interaction manner.

Specifically, as shown in fig. 1, fig. 1 is a basic architecture diagram of the smart ship cabin system of the present invention, and the smart ship cabin system includes a data acquisition subsystem 1, an auxiliary subsystem 2, and an external subsystem 3; the data acquisition subsystem 1 is used for acquiring sensing data of the cabin equipment and the operating environment of the cabin equipment in real time through a plurality of sensors, and converting and processing the sensing data to form standard data; the auxiliary subsystem 2 comprises a health evaluation module 21, an auxiliary decision module 22 and a visual condition maintenance module 23; the health evaluation module 21 is used for analyzing and evaluating the operation state and health condition of the cabin equipment according to standard data to form an analysis and evaluation report; the auxiliary decision module 22 is used for diagnosing the fault of the cabin equipment according to the analysis and evaluation report and the standard data, forming fault information, comparing the fault information with historical data stored in the external subsystem 3 to form a comparison result, wherein the historical data comprises the standard data, the analysis and evaluation report, a maintenance scheme and historical data of a schedule, and making the maintenance scheme according to the comparison result; the visual maintenance module 23 is used for predicting the effective working period of the cabin equipment according to the analysis evaluation report and the maintenance scheme and making a maintenance scheduling schedule; the external subsystem 3 is used to store data generated by the data acquisition subsystem 1 and the auxiliary subsystem 2.

The data acquisition subsystem 1 is based on various sensors, data acquisition equipment and the like, senses and monitors the working state and working environment of the ship cabin equipment in real time, and transmits the sensed data, so that the ship navigation safety and the cabin equipment working efficiency are improved. The equipment monitored by the data acquisition subsystem 1 mainly comprises a main propulsion power device, an electric power supply and distribution device, a power generation power device, a boiler and accessory equipment, a steering engine device and accessory equipment and auxiliary machinery.

The monitoring of the main propulsion power plant is divided into external monitoring and internal monitoring. Wherein, the information that outside control detected has: starting air/control air, strong current/weak point of an external power supply, pressure/temperature of cooling seawater, pressure/temperature of inlet lubricating oil, pressure/temperature of inlet cooling water, pressure/temperature/viscosity of inlet fuel oil, liquid level monitoring oil/water and the like; the information detected by internal monitoring is as follows: exhaust gas temperature, supercharger oil/gas pressure/temperature, cooling fresh water pressure/temperature, lubricating oil pressure/temperature, crankcase oil pressure/oil temperature, axial vibration, rotating speed output shaft front/back and the like;

the monitoring of the power generation power device comprises detection of generator insulation condition, detection of generator bearing temperature and oil level, detection of phase current and excitation voltage current of a three-phase power supply output by a generator and the like; monitoring of the power generation powerplant includes both external monitoring and internal monitoring. Wherein, the external monitoring and detecting information mainly comprises liquid level monitoring, external power supply condition, cooling water and seawater inlet temperature/pressure, lubricating oil inlet temperature/pressure, fuel oil inlet temperature/pressure/viscosity/flow and the like; the information of internal monitoring detection mainly comprises cylinder compression pressure and explosion pressure detection, rotating speed detection, lubricating oil inlet temperature/pressure, cooling fresh water temperature/pressure, common rail fuel pressure, exhaust gas temperature and the like.

The monitoring of the boiler and the accessory equipment is divided into internal monitoring and external monitoring. Wherein, the information that outside control detected has: external steam pressure, external power supply, fuel pressure/temperature/viscosity/oil product, water tank level, water supply pressure and the like; the information detected by internal monitoring is as follows: fuel conversion, combustion chamber monitoring, automatic ignition program, nozzle control, up-down pollution discharge, water level control, steam pressure control and the like.

The pressure sensor is mainly applied to cooling seawater, feeding lubricating oil, feeding fuel oil, a compressor, a cam and the like in the main propulsion power device; compression pressure and explosion pressure in a cylinder in the power generation power device, cooling fresh water and the like; and external steam pressure, water supply pressure, etc. in the boiler and the auxiliary equipment; detecting the pressure of hydraulic oil and the like in the steering engine device and the accessory equipment, and converting the monitored pressure value into an electric signal for transmission;

the flow sensor is mainly applied to the detection of the flow of fuel oil entering the device in the power generation device, and converts the monitored flow value into an electric signal for transmission;

the temperature sensor is mainly applied to cooling seawater, smoke exhaust, engine inlet lubricating oil, engine inlet fuel oil, a supercharger and the like in the main propulsion power device; fuel oil, cooling fresh water and lubricating oil in the power generation power device enter the machine, and the like; detecting the temperature of fuel oil in the boiler and the accessory equipment, and converting the temperature value obtained by monitoring into an electric signal for transmission;

