CN115469643A - Nuclear power station rotating machinery health management method, system and medium - Google Patents
Nuclear power station rotating machinery health management method, system and medium Download PDFInfo
- Publication number
- CN115469643A CN115469643A CN202211130386.9A CN202211130386A CN115469643A CN 115469643 A CN115469643 A CN 115469643A CN 202211130386 A CN202211130386 A CN 202211130386A CN 115469643 A CN115469643 A CN 115469643A
- Authority
- CN
- China
- Prior art keywords
- data
- fault
- monitoring
- characteristic data
- fault diagnosis
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 230000036541 health Effects 0.000 title claims abstract description 62
- 238000007726 management method Methods 0.000 title claims description 59
- 238000012544 monitoring process Methods 0.000 claims abstract description 158
- 238000003745 diagnosis Methods 0.000 claims abstract description 104
- 238000012423 maintenance Methods 0.000 claims abstract description 67
- 238000000034 method Methods 0.000 claims abstract description 31
- 238000007781 pre-processing Methods 0.000 claims abstract description 21
- 238000013075 data extraction Methods 0.000 claims abstract description 9
- 238000004422 calculation algorithm Methods 0.000 claims description 40
- 238000000605 extraction Methods 0.000 claims description 15
- 238000012545 processing Methods 0.000 claims description 7
- 238000001914 filtration Methods 0.000 claims description 4
- 238000012216 screening Methods 0.000 claims description 3
- 230000003862 health status Effects 0.000 claims 1
- 238000010586 diagram Methods 0.000 description 11
- 230000000007 visual effect Effects 0.000 description 9
- 238000011156 evaluation Methods 0.000 description 8
- 238000004458 analytical method Methods 0.000 description 5
- 230000002452 interceptive effect Effects 0.000 description 5
- 230000009466 transformation Effects 0.000 description 5
- 238000012800 visualization Methods 0.000 description 5
- 230000008901 benefit Effects 0.000 description 4
- 230000005540 biological transmission Effects 0.000 description 4
- 230000006870 function Effects 0.000 description 4
- 238000012360 testing method Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 3
- 230000003993 interaction Effects 0.000 description 3
- 238000004519 manufacturing process Methods 0.000 description 3
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 238000013528 artificial neural network Methods 0.000 description 2
- 238000012217 deletion Methods 0.000 description 2
- 230000037430 deletion Effects 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000010354 integration Effects 0.000 description 2
- 238000005065 mining Methods 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 238000001228 spectrum Methods 0.000 description 2
- 241001391944 Commicarpus scandens Species 0.000 description 1
- 208000037170 Delayed Emergence from Anesthesia Diseases 0.000 description 1
- 241000220317 Rosa Species 0.000 description 1
- 230000002159 abnormal effect Effects 0.000 description 1
- 238000009825 accumulation Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 238000003066 decision tree Methods 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000012854 evaluation process Methods 0.000 description 1
- 239000000383 hazardous chemical Substances 0.000 description 1
- 231100000206 health hazard Toxicity 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 230000001788 irregular Effects 0.000 description 1
- 239000010687 lubricating oil Substances 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000013508 migration Methods 0.000 description 1
- 230000005012 migration Effects 0.000 description 1
- 230000000737 periodic effect Effects 0.000 description 1
- 230000002035 prolonged effect Effects 0.000 description 1
- 238000007637 random forest analysis Methods 0.000 description 1
- 230000008439 repair process Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000007789 sealing Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 238000010183 spectrum analysis Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000013526 transfer learning Methods 0.000 description 1
- 238000010977 unit operation Methods 0.000 description 1
- 238000011144 upstream manufacturing Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0208—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the configuration of the monitoring system
- G05B23/0213—Modular or universal configuration of the monitoring system, e.g. monitoring system having modules that may be combined to build monitoring program; monitoring system that can be applied to legacy systems; adaptable monitoring system; using different communication protocols
-
- G—PHYSICS
- G21—NUCLEAR PHYSICS; NUCLEAR ENGINEERING
- G21D—NUCLEAR POWER PLANT
- G21D3/00—Control of nuclear power plant
- G21D3/04—Safety arrangements
- G21D3/06—Safety arrangements responsive to faults within the plant
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/20—Pc systems
- G05B2219/24—Pc safety
- G05B2219/24065—Real time diagnostics
Landscapes
- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Emergency Management (AREA)
- Plasma & Fusion (AREA)
- General Engineering & Computer Science (AREA)
- High Energy & Nuclear Physics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Testing And Monitoring For Control Systems (AREA)
Abstract
The embodiment of the invention provides a method, a system and a medium for health management of a rotating machine of a nuclear power station, wherein the method comprises the following steps: acquiring monitoring data and DCS data of a rotating machine; carrying out data preprocessing on the monitoring data and the DCS data; performing characteristic data extraction on the preprocessed monitoring data and DCS data to generate state monitoring characteristic data, fault diagnosis characteristic data and fault prediction characteristic data; performing state monitoring, fault diagnosis and fault prediction on the rotary machine according to the state monitoring characteristic data, the fault diagnosis characteristic data and the fault prediction characteristic data; and judging the health state of the rotary machine of the nuclear power station according to the state monitoring data, the fault diagnosis data, the fault prediction data, the historical condition of the rotary machine and the prior knowledge. The embodiment of the invention solves the problem of low operation and maintenance management efficiency caused by poor real-time performance of monitoring the rotating machinery in the prior art.
