CN113375952B - Stabilizer device fault diagnosis and health forecasting system - Google Patents
Stabilizer device fault diagnosis and health forecasting system Download PDFInfo
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- 230000036541 health Effects 0.000 title claims abstract description 75
- 239000003381 stabilizer Substances 0.000 title claims abstract description 65
- 238000003745 diagnosis Methods 0.000 title claims abstract description 53
- 238000004458 analytical method Methods 0.000 claims abstract description 36
- 238000012423 maintenance Methods 0.000 claims abstract description 13
- 238000007405 data analysis Methods 0.000 claims abstract description 5
- 230000003993 interaction Effects 0.000 claims description 13
- 238000012544 monitoring process Methods 0.000 claims description 13
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 13
- 230000007246 mechanism Effects 0.000 claims description 12
- 239000007788 liquid Substances 0.000 claims description 10
- 238000005096 rolling process Methods 0.000 claims description 7
- 238000001514 detection method Methods 0.000 claims description 6
- 239000012530 fluid Substances 0.000 claims description 6
- 238000004364 calculation method Methods 0.000 claims description 5
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- 238000013528 artificial neural network Methods 0.000 claims description 3
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- 238000010586 diagram Methods 0.000 description 7
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- 238000011160 research Methods 0.000 description 3
- 206010063385 Intellectualisation Diseases 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 238000007689 inspection Methods 0.000 description 2
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- 230000008439 repair process Effects 0.000 description 2
- 238000007789 sealing Methods 0.000 description 2
- 208000024891 symptom Diseases 0.000 description 2
- 238000012546 transfer Methods 0.000 description 2
- HCHKCACWOHOZIP-UHFFFAOYSA-N Zinc Chemical compound [Zn] HCHKCACWOHOZIP-UHFFFAOYSA-N 0.000 description 1
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
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- G01M99/008—Subject matter not provided for in other groups of this subclass by doing functionality tests
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
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Abstract
The invention relates to a stabilizer device fault diagnosis and health prediction system, which comprises a fault diagnosis and health prediction analysis and prediction system, a stabilizer main control system, an execution element and a hydraulic unit field sensing acquisition element, wherein the stabilizer main control system is connected with the execution element; the fault diagnosis and health forecast analysis and prediction system directly collects the message information and high-frequency data of the gyroscope, and the fin stabilizer main control system collects the state information of the executive component and the on-site sensor signals of the hydraulic unit to convert the state information and the on-site sensor signals into text data to be uploaded to the fault diagnosis and health forecast analysis and prediction system; the fault diagnosis and health forecast analysis and prediction system monitors the state information of the executing element, the hydraulic unit and the electric control module in real time, and visually presents the health state of the stabilizer through a human-computer interface. The intelligent control system can realize fault diagnosis, health prediction, maintenance prompt and data analysis on the fin stabilizer machine, the electric and hydraulic system, improves the automation and intelligent level of the fin stabilizer, and further improves the navigation safety.
Description
Technical Field
The invention relates to an electromechanical equipment management technology, in particular to a fin stabilizer fault diagnosis and health forecasting system.
Background
The research of the fault diagnosis and health management technology of the fin stabilizer mainly takes a hydraulic driving fin stabilizer as a research object, and through analyzing and researching the working principle and characteristics of the fin stabilizer, the fault mechanism of the fin stabilizer and the health state of main elements are predicted and an optimal maintenance decision is given from each layer of perception, analysis, decision and the like, and the fault diagnosis mechanism and health prediction function of the fin stabilizer are realized through analyzing the data acquired by the fin stabilizer; the reliability of the fin stabilizer is improved, unnecessary excessive repair and maintenance are reduced, and the use and maintenance cost of the fin stabilizer is reduced.
For the fault diagnosis and health forecasting technology of the fin stabilizer, the technology maturity of companies such as MANT, CMC, SIDE-POWER, pinfabb abroad is higher in the aspects of intelligent control, fault diagnosis, health forecasting and energy consumption management technology, and the intelligent technology such as distance intellectualization, fault diagnosis and health forecasting is mostly theoretical research, so that the fin stabilizer is lack of on-line/off-line fault diagnosis and health forecasting engineering application.