the rotating speed sensor is mainly applied to a rotating speed output shaft and the like in the main propulsion power device; detecting the rotating speed of devices such as a diesel engine, a supercharger and the like in the power generation device, and converting the monitored rotating speed value into an electric signal for transmission;

the liquid level sensor is mainly applied to oil/water level monitoring and the like in the main propulsion power generator; lubricating oil circulating oil tank, generator bearings and the like in the power generation power device; detecting liquid levels of equipment such as water level of a water tank in the boiler and accessory equipment, controlling the water level and the like, and converting monitored liquid level information into electric signals for transmission;

the voltage and current sensor is mainly applied to a power generator in a power generation power device to output a three-phase power supply, excite voltage and current, monitor the insulation of the power generator and the like; the steering engine device and the accessory equipment are connected with an external power supply of the steering engine, a steering motor load and the like; detecting voltage and current of external power supply and other equipment in the boiler and accessory equipment, and converting the monitored voltage and current values into electric signals for transmission;

the oil mist concentration monitoring sensor is mainly applied to oil mist concentration detection in a crankcase device in a main propulsion power device, and converts a monitored concentration value into an electric signal to be transmitted and the like.

The intelligent ship engine room system has the advantages that the types of the sensors in the intelligent ship engine room system are more, the detected data are quite rich, the working state and the running state of the engine room equipment are learned and analyzed subsequently based on the data detected and detected by the sensors, the possible faults or failures of the engine room equipment are predicted and diagnosed, the fault reasons and the corresponding measures and alternative schemes are output in time, the service life of the mechanical equipment in the engine room is prolonged, and the maintenance cost is reduced.

Specifically, as shown in fig. 2, fig. 2 is a system architecture diagram of an intelligent ship cabin system of the invention, a data acquisition subsystem 1 is a system for acquiring, processing and monitoring sensing data of sensing equipment, the data acquisition subsystem 1 comprises a data module 12 and a sensing module 11, the sensing module 11 acquires the sensing data of the working state and the working environment of the cabin equipment through sensors, the cabin equipment comprises a main propulsion power device, an electric power device, a power generation power device, a boiler device and a steering engine device, the data module 12 comprises a data acquisition unit and a data processing unit, the data acquisition unit inputs the data transmitted by the sensing module 11 to interfaces in the system by using the data device, the sensing module 11 acquires more types of data, and the data acquisition unit converts the transmitted data into digital data.

The data processing is to collect and process information sensed by all ship sensor nodes distributed in a ship monitoring area, and the data collection and processing of the sensor nodes adopt a data fusion technology. The compressed sensing can perform low-rate distortion-free sampling on any compressible signal or sparse signal, a convenient way is provided for acquisition, storage, transmission and processing of signals, the processing of data by the compressed sensing is shown in fig. 2, and fig. 2 is a flow chart of encoding and decoding of the compressed sensing signal of the intelligent ship engine room system.

The compressed sensing method has the advantages that the sampling rate does not depend on the bandwidth of the signal, but depends on the structure and content of information in the signal, and the complexity of calculation is reduced.

Let dimension signal x ∈ R^N×1Can be formed from orthogonal base psi ═ psi₁,ψ₂,...,ψ_N]Unfolding, then there are:

wherein α is the inner product ofWhen x is in orthogonal base psi ∈ C^N×NOnly K (K is less than or equal to N) nonzero coefficients α_iWhen so, psi ∈ C^N×NA sparse radical called x.

Projecting x to another observation basis matrices that are not coherent with sparse basis

A linear observation of x is obtained:

compressed sensing reduces the original signal x dimension to M-dimensional observations. In order to enable the K coefficients to be accurately recovered from the M measurements, a matrix

RIP criterion must be satisfied, i.e. matrix for any strictly K sparse vector gamma

The following inequality is guaranteed to hold:

in the formula: ε is any small positive number.

Wherein, the sensor type mainly has pressure sensor, flow sensor, temperature sensor, speed sensor, level sensor, voltage current sensor, oxygen content sensor, carbon dioxide content sensor, fire detector, oil mist concentration monitoring sensor, axial displacement alarm device etc. wherein:

the data acquisition subsystem 1 further comprises a state detection module for performing logic or mathematical operation on the data packet, and comparing the data output by the two modules after the operation is completed with an expected value by adopting a strong object-oriented method to obtain a limit exceeding value of the output value and the expected value and obtain a state index.