Description
Technical Field
The invention relates to a method, a system and a medium for health management of a rotating machine of a nuclear power station.
Background
Nuclear power is a very complex system with immeasurable consequences in case of an accident. The rotary machine occupies a considerable proportion in a nuclear power station, is easy to break down, and seriously influences nuclear power safety, so that the rotary machine has important practical application value in condition monitoring and fault diagnosis research.
In China, besides a vibration monitoring system imported from abroad is arranged in each nuclear power plant, a BENTLY vibration data acquisition device is used for carrying out data acquisition on site aiming at other common means for monitoring the state of a plurality of rotating machines at present; and the main pump vibration monitoring system of the domestic M310 unit has irregular cabinet end functions and also needs to perform off-line data acquisition and analysis during overhaul. Every nuclear power unit has hundreds of rotating machines, and the monitoring and operation and maintenance management mode of the existing rotating machines has the following problems: the efficiency of manual data acquisition and offline analysis is low, the real-time performance is poor, faults cannot be found in time, and the maintenance time of the main pump is prolonged; the field noise is extremely large, and potential health hazards exist for collection personnel; the conventional data analysis is only directed to vibration data of a rotating machine, and the analysis of the vibration data is limited to comparison and evaluation between a vibration intensity value and a threshold value (a warning value and a shutdown value), so that the comprehensive equipment health management cannot be performed.
Disclosure of Invention
In order to solve the problem of low operation and maintenance management efficiency caused by poor real-time monitoring performance of rotating machinery in the prior art, the embodiment of the invention provides a method, a system and a medium for health management of the rotating machinery of a nuclear power station.
The embodiment of the invention is realized by the following technical scheme:
in a first aspect, an embodiment of the present invention provides a method for health management of a rotating machine in a nuclear power plant, including:
acquiring monitoring data and DCS data of the rotating machinery;
carrying out data preprocessing on the monitoring data and the DCS data;
performing characteristic data extraction on the preprocessed monitoring data and DCS data to generate state monitoring characteristic data, fault diagnosis characteristic data and fault prediction characteristic data;
performing state monitoring, fault diagnosis and fault prediction on the rotating machine according to the state monitoring characteristic data, the fault diagnosis characteristic data and the fault prediction characteristic data;
and judging the health state of the rotary machine of the nuclear power station according to the state monitoring data, the fault diagnosis data, the fault prediction data, the historical condition of the rotary machine and the prior knowledge.
Further, the monitoring data includes: vibration monitoring data and acoustic emission data of the rotating machine; the DCS data comprises the operation condition of the unit and the operation parameters of the rotating machinery.
Further, carrying out data preprocessing on the monitoring data and the DCS data; the method comprises the following steps:
and screening and filtering the monitoring data and the DCS data according to the data structures and the data lengths of the monitoring data and the DCS data information, and fusing the filtered monitoring data and the filtered DCS data to obtain the preprocessed monitoring data and the preprocessed DCS data.
Further, the rotating machine is subjected to state monitoring, fault diagnosis and fault prediction according to the state monitoring characteristic data, the fault diagnosis characteristic data and the fault prediction characteristic data; the method comprises the following steps:
judging whether to carry out threshold value alarm, trend alarm and/or comprehensive alarm according to the state monitoring characteristic data; if yes, visually displaying alarm information and an alarm log according to a three-dimensional result; if comprehensive alarm is judged according to the state monitoring characteristic data, whether fault diagnosis and fault prediction are started or not is judged; if so, carrying out fault diagnosis and fault prediction.