Disclosure of Invention
Aiming at the intelligent management problem of the stabilizer, the fault diagnosis and health prediction system of the stabilizer is provided, fault diagnosis, health prediction, maintenance prompt and data analysis can be carried out on a stabilizer machine, an electric system and a liquid system, unnecessary waste of manpower, material resources and financial resources caused by original planned maintenance and accident maintenance is avoided, mean Time Between Failures (MTBF) of the stabilizer is improved, mean time between repair (MTTR) is reduced, and automation and intellectualization level of the stabilizer is improved.
The technical scheme of the invention is as follows: a stabilizer device fault diagnosis and health prediction system comprises a fault diagnosis and health prediction analysis and prediction system, a stabilizer main control system, an execution element and a hydraulic unit field sensing acquisition element; the fault diagnosis and health forecast analysis and prediction system directly collects the message information and high-frequency data of the gyroscope, and the fin stabilizer main control system collects the state information of the executive component and the on-site sensor signals of the hydraulic unit to convert the state information and the on-site sensor signals into text data to be uploaded to the fault diagnosis and health forecast analysis and prediction system; the fault diagnosis and health forecast analysis and prediction system monitors the state information of the executing element, the hydraulic unit and the electric control module in real time, and visually presents the health state of the stabilizer through a human-computer interface.
Preferably, the fault diagnosis and health forecast analysis and prediction system comprises a PXIe main controller, a PXIe dynamic acquisition module, a PXIe analog acquisition module and a display; the fin stabilizer master control system comprises a control master station and a control slave station; the executing element comprises a proportional valve, a reversing valve and an executing mechanism; the hydraulic unit on-site sensing acquisition element comprises a vibration sensor, a flow sensor, a pressure sensor, a temperature sensor, a liquid level sensor, an oil water sensor, a water seepage detection rope and a triaxial gyroscope;
the vibration sensor is an acceleration sensor and is connected with the PXIe dynamic acquisition module through a BNC interface, the system pressure, the fin rotating pressure, the accumulator pipeline flow, the main pump overflow port flow and the main pump outlet flow sensor are all connected with the PXIe analog acquisition module through a transfer box by using 4-20 mA current signals, the triaxial gyroscope is connected with the PXIe main controller self-contained serial port through an RS232 interface, the temperature sensor and the liquid level sensor are all connected with the PXIe main controller self-contained serial port by using 4-20 mA current signals, the oil water content sensor is connected with the control slave station communication interface by using a ModbusRTU interface, the seepage detection rope is connected with the control slave station firstly, the control slave station interface module is connected with the control master station through a network cable, the anti-rolling main control master station CPU is connected with the fault diagnosis and health analysis prediction system Ie main controller through the network cable, the PXID is controlled to convert the data acquired by the control slave station into the engineering quantity and send the engineering quantity into the PXIe prediction system.
Preferably, the fault diagnosis and health forecast analysis and prediction system comprises a database for providing data support for monitoring the relevant real-time state of the stabilizer man-machine interaction interface, diagnosing faults, analyzing health forecast and reminding maintenance;
receiving data transmitted by a control master station through ModbusTCP and directly storing the data into a database;
reading the message information of the gyroscope from a serial port of the PXIe main controller, analyzing the real-time value through data analysis, analyzing the rolling, pitching and heaving amplitude of the ship, and storing the real-time value into a database;
the main pump vibration signals acquired by the PXIe dynamic acquisition module are configured on a human-computer interaction interface, the acquired signals are directly stored in a specified file directory, FFT time-frequency transformation analysis is carried out on the signals, the energy values of specified frequency bands are extracted and are used as characteristic values to be stored in a database, and the average amplitude value, square root amplitude value, peak value, mean square amplitude value, waveform index, peak value index, pulse index, margin index and skewness index of the amplitude value domain are calculated and are used as characteristic values of the amplitude value domain to be stored in the database.