, the health evaluation module 21 is used for analyzing and evaluating the operation state and health condition of the cabin equipment according to the standard data to form an analysis and evaluation report, the health evaluation module 21 comprises a diagnosis sub-module and a prediction sub-module, wherein the diagnosis sub-module realizes fault failure detection, identification, positioning and early isolation, and the diagnosis information comprises:

(1) machines and their components that may fail/fail, as well as failure mode failure modes;

(2) latent observable symptoms of failure;

(3) a relevant condition monitoring parameter;

(4) diagnostic methods, basis and interpretation.

The conditions that the diagnostic method of the diagnostic submodule should fulfil are:

(1) the capability of detecting the system performance and the degradation level is provided;

(2) detecting a mechanism of occurrence of a failure based on a change in physical characteristics by a measurable phenomenon;

(3) the ability to identify a particular system or component and its failure/failure mechanism;

(4) a diagnostic result is given of the potential impact of the fault/failure on the operational integrity of the system.

The performance indicators diagnosed by the diagnostic submodule include: timeliness, sensitivity, false alarm rate, false alarm failure rate, fault/failure isolation capability, fault/failure identification capability, robustness, adaptability, and the like.

The prediction submodule is a module that predicts future conditions and trends of the mechanical failure mode. The prediction information includes:

(1) in the prediction process, the operation condition, monitoring parameters and the like of the monitored machine are monitored;

(2) predicting a conclusion, including all identified failure modes;

(3) confidence, effective condition and risk analysis;

(4) additional trial/validation work required to improve confidence;

(5) prediction method, basis and interpretation.

The basis for the prediction by the prediction sub-module comprises: database output data and historical data, output data and historical data of health assessment module 21, expert knowledge, maintenance records, control commands, relevant configuration parameters, and the like. The information output by the prediction sub-module after the prediction is completed includes: performance evaluation, interpretation, relevant configuration parameters, historical data to be saved, and the like. And the output data of the diagnosis submodule and the prediction submodule form an analysis and evaluation report.

The health evaluation module 21 mainly aims at detecting the operation condition of the equipment of the marine engine room, wherein for the marine main engine, typical main engine faults comprise cylinder pulling, abnormal air clearance, air valve leakage, abrasion of a connecting rod big end bearing, a connecting rod small end bearing, a main bearing and the like, mutual coupling easily occurs among the faults, and the accurate fault position is difficult to judge.

The method for monitoring the state of the marine main engine comprises an in-cylinder pressure method, a vibration analysis method and an instantaneous rotating speed method, wherein the in-cylinder pressure method is characterized in that a sensor is installed in a cylinder in a complex mode and is difficult to operate actually, the vibration analysis method is characterized in that a main engine structure is complex and vibration sources are more, and a fault vibration source is difficult to extract accurately, the instantaneous rotating speed method has the advantages of simplicity and convenience in installation, low signal complexity and easiness in analysis, and can be used for effectively analyzing the state of the intelligent marine main engine, the instantaneous rotating speed fluctuation reflects the working state of each cylinder of the main engine, under the normal condition, the dynamic performance of each cylinder of the main engine is basically , although the instantaneous rotating speed fluctuation of each cylinder is different, certain regularity in ranges is always presented, and the instantaneous rotating speed fluctuation coefficient (:

in the formula:

is the crankshaft instantaneous speed, ω is the average angular velocity.

The instantaneous rotating speed of the main machine is mainly influenced by the work fluctuation of the cylinder on the crankshaft, the work of the cylinder is directly related to factors such as air inlet pressure, friction of a piston of the cylinder, external load and the like, and the balance equation of the motion of the main machine is expressed as follows:

in the formula: f is crankshaft moment of inertia, Q_XFor the work-producing output of diesel engines, Q_LIs the load average angular velocity.

Thus, the formula for the host machine instantaneous speed ripple factor can be expressed as:

ω/F＝φ₁(θ)＝cons tan t

therefore, step is performed to obtain the calculation formula of the instant speed of the main engine:

in the formula: r is the crankshaft radius, lambda is the ratio of the crankshaft radius to the connecting rod length, L is the connecting rod length, m is the sum of the masses of the piston, the connecting rod, etc.,

is the initial phase.

According to the above formula, the calculation results

When the marine main engine works normally, the fluctuation of the instantaneous rotating speed of the main engine is small, and when fails, the instantaneous rotating speed of the marine main engine can obviously react.