Further, the fault diagnosis includes:
diagnosing the type, position and degree of equipment faults according to the fault diagnosis characteristic data to obtain diagnosis data;
and judging whether the time required by serious equipment failure and secondary failure occur or not according to the diagnostic data, the failure correlation data and the historical trend.
Further, the failure prediction includes:
comparing the trend of the fault prediction characteristic data with a standard trend, and judging whether the rotary machine has a potential fault, a fault type, a fault position and fault time;
acquiring historical average time before failure by using a fault abnormity database to predict the time of next fault generation;
and comparing the trend of the characteristics of the potential fault with the standard trend to generate potential fault information, the next fault time and the residual life of the rotary machine.
Further, judging the health state of the rotary machine of the nuclear power station according to the state monitoring data, the fault diagnosis data, the fault prediction data, the historical condition of the rotary machine and the prior knowledge; the method comprises the following steps:
and comprehensively evaluating the health state of the rotating machine by using the state monitoring result, the fault diagnosis result, the fault prediction result, the maintenance result and the prior knowledge.
In a second aspect, an embodiment of the present invention provides a health management system for a rotating machine of a nuclear power plant, including:
the acquisition unit is used for acquiring monitoring data and DCS data of the rotary machine;
the preprocessing unit is used for preprocessing the monitoring data and the DCS data;
the extraction unit is used for extracting characteristic data of the preprocessed monitoring data and the preprocessed DCS data to generate state monitoring characteristic data, fault diagnosis characteristic data and fault prediction characteristic data;
the diagnosis and prediction unit is used for carrying out state monitoring, fault diagnosis and fault prediction on the rotary machine according to the state monitoring characteristic data, the fault diagnosis characteristic data and the fault prediction characteristic data; and
and the judging unit is used for judging the health state of the rotary machine of the nuclear power station according to the state monitoring data, the fault diagnosis data, the fault prediction data, the historical condition of the rotary machine and the prior knowledge.
In a third aspect, an embodiment of the present invention provides a health management system for a rotating machine of a nuclear power plant, including:
the edge end is used for being arranged on the rotary machine and used for acquiring monitoring data and DCS data of the rotary machine; carrying out data preprocessing on the monitoring data and the DCS data; performing characteristic data extraction on the preprocessed monitoring data and DCS data to generate state monitoring characteristic data, fault diagnosis characteristic data and fault prediction characteristic data; performing state monitoring, fault diagnosis and fault prediction on the rotating machine according to the state monitoring characteristic data, the fault diagnosis characteristic data and the fault prediction characteristic data; the system is used for judging the health state of the rotary machine of the nuclear power station according to state monitoring data, fault diagnosis data, fault prediction data, the historical condition of the rotary machine and priori knowledge; and
and the cloud end is used for establishing, maintaining, pushing and deploying various algorithms by utilizing the key data and the processing result returned by the edge end, and evaluating and deciding the comprehensive health state of the equipment.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, where instructions are stored, and when the instructions are executed on a computer, the method for health management of a rotating machine of a nuclear power plant is performed.
Compared with the prior art, the embodiment of the invention has the following advantages and beneficial effects:
according to the method, the system and the medium for health management of the rotating machinery of the nuclear power station, monitoring data and DCS data of the rotating machinery are obtained; carrying out data preprocessing on the monitoring data and the DCS data; performing characteristic data extraction on the preprocessed monitoring data and DCS data to generate state monitoring characteristic data, fault diagnosis characteristic data and fault prediction characteristic data; performing state monitoring, fault diagnosis and fault prediction on the rotary machine according to the state monitoring characteristic data, the fault diagnosis characteristic data and the fault prediction characteristic data; the health state of the rotary machine of the nuclear power station is judged according to the state monitoring data, the fault diagnosis data, the fault prediction data, the historical condition of the rotary machine and the priori knowledge, and the problem of low operation and maintenance management efficiency caused by poor monitoring real-time performance of the rotary machine in the prior art is solved.
Drawings
In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and that for those skilled in the art, other related drawings can be obtained from these drawings without inventive effort.