Preferably, the fault diagnosis and health forecast analysis and prediction system comprises a state analysis module, wherein vibration signals, flow signals and pressure signals in a database are extracted and subjected to filtering treatment, the vibration signals of power spectrum density are reconstructed by data decomposition according to different frequency bands, and the characteristic values of main pump outlet pressure, flow and temperature are combined with the typical fault characteristics and theoretical volumetric efficiency of a hydraulic pump to judge the fault type, failure mode and health state of the hydraulic pump in real time through a fuzzy neural network.
Preferably, the state analysis module calculates a real-time differential pressure signal according to a fin rotation angular velocity signal and a fin rotation oil cylinder pressure signal in a database, and combines the fin rotation angular velocity signal to calculate a resistance lifting force, a resistance moment, a fluid moment and a fluid moment coefficient acting on fin leaves of the stabilizer through iteration, and calculates a bearing stress value acting on a fin shaft of the actuating mechanism according to a stress calculation formula, wherein the life value under the current stress can be checked through a relation curve of the stress amplitude level born by the material and the stress cycle number experienced when fatigue damage occurs under the stress amplitude, and then the residual life of the bearing is obtained according to an accumulated damage theory.
The invention has the beneficial effects that: the fin stabilizer fault diagnosis and health prediction system solves the problems that the existing fin stabilizer only has liquid level, pressure and temperature switching value data, system data such as loop pressure, flow, oil water and the like are not easy to acquire, a control system acquisition module is not monitored, mechanical equipment data and the like cannot be acquired, improves the automation and intelligence level of the fin stabilizer, and further improves navigation safety.
Drawings
FIG. 1 is a block diagram of a fin stabilizer fault diagnosis and health prediction system of the present invention;
FIG. 2 is a software architecture diagram of a fin stabilizer fault diagnosis and health prediction system according to the present invention;
FIG. 3 is a schematic illustration of a human-computer interaction main interface of the fin stabilizer fault diagnosis and health prediction system of the present invention;
FIG. 4 is a diagram showing a monitoring interface of a human-computer interaction control system of the fin stabilizer fault diagnosis and health prediction system according to the present invention;
FIG. 5 is a schematic illustration of a human-computer interaction hydraulic system monitoring interface for a fin stabilizer fault diagnosis and health prediction system according to the present invention;
FIG. 6 is a schematic illustration of a human-machine interaction mechanical system monitoring interface for a fin stabilizer fault diagnosis and health prediction system according to the present invention.
Detailed Description
The invention will now be described in detail with reference to the drawings and specific examples. The present embodiment is implemented on the premise of the technical scheme of the present invention, and a detailed implementation manner and a specific operation process are given, but the protection scope of the present invention is not limited to the following examples.
As shown in fig. 1, the system comprises a fault diagnosis and health forecast analysis and prediction system, a fin stabilizer main control system, an execution element and a hydraulic unit field sensing acquisition element; the fault diagnosis and health forecast analysis and prediction system comprises a PXIe chassis, a PXIe main controller, a PXIe dynamic acquisition module (PXIe-4492), a PXIe analog acquisition module (PXIe-4300), a display and the like. The fin master control system comprises a control master station, a control slave station, an HMI touch screen and the like. The actuating element comprises a proportional valve, a reversing valve, an actuating mechanism and the like. The field sensing acquisition element comprises a vibration sensor, a flow sensor, a pressure sensor, a temperature sensor, a liquid level sensor, an oil water content sensor, a water seepage detection rope, a triaxial gyroscope and the like.
The vibration sensor is an acceleration sensor and is connected with the PXIe dynamic acquisition module through a BNC interface, the system pressure, the fin rotating pressure, the accumulator pipeline flow, the main pump overflow port flow and the main pump outlet flow sensor are all respectively connected with the PXIe analog acquisition module through a transfer box by using 4-20 mA current signals, the triaxial gyroscope is connected with the PXIe main controller self-contained serial port through an RS232 interface, the temperature sensor and the liquid level sensor are respectively connected with the PXIe main controller self-contained serial port by using 4-20 mA current signals, the oil water content sensor is connected with the control slave station communication interface by using a ModbusRTU interface, the seepage detection rope is connected with the control slave station firstly, the control slave station interface module is connected with the control master station through a network cable, and the anti-rolling main control master station CPU is connected with the fault diagnosis and health forecast analysis prediction system Ie main controller through the network cable. And controlling the A/D conversion of the data collected by the slave station, converting the data into engineering quantity, and sending the engineering quantity to the PXIe master controller in a WORD mode.