The aid decision module 22 is a system for outputting hazard sources and their influences and hazard reasons, recommended measures and alternatives (sorted according to recommended grades), and giving optimization suggestions for mechanical operation, maintenance and the like according to information such as recommended measures and explanations or for optimizing the running state of the ship mechanical equipment according to alarm/early warning information, current failure/failure information and prediction information of the ship mechanical equipment. The aid decision module 22 has the characteristics of simple execution, strong adaptability, and quick and friendly user interface.

The recommended maintenance schedule for the aid decision module 22 should be based on the risk level of the mechanical equipment or components, the operating cost, the maintenance cost, the availability of spare parts, and other factors, and should satisfy the following conditions:

(1) self-learning/self-improvement functions;

(2) a human-computer interaction function;

(3) the knowledge and important problems required by the problem solution are stored and are fully complete;

(4) sufficient database/data warehouse capacity;

(5) maintenance means such as knowledge acquisition, machine learning, modification, expansion and perfection of a knowledge base are provided;

(6) according to the situation information such as time sequence automatic recording, backup decision suggestion adoption, execution, error correction and manual supplement (if at all), and the like, the ship end system operator can assist in completing the work when necessary.

In addition, the aid decision module 22 includes a model library unit and an expert system.

The model library unit comprises a model library and other quantitative models which can provide decision analysis capability, and a model library management system provides modeling language and functions and model library management functions for users.

The expert system comprises a knowledge acquisition subunit, a knowledge base and an inference machine.

The knowledge acquisition subunit extracts knowledge from the knowledge source for solving problems in the domain of expert and converts the knowledge into a computer program to build or extend a knowledge base;

the knowledge base includes facts (such as problem cases and theories thereof in the relevant problem area), heuristic knowledge or rules of patent ;

when the fault/failure of the diagnosis object occurs, the inference machine calls corresponding knowledge in the knowledge base by adopting a certain strategy to detect the diagnosis object, and carries out analysis and isolation according to the symptom data until a fault/failure source is positioned.

The intelligent ship engine room system is composed of a plurality of subsystems, the number of components is very large, and the structure of the system is relatively complex, so that the knowledge base of the expert system is divided into a dictionary base, a rule base, a case base and a comprehensive database. The expert system adopts an organization mode of a plurality of subsystems, so that the query speed of the knowledge base can be improved.

The inference mechanism of the expert system has different classification modes according to the reasoning way, the certainty of reasoning knowledge, heuristic knowledge whether the reasoning is applied to the problem, and the like, wherein the example-based reasoning mode is similar to the mode of solving the problem by human beings, and when new problems are encountered, answers of similar problems are always searched from the past experience.

The expert system combines the standard data, the analysis and evaluation report, the maintenance scheme and the schedule into fault cases, and stores the fault cases in the external subsystem 3, wherein fault cases can be described by quintuple:

case＝<T,E,M,D,P>

wherein: t ═ T₁,t₂,...t_nIs a finite non-empty set, representing the case number;

E＝{e₁,e₂,...e_nthe description information of the fault case is represented by a limited set;

M＝{m₁,m₂,...m_nis a finite, non-empty set, representing various symptoms of a fault instance;

D＝{d₁,d₂,...d_na finite non-empty set, representing fault conclusions caused by various fault symptoms;

P＝{p₁,p₂,...p_nthe is a finite set, representing a maintenance solution for the fault case.

The similarity calculation between the cases is generally to establish similarity calculation functions to compare a target case with a source case, and a nearest neighbor algorithm is adopted in an expert system of an intelligent ship cabin to calculate the similarity of the cases, the cases needing to be solved are compared with the similar cases in the expert system by , the similarity of each characteristic attribute between the cases is calculated, the similarity between all the cases and the cases needing to be solved is finally determined according to the weighting vector, the cases with inconsistent similarity are finally fed back to the expert system, and the intelligent ship shore-based center analyzes and processes the accidents according to the fed-back information and gives a decision scheme.

The expert system carries out similarity calculation on the fault information and the fault case, and the calculation formula of the similarity is expressed as follows:

in the formula, sim_i(X_i,Y_i) Finger fault information and ith faultThe similarity of the instances; mu.s_iThe weight of the fault occurrence part characteristic;

the determination method regarding the attribute weight is determined using a relative comparison method.

The basic idea of the relative comparison method is that all the characteristic attributes are respectively arranged into square tables according to rows and columns, the characteristic attributes are compared pairwise according to three-level comparison standards to give comparison values, then the comparison values are summed according to rows to obtain the sum of the comparison values of the characteristic attributes, and finally the weight coefficient of each characteristic attribute is obtained through the treatment of returning .