Fig. 1 is a flow chart diagram of a health management method for a rotating machine of a nuclear power plant.
Fig. 2 is a schematic structural diagram of a health management system of a rotating machine of a nuclear power plant.
FIG. 3 is a schematic diagram of an exemplary health management system for a rotating machine of a nuclear power plant.
FIG. 4 is a logic diagram of monitoring the health management status of the rotating machinery of the nuclear power plant.
FIG. 5 is a logic diagram of a health management fault prediction for a rotating machine of a nuclear power plant.
FIG. 6 is a logic diagram of intelligent decision making for health management and maintenance of rotating machinery in a nuclear power plant.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to examples and accompanying drawings, and the exemplary embodiments and descriptions thereof are only used for explaining the present invention and are not meant to limit the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. However, it will be apparent to one of ordinary skill in the art that: it is not necessary to employ these specific details to practice the present invention. In other instances, well-known structures, circuits, materials, or methods have not been described in detail in order to avoid obscuring the present invention.
Throughout the specification, reference to "one embodiment," "an embodiment," "one example" or "an example" means: the particular features, structures, or characteristics described in connection with the embodiment or example are included in at least one embodiment of the invention. Thus, the appearances of the phrase "one embodiment," "an embodiment," "one example" or "an example" in various places throughout this specification are not necessarily all referring to the same embodiment or example. Furthermore, the particular features, structures, or characteristics may be combined in any suitable combinations and/or subcombinations in one or more embodiments or examples. Further, those of ordinary skill in the art will appreciate that the illustrations provided herein are for illustrative purposes and are not necessarily drawn to scale. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
In the description of the present invention, the terms "front", "rear", "left", "right", "upper", "lower", "vertical", "horizontal", "upper", "lower", "inner", "outer", etc. indicate orientations or positional relationships based on those shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed in a particular orientation, and be operated, and therefore, should not be construed as limiting the scope of the present invention.
Examples
In order to solve the problem of low operation and maintenance management efficiency caused by poor real-time monitoring of rotating machinery in the prior art, the embodiment of the invention provides a method, a system and a medium for health management of the rotating machinery of a nuclear power station. In a first aspect, an embodiment of the present invention provides a method for health management of a rotating machine in a nuclear power plant, which is shown in fig. 1, and includes:
s1, acquiring monitoring data and DCS data of a rotating machine;
s2, performing data preprocessing on the monitoring data and the DCS data;
s3, extracting characteristic data of the preprocessed monitoring data and DCS data to generate state monitoring characteristic data, fault diagnosis characteristic data and fault prediction characteristic data;
for feature data extraction, a deep learning network can be specially designed according to the vibration signal characteristics of the rotary bearing, for example, an embedded circulation network architecture is designed according to the characteristics of time-frequency conversion and quasi-periodicity of signals of the rotary bearing, deep local features are extracted by stacking residual blocks, time domain features are extracted by a circulation unit, and weak fault features under the condition of low signal-to-noise ratio are extracted.
S4, performing state monitoring, fault diagnosis and fault prediction on the rotary machine according to the state monitoring characteristic data, the fault diagnosis characteristic data and the fault prediction characteristic data;
in engineering practice, the model, size, load, rotating speed and the like of the rotating mechanical equipment are different from test conditions, the equipment on site is in a normal state for a long time, and the amount of labeled fault data is small. Aiming at the problems that time domain data features are difficult to extract, data are different under different working conditions, data are more in label and less in label, and the like, a fault diagnosis or classification recognition algorithm can be set up by adopting a transfer learning method.
The rotary machine fault prediction is based on a large amount of historical monitoring data, and is supported by priori knowledge experience, so that the service life and the equipment operation trend are predicted. The historical monitoring data is used as a data source, comprehensive monitoring indexes are constructed, a big data algorithm (such as a neural network, a random forest, an SVM, a Bayesian algorithm and the like) is adopted for analyzing and modeling the key monitoring indexes to form a fault diagnosis model, and the accuracy of the model is improved by constantly comparing the key monitoring indexes with the conformity of the results. And accessing the real-time data of the monitoring of the state of the rotating machine into the prediction model for mining analysis to obtain a model result and evaluating the model result, and storing the final result of evaluation to continue optimizing the model.