The technical problem to be solved by the invention can be further solved by the following technical scheme, the fault diagnosis health forecasting system software mainly comprises a data acquisition module, a data management module, a state analysis module and a man-machine interaction module, and the main functions are that state information of an executing mechanism, a hydraulic unit, an electric control module and the like is monitored in real time, sensor data arranged on equipment are acquired, the acquired data are subjected to feature extraction, after being processed by an algorithm identification system and an equipment health state management system, the health state of the stabilizer is visually presented through a man-machine interface, and the structural block diagram is shown in figure 2.
The data acquisition part comprises:
the control master station and the control slave station form a stabilizer control system, meanwhile, the water seepage detection rope signal and the fin rotation angle signal of the executing mechanism and the parameters such as oil temperature, liquid level height, inlet and outlet pressure of a cooler, motor current, motor voltage and the like of the oil tank of the hydraulic unit are collected, and collected data are transmitted to the data management module through ModbusTCP after being converted in engineering quantity. The gyroscope measures the ship state and contains ship rolling, pitching and heave information, and the ship rolling, pitching and heave information is transmitted to the data management module through the RS232 interface. The PXIe dynamic acquisition module acquires vibration acceleration signals of a main pump X, Y, Z of the hydraulic system at the acquisition frequency of 200kHz, and the PXIe analog module acquires parameters such as hydraulic system pressure, fin rotation pressure, accumulator pipeline flow, total pipeline flow, main pump overflow port flow and the like at the acquisition frequency of 100kHz, and PXIe acquires data with higher frequency.
Data management module:
receiving data of a control system transmitted by ModbusTCP and directly storing the data into a database; reading the message information of the gyroscope from a serial port of the PXIe main controller, analyzing the real-time value through data analysis, analyzing the rolling, pitching and heaving amplitude of the ship, and storing the real-time value into a database; the main pump vibration signals acquired by the PXIe dynamic acquisition module can be configured on a human-computer interaction interface, the acquired original signals (the original signals refer to data acquired by the PXIe dynamic acquisition module and the analog acquisition module and not subjected to characteristic value calculation) are directly stored under a specified file directory, and meanwhile, FFT time-frequency transformation analysis is carried out on the signals to extract a specified frequency band energy value as a characteristic value and store the characteristic value into a database, and the average amplitude value, square root amplitude value, peak value, mean square amplitude value, waveform index, peak value index, pulse index, margin index, skewness index and the like of a amplitude value domain are calculated and stored into the database as the characteristic value of the amplitude value domain; and the data support is provided for the relevant real-time state monitoring, fault diagnosis, health forecast analysis, maintenance reminding and the like of the fin stabilizer man-machine interaction interface.
The state analysis module:
the method comprises the steps of extracting vibration signals, flow signals and pressure signals from a database, performing filtering treatment, decomposing data according to different frequency bands to reconstruct vibration signals of power spectral density, combining characteristic values of main pump outlet pressure, flow, temperature and the like, combining typical fault characteristics and theoretical volumetric efficiency of a hydraulic pump, and judging the fault type, failure mode and health state of the hydraulic pump in real time through a fuzzy neural network.
The control master station in the fin stabilizer master control system is programmed, state information of each acquisition module channel, network and the like of the control slave station is monitored in real time, and if abnormality exists, the state information is sent to the fault diagnosis and health forecasting system in time.