Wherein the meaning of the three-level comparison standard is:

when r is_iRatio r_jWhen it is important, p_ij＝1；

When r is_iAnd r_jWhen of equal importance, p_ij＝0.5；

When r is_iRatio r_jWhen not essential, p_ij＝0。

Let r₁,r₂,...r_nFor n indices, the comparison value according to the three-level comparison standard is p_ijThe comparison values form a matrix N:

N＝(p_ij)_n*n

the comparison value can be more intuitively analyzed by adopting a matrix mode.

Characteristic attribute r_iThe weight coefficients of (a) are:

because the intelligent ship engine room system comprises a plurality of subsystems, and each subsystem is divided into a plurality of modules, the weight of each characteristic attribute needs to be calculated in a complicated way, and in order to improve the calculation efficiency of the similarity, the weight of each characteristic attribute is simplified to 1, and then:

in the formula, C_xThe number of fault phenomena as fault information; c_yNumber of fault phenomena for a fault instance; c_x∩C_yThe number of fault phenomena matching the fault information with the fault case.

The intelligent marine engine room system is complex systems, and the understanding of the failure mechanism in the engine room is difficult things, so the intelligent marine engine room system has high requirements on the integrity and the accuracy of an expert system, and by perfecting the failure diagnosis method in the expert system, a reasonable diagnosis method can be adopted for different failure types in the engine room, so the reliability of the diagnosis result is increased, a reliable basis is provided for the intelligent engine room auxiliary decision module 22, and the accuracy of the decision scheme is ensured.

The visual maintenance module 23 is used for predicting the effective working period of the cabin equipment according to the analysis evaluation report and the maintenance scheme and making a maintenance scheduling schedule; the episode maintenance module 23 is also used to manage the storage and access of the schedule and the configuration of the episode maintenance module 23.

The main functions of the episode maintenance module 23 are:

(1) predicting future health profiles and failure modes, etc.;

(2) maintenance schedules or evaluations of equipment availability in a particular operating environment;

(3) history data storage and access management;

(4) managing system configuration;

(5) performing human-computer interaction;

(6) safe and reliable bidirectional data exchange between ship shore ends;

(7) according to the situation information of time sequence automatic recording, backup maintenance scheme adoption, execution, error correction and manual supplement (if at all), and the like, when necessary, the ship end system operator can assist in completing the work.

The system requirements of the episodic maintenance module 23 are:

(1) the maintainability and the safety are ensured to be improved, and the use and guarantee cost of a key system/process is reduced in the whole life cycle;

(2) the structure should be designed to be open, and when the external subsystem 3 or the subsystem and the component are changed, upgraded and replaced, the use should be convenient, and the interface change of the system/process is as less as possible;

(3) reliability, availability, maintainability and durability requirements have to be met;

(4) the knowledge base is mainly from design (initial data and information and its components) and operation (including operational data records, maintenance history, material consumption history);

(5) the independence requirement is that the modules and functions are designed or made independent from each other, and when one or a plurality of modules have faults, the normal work of other modules is not influenced;

(6) the design of the module is such that faults occurring during operation do not cause other faults and the danger thereof is reduced to the lowest possible extent, and the module ensures continuous, efficient and reliable operation, updating and maintenance.

(7) And (3) redundancy design: the double-set power supply is adopted, wherein when the main power supply fails, the standby power supply can immediately bear all loads; when a module fails, the redundantly configured components intervene and assume the work of the failed component, thereby reducing the failure time.

Preferably, the intelligent ship cabin system further comprises an execution subsystem 4, and the execution subsystem 4 is a system for autonomously making and executing a cabin management and action scheme according to the output results of the modules such as health assessment, assistant decision, visual maintenance and the like.

In the process, the potential conflict between the action scheme and other subsystems (such as an intelligent navigation system, an intelligent energy efficiency management system and the like) can be automatically identified, the action scheme with the potential conflict is uploaded to the ship intelligent comprehensive control and command central system in time, and finally a coordination control instruction sent by the central system is executed. The system comprehensively utilizes various information and data obtained by the state monitoring system to analyze, evaluate, decide and predict the running state and health condition of equipment such as an internal machine, electricity, gas, liquid, pipes and the like in an engine room, and has the functions of automatic early warning, alarming, maintenance, control and the like on corresponding equipment.

The external subsystem 3 comprises a database module 31 and an information interaction module 32, the database module 31 is used for storing and transmitting data generated by the data acquisition subsystem 1 and the auxiliary subsystem 2, and the information interaction module 32 is used for realizing connection of a ship end, a bank end and a mobile terminal in an information interaction mode.