Higher-order spectrum analysis and typical time-frequency transformation technologies can be tried to be introduced, such as a higher-order moment spectrum, a higher-order cumulant spectrum, fractional Fourier transformation, wavelet transformation, a Lipschitz index, a fractal dimension, cohen-type time-frequency transformation, a fuzzy function and the like, characteristics in signals are comprehensively extracted, PCA and popular learning are combined to screen the characteristics, the obtained characteristics can maximally represent information of monitoring data, and meanwhile the size of the characteristics is minimized. And then, identifying the running state of the rotating machine by utilizing a neural network, an SVM, a KNN and a decision tree and combining Boosting, bagging and other integrated algorithms, and timely discovering the existing early faults of the rotating machine.
And S5, judging the health state of the rotary machine of the nuclear power station according to the state monitoring data, the fault diagnosis data, the fault prediction data, the historical condition of the rotary machine and the prior knowledge.
The embodiment of the invention obtains the monitoring data and DCS data of the rotating machinery; carrying out data preprocessing on the monitoring data and the DCS data; performing characteristic data extraction on the preprocessed monitoring data and DCS data to generate state monitoring characteristic data, fault diagnosis characteristic data and fault prediction characteristic data; performing state monitoring, fault diagnosis and fault prediction on the rotary machine according to the state monitoring characteristic data, the fault diagnosis characteristic data and the fault prediction characteristic data; the health state of the rotary machine of the nuclear power station is judged according to the state monitoring data, the fault diagnosis data, the fault prediction data, the historical condition of the rotary machine and the priori knowledge, and the problem of low operation and maintenance management efficiency caused by poor monitoring real-time performance of the rotary machine in the prior art is solved.
Further, the monitoring data includes: vibration monitoring data and acoustic emission data of the rotating machine; the DCS data comprises the operation condition of the unit and the operation parameters of the rotating machinery.
Optionally, the DCS data includes operation conditions of the unit (such as temperatures of an inlet and an outlet of an important device/pipe section, pressures, unit operation power, and the like), and important operation parameters of the rotary machine (such as coil voltage/current, lubricating oil temperature, sealing pressures at various levels, and the like).
Further, carrying out data preprocessing on the monitoring data and the DCS data; the method comprises the following steps:
and screening and filtering the monitoring data and the DCS data according to the data structures and the data lengths of the monitoring data and the DCS data information, and fusing the filtered monitoring data and the filtered DCS data to obtain the preprocessed monitoring data and the preprocessed DCS data.
Further, the rotating machine is subjected to state monitoring, fault diagnosis and fault prediction according to the state monitoring characteristic data, the fault diagnosis characteristic data and the fault prediction characteristic data; the method comprises the following steps:
judging whether to carry out threshold value alarm, trend alarm and/or comprehensive alarm according to the state monitoring characteristic data; if yes, visually displaying alarm information and an alarm log according to a three-dimensional result; if comprehensive alarming is judged according to the state monitoring characteristic data, whether fault diagnosis and fault prediction are started is judged; if yes, fault diagnosis and fault prediction are carried out.
Further, the fault diagnosis includes:
diagnosing the type, position and degree of equipment fault according to the fault diagnosis characteristic data to obtain diagnosis data;
and judging whether the time required by the equipment to be seriously failed and whether secondary failure occurs or not according to the diagnostic data, the failure correlation data and the historical trend.
Further, the failure prediction includes:
comparing the trend of the fault prediction characteristic data with the standard trend, and judging whether the rotary machine has a potential fault, a fault type, a fault position and fault time;
acquiring historical average time before failure by using a fault abnormity database to predict the time of next fault generation;
and comparing the trend of the characteristics of the latent fault with the standard trend to generate latent fault information, next fault time and residual service life of the rotary machine.
Further, judging the health state of the rotary machine of the nuclear power station according to the state monitoring data, the fault diagnosis data, the fault prediction data, the historical condition of the rotary machine and the prior knowledge; the method comprises the following steps:
and comprehensively evaluating the health state of the rotating machine by using the state monitoring result, the fault diagnosis result, the fault prediction result, the maintenance result and the prior knowledge.
Illustratively, the health management method for the rotating machinery of the nuclear power plant comprises the following steps:
(1) Receiving and storing data and information
The data sources of the rotary machine health management system comprise data acquired from additional sensors (stored in binary files), data acquired from DCS, offline test data and result data after feature extraction, state monitoring, fault diagnosis and fault prediction and intermediate data needing visualization