According to a fin rotation angular speed signal (from a first group of data) and a fin rotation oil cylinder pressure signal in a database, a real-time differential pressure signal is calculated through the fin rotation inlet and outlet pressure signal, the lift resistance force, the resistance moment, the fluid moment and the fluid moment coefficients acting on fin blades of the fin are solved through iteration by combining the fin rotation angular speed signal, the stress value acting on a bearing (the fin shaft of an actuating mechanism, not an oil cylinder bearing) is calculated according to a stress calculation formula, the service life value under the current stress can be checked through an S-N curve (the relation curve of the stress amplitude level born by a material and the stress cycle number experienced when fatigue damage occurs under the stress amplitude), and the residual service life of the bearing is obtained according to an accumulated damage theory.
Through the calculation, the fault modes and the health states of main elements in a computer, electricity and liquid are known, and the health states of a hydraulic system, execution equipment and a control system can be judged by combining other signals. And estimating the overall health state of the fin stabilizer by adopting an evaluation method combining a analytic hierarchy process and fuzzy comprehensive evaluation according to the structural hierarchy characteristics and the health fuzzy characteristics of the fin stabilizer.
The maintenance time of the fin stabilizer is judged by combining the running time of the fin stabilizer and the health state of the subsystem elements, and the maintenance time mainly comprises the physical and chemical indexes of oil in an oil tank, the inspection/replacement of a filter element, the inspection/replacement of an anti-corrosion zinc rod, the filling of lubricating grease into mechanical movement parts and the like.
Human-computer interaction module
The man-machine interaction mainly comprises user management, system configuration and data monitoring;
the user management operation can set the authority of an administrator and the operator, and the administrator can be logged in to configure the system and add or delete operators.
The system configuration mainly comprises general configuration, channel configuration, alarm configuration, database configuration and the like.
The data monitoring can call the three-axis gyroscope in the database to display the ship attitude, the real-time fin turning angle information, the current navigational speed information, the alarm information and the health state of the fin stabilizer calculated through the health forecast. The control system can display the current state of the PLC and the proportional valve, the current and voltage change trend of the hydraulic pump motor, the real-time power curve, the real-time monitoring of the voltage of the control loop and the like in real time. The hydraulic system can display the current state of each valve element, the pressure and flow real-time value of each loop, the vibration frequency of the main pump, the volumetric efficiency, the health state and the like. The mechanical system can display the rotating fin angle, the water seepage state of the sealing element, the health state of the bearing and the like.
FIG. 3 is a schematic diagram showing a typical main interface of fin stabilizer fault diagnosis and health software, wherein the main interface comprises a front left-right, a front right-left, a rear left-right turning fin angle real-time display (from a first set of data), a ship attitude dynamic display and a real-time curve (from a second set of data), and a current ship sailing speed display; the fin stabilizer state display has a normal navigational speed fin stabilizer state index of more than 8Kn, and the fin stabilizer device health state display and corresponding score values are divided into 5 grades of health (85-100), good (65-85), abnormal symptoms (45-65), serious abnormal symptoms (35-45) and incapacity of running (0-35); the alarm bar alarm display function can display alarm time, alarm code number, alarm text and the like, click an alarm bell to enter an alarm interface, and view historical alarms according to time; the bottom of the interface is provided with a maintenance prompt function.
Fig. 4 is a network monitoring interface of a typical control system of fin stabilizer fault diagnosis and health software, which is mainly capable of monitoring real-time states of a master station module, a slave station module, a proportional valve and a network of the control system, and if a corresponding control module fails, a red indicator light will appear below the corresponding control module to give an alarm, and meanwhile, a main interface alarm bar will be correspondingly displayed.
Fig. 5 is a schematic diagram showing a monitoring and diagnosing interface of a typical hydraulic system of a fin stabilizer fault diagnosis and health software, and has the main functions of working states of an electromagnetic relief valve, a fin turning valve, a locking valve and a pump outlet electromagnetic reversing valve of a hydraulic circuit, outlet flow and pressure of a main pump outlet, a main pipeline, an energy accumulator and the like, and water PPM value in oil in an inlet and outlet of an oil cooler, and can display liquid level height of an oil tank and oil temperature, and display information of health state, volumetric efficiency and the like of a state analysis module diagnosis main pump.