The functional requirements of the database module 31 are mainly:

(1) setting a server or a database with enough capacity to realize the functions of storing, backing up, managing and replaying data, networks and systems;

(2) storing data for at least inspection periods (the ship end stores for at least 5 years, and a data backup mechanism is established);

(3) historical operating data can be retrieved and called by other functional modules such as health assessment and the like at any time;

(4) historical data trends may be used for statistical correlation analysis, and for accuracy, previous health status assessments and root cause information should be reviewed and verified;

(5) the system has a dual-redundancy hot backup function, and can be used for realizing function recombination when any display console (or network) fails, ensuring that the external subsystem 3 runs uninterruptedly and completing all functions of the original external subsystem 3;

(6) and the simulation drilling function simulates and generates sensor and equipment information necessary for system drilling and monitoring working state by means of the database server, simulates and generates a ship navigation working flow, and performs simulation drilling such as ship navigation, intelligent engine room management control and the like under specific marine environment conditions.

The information interaction module 32 is that an operator learns the overall mechanical picture and each subsystem through an interaction system interface by means of equipment such as a ship end, a bank end, a mobile terminal and the like, can also set an alarm value of a mechanical state steering engine, and simultaneously outputs different statistical information including alarm information, measurement information, health evaluation results and the like, and the synchronization of the ship-bank integrated information is to establish an independent local area network on each ship, connect the local area network with a communication gateway unit on the ship, transmit the information in a mail mode by means of ship satellite communication equipment, verify and intelligently split the data by an application server, and automatically store the information in a ship database.

The information interaction module 32 mainly has the following functions:

(1) the interaction module has a user-friendly man-machine interface and is convenient to operate; all mechanical overall pictures and all sub-systems or component sub-pictures can be displayed; and the mechanical state multi-level alarm value can be set, and different statistical information including alarm information, measurement information, signal abnormal information, health evaluation results and the like can be output simultaneously.

(2) The output data and information at least comprises:

a. identification code or number: describing the identification of the detected machine through history records such as equipment number, part number, evaluation date and the like;

b. and (3) state monitoring: displaying specific state information, trend data and the like of the detected machine;

c. health evaluation: and displaying the diagnosis conclusion of the current or potential fault/failure of the detected machine and failure prediction information.

(3) The information interaction module 32 should be able to convert the data into a format that clearly expresses the information necessary to make a proper decision, such as a textual description, a numerical representation of the duplication, an iconic representation of the trend, or a combination of the three;

(4) to facilitate analysis by qualified personnel, a related technical display is required to display data such as trends and related abnormal areas, providing data to the analyst that requires identification, confirmation or understanding of the abnormal condition;

(5) in order to allow the user to quickly understand the state, for most users, the display can be divided into five different areas, and the following multiple windows can display more detailed additional data, wherein the five different areas are used for state monitoring, health evaluation, prediction, measure recommendation and identification, and if the situation is special, a customized display format can be developed and customized for a specific application;

(6) different authorities are set for operators with different requirements, misoperation of personnel is prevented, and safety of the system is guaranteed.

In a preferred embodiment, the information interaction module 32 includes five windows for status detection, health assessment, prediction, recommendation and identification, and by dividing information of different areas, a user can conveniently and quickly know the operating status of the device, and at the same time, additional information of the device can be displayed, or by adding an information window.

The data storage in the external subsystem 3 adopts an SQL Server database; the data acquisition subsystem 1 acquires data through a NETBILS adapter; the external subsystem 3 collects data through a TCP/IP adapter; the health evaluation module 21 acquires data through a PROFINET interface; the auxiliary decision module 22 collects data through a PROFINET interface; the video maintenance module 23 collects data through a PROFINET interface; the execution subsystem 4 collects data through a NETBILS adapter; the data system acquires data through the FTP adapter; the information perception system acquires the collected data through the FTP adapter.

The specific protocol content is as follows:

(1) NETBILS protocol

NETBILS (Network Basic Input/Output System) Network Basic Input/Output System is application program interfaces, can be connected with each other and share data in a local area Network, defines software interfaces and provides a standard method of a communication interface between an application program and a connection medium, and is session layer protocols, and the transmission speed of NETBIL is 60 MB/s.

(2) TCP/IP protocol

TCP/IP (Internet protocol) is network communication models, and entire network transmission protocol family, which is the basic communication architecture of the Internet, is also called TCP/IP protocol stack because of the common layered structure adopted in the network communication protocol, when the protocols of multiple layers work together, the stack in computer science is similar, TCP/IP provides a point-to-point link mechanism, and the TCP/IP standardizes how data should be packaged, addressed, transmitted, routed and received at the destination, and the transmission speed of the TCP/IP can reach 100MB/s in a local area network.