FIG. 6 is a diagram showing a typical mechanical equipment monitoring and diagnosing interface of the stabilizer device fault diagnosis and health software, wherein the main functions are to display the fin turning pressure and pressure difference curves collected and processed by the PXIe analog module in real time, the state information of the locking mechanism of the actuator transmitted by the main controller, the real-time fin turning angle information and the underwater sealing water containing condition of the actuator; and displaying the health state of the bearing of the executing mechanism calculated by the state analysis module.
The above examples illustrate only a few embodiments of the invention, which are described in detail and are not to be construed as limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
Claims (1)
1. The stabilizer device fault diagnosis and health forecast system is characterized by comprising a fault diagnosis and health forecast analysis and forecast system, a stabilizer main control system, an execution element and a hydraulic unit field sensing acquisition element; the fault diagnosis and health forecast analysis and prediction system directly collects the message information and high-frequency data of the gyroscope, and the fin stabilizer main control system collects the state information of the executive component and the on-site sensor signals of the hydraulic unit to convert the state information and the on-site sensor signals into text data to be uploaded to the fault diagnosis and health forecast analysis and prediction system; the fault diagnosis and health forecast analysis prediction system monitors the state information of the executing element, the hydraulic unit and the electric control module in real time, and visually presents the health state of the stabilizer through a human-computer interface;
the fault diagnosis and health forecast analysis and prediction system comprises a PXIe main controller, a PXIe dynamic acquisition module, a PXIe analog acquisition module and a display; the fin stabilizer master control system comprises a control master station and a control slave station; the executing element comprises a proportional valve, a reversing valve and an executing mechanism; the hydraulic unit on-site sensing acquisition element comprises a vibration sensor, a flow sensor, a pressure sensor, a temperature sensor, a liquid level sensor, an oil water sensor, a water seepage detection rope and a triaxial gyroscope;
the fault diagnosis and health forecast analysis and prediction system comprises a database for providing data support for relevant real-time state monitoring, fault diagnosis, health forecast analysis and maintenance reminding of the fin stabilizer man-machine interaction interface; receiving data transmitted by a control master station through ModbusTCP and directly storing the data into a database;
reading the message information of the gyroscope from a serial port of the PXIe main controller, analyzing the real-time value through data analysis, analyzing the rolling, pitching and heaving amplitude of the ship, and storing the real-time value into a database;
the method comprises the steps that a main pump vibration signal collected through a PXIe dynamic collection module is configured on a human-computer interaction interface, the collected signal is directly stored under a specified file directory, FFT time-frequency transformation analysis is carried out on the signal, a specified frequency band energy value is extracted and is used as a characteristic value to be stored in a database, and a value range average amplitude value, square root amplitude value, peak value, mean square amplitude value, waveform index, peak value index, pulse index, margin index and skewness index are calculated and are used as value range characteristic values to be stored in the database;
the fault diagnosis and health forecast analysis prediction system comprises a state analysis module, wherein vibration signals, flow signals and pressure signals in a database are extracted and subjected to filtering treatment, the vibration signals with power spectrum density are reconstructed by data decomposition according to different frequency bands, and the fault type, failure mode and health state of a hydraulic pump are judged in real time through a fuzzy neural network by combining typical fault characteristics and theoretical volumetric efficiency of the hydraulic pump and the characteristic values of the outlet pressure, flow and temperature of the main pump;
the state analysis module calculates a real-time differential pressure signal according to a fin rotating angular speed signal and a fin rotating oil cylinder pressure signal in a database, solves the resistance lifting force, the resistance moment, the fluid moment and the fluid moment coefficients acting on fin leaves of the stabilizer through iteration according to the fin rotating angular speed signal, calculates a bearing stress value acting on a fin shaft of an actuating mechanism according to a stress calculation formula, and can find a life value under current stress according to a relation curve of a stress amplitude level born by a material and the number of stress cycles experienced when fatigue damage occurs under the stress amplitude, and then obtains the residual life of the bearing according to an accumulated damage theory.
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