(3) FTP protocol

FTP (File Transfer protocol) file Transfer protocol for the bi-directional Transfer of control files on Tnternet, FTP being a client server protocol of 8 bits capable of handling any type of file without the need for steps the difference between file Transfer (file Transfer) and file access (file access) is that the former is provided by FTP and the latter by NFS applications, and FTP is transferred in two ways, ASCII, binary, additionally at a Transfer rate of 100MB/s for FTP and at a Transfer rate of 50-60MB/s for gigabit FTP, which is very common.

(4) PROFINET protocol

PROFINET was introduced by the ROFIBUS international organization and is a new generation automation bus standard based on industrial ethernet technology, PROFINET provides complete network solutions for the field of automation communication, which cover the hot topics of current automation fields such as real-time ethernet, motion control, distributed automation, fail-safe and network safety, and PROFINET is fully compatible with industrial ethernet and existing field bus technologies.

(5) SQL Server database

According to ANSI, SQL (Structured Query Language) is used as a standard Language of a relational database management system, SQL Server is a relational database management system developed and developed by Microsoft , the transmission speed of SQL Server mainly depends on the configuration of a computer and the network bandwidth, the fastest speed is the speed of copying files, and the slowest speed is 0.

The design of the intelligent ship cabin system architecture adopts a distributed architecture mode, the intelligent ship cabin system is divided into an external subsystem 3, an auxiliary subsystem 2, an execution subsystem 4 and a data acquisition subsystem 1, a communication interface protocol is used, the coupling degree between the systems is reduced, different software is responsible for different subsystems, when the system function is increased, only new subsystems need to be added, and the interfaces of other systems are called, so that the intelligent ship cabin system architecture has more flexibility in distributed deployment.

Claims (10)

1. The kinds of intelligent ship cabin systems are characterized in that the intelligent ship cabin systems comprise data acquisition subsystems, auxiliary subsystems and external subsystems;

   the data acquisition subsystem is used for acquiring sensing data of the cabin equipment in real time through a plurality of sensors, and converting and processing the sensing data to form standard data;

   the auxiliary subsystem comprises a health evaluation module, an auxiliary decision module and a visual condition maintenance module;

   the health evaluation module is used for analyzing and evaluating the operation state and health condition of the cabin equipment according to the standard data to form an analysis and evaluation report;

   the auxiliary decision-making module is used for diagnosing the faults of the cabin equipment according to the analysis and evaluation report and the standard data, forming fault information, comparing the fault information with historical data stored in the external subsystem to form a comparison result, wherein the historical data comprises the standard data, the analysis and evaluation report, the maintenance scheme and the historical data of the time table, and making a maintenance scheme according to the comparison result;

   the visual condition maintenance module is used for predicting the effective working period of the cabin equipment according to the analysis and evaluation report and the maintenance scheme and formulating a maintenance scheduling schedule;

   the external subsystem is used for storing data generated by the data acquisition subsystem and the auxiliary subsystem and the historical data;

   the comparing the fault information with historical data stored in the external subsystem to form a comparison result comprises:

   combining the standard data, the analysis evaluation report, the maintenance schedule, and the schedule into a failure case, and storing the failure case in the external subsystem; the five tuple of the failure case is represented as:

   case＝<T,E,M,D,P>

   in the formula: t ═ T₁,t₂,...t_nIs a finite, non-empty set, representing the number of the failure instance; e ═ E₁,e₂,...e_nIs a finite set, representing descriptive information of the fault instance; m ═ M₁,m₂,...m_nIs a limited non-empty set, representing a symptom of the fault instance; d ═ D₁,d₂,...d_nIs a finite non-empty set, representing a fault conclusion caused by a fault symptom; p ═ P₁,p₂,...p_nIs a finite set, representing the maintenance scenario for a fault case;

   the auxiliary decision module carries out similarity calculation on the fault information and the fault case, and a calculation formula of the similarity is represented as:

   

   in the formula, sim_i(X_i,Y_i) Similarity between the fault information and the ith fault case is obtained; mu.s_iWeight coefficient of characteristic attribute of fault occurrence part, mu_iExpressed as:

   

   in the formula, P_ijIs the value of the three-level comparison standard;

   when mu is to be measured_iTo simplify to 1, the calculation formula of the similarity is expressed as:

   

   in the formula, C_xThe number of fault phenomena is the fault information; c_yThe number of fault phenomena for the fault instance; c_x∩C_yThe number of fault phenomena matched with the fault case is taken as the fault information;

   the comparison result is a similarity value, when the similarity value falls within a set range, the similarity value indicates that the fault information is similar to the fault case, and the auxiliary decision module makes the maintenance scheme which is the same as the fault case; otherwise, the assistant decision module acquires the maintenance scheme from the external subsystem.
2. 2. The intelligent marine engine room system of claim 1, wherein the health assessment module analyzes the health condition of the marine main engine by using an instantaneous rotation speed method, and the fluctuation coefficient of the instantaneous rotation speed of the marine main engine is expressed as:

   

   in the formula:

   

   is the crankshaft instantaneous speed, omega is the average angular velocity;

   the equilibrium equation for the motion of the marine vessel's main engine is expressed as:

   

   in the formula: f is crankshaft moment of inertia, Q_XFor the work-producing output of diesel engines, Q_LIs the load average angular velocity;

   thus, the equilibrium equation for the motion of the marine vessel's main engine is expressed as:

   ω/F＝φ₁(θ)＝cons tant

   the instantaneous rotational speed of the marine main engine is expressed as:

   

   in the formula: r is a crankshaft halfDiameter, λ is the ratio of the crankshaft radius to the connecting rod length, L is the connecting rod length, m is the sum of the masses of the piston and connecting rod, etc.,

   

   is the initial phase;

   thereby obtaining an expression of the instantaneous rotating speed fluctuation coefficient of the ship main engine:

   
3. 3. a smart marine engine room system as set forth in claim 1, wherein the meaning of the three-level comparison criterion is expressed as:

   the auxiliary decision module calculates the characteristic attribute values of the fault case and the fault information and calculates a comparison value according to the characteristic attribute values; when r is_iGreater than r_jWhen is, p_ij1 is ═ 1; when r is_iIs equal to r_jWhen is, p_ij0.5; when r is_iLess than r_jWhen is, p_ij0; wherein r is_iFor the ith characteristic attribute value, r, of the fault case_jIs the jth characteristic attribute value, p, of the fault information_ijIs a comparison value of a three-level comparison standard;

   the comparison value formation matrix N is represented as:

   N＝(p_ij)_n*n

   in the formula, n is the index number.
4. 4. The smart marine engine room system of claim 1, wherein the auxiliary decision module is further configured to formulate an optimization plan for operation and maintenance of the engine room equipment based on the analysis evaluation report and the standard data.
5. 5. The smart marine engine room system of claim 1, wherein the data acquisition subsystem transmits data using a compressive sensing method, the compressive sensing method comprising:

   carrying out sparse transformation on the standard data to obtain sparse data;

   compressing the sparse data to remove zero-valued or nearly zero-valued coefficients to form transformed data;

   sampling the transformation data at a low speed to obtain sampling data;

   encoding and transmitting the sampling data;

   and reconstructing the transmitted sampling data.
6. 6. The smart marine engine room system of claim 1, wherein the situational maintenance module is further configured to store and access the schedule and to manage configuration of the situational maintenance module.
7. 7. The smart marine engine room system of claim 1, wherein the auxiliary decision module comprises a model library unit and an expert system;

   the model library unit is used for storing quantitative models providing decision analysis capability;

   the expert system comprises a knowledge acquisition subunit, a knowledge base and an inference machine;

   the knowledge acquisition subunit is used for acquiring knowledge of the ship field from a knowledge source and converting the knowledge into a computer program;

   the knowledge base is used for storing facts, heuristic knowledge of special or rules;

   the inference machine is used for calling corresponding knowledge in the knowledge base, detecting the diagnosis object and analyzing and isolating the fault source according to the fault information.
8. 8. The smart marine engine room system of claim 1, wherein the data acquisition subsystem comprises a data module including a data acquisition unit and a data processing unit;

   the data acquisition unit is used for converting the perception data into digital data;

   the data processing unit is used for performing operations including signal processing, synchronous or asynchronous averaging, algorithm calculation and feature extraction on the digital data and obtaining the result data.
9. 9. The intelligent marine engine room system of claim 8, wherein the data acquisition subsystem further comprises a sensing module, the sensing module acquires the sensed data of the working state and the operating environment of the engine room equipment through a sensor, and the engine room equipment comprises a main propulsion power device, an electric power device, a power generation power device, a boiler device and a steering engine device.
10. 10. The intelligent marine engine room system of claim 1, wherein the external subsystem comprises a database module and an information interaction module, the database module is used for storing and transmitting data generated by the data acquisition subsystem and the auxiliary subsystem, and the information interaction module is used for realizing connection of a ship end, a shore end and a mobile terminal in an information interaction mode.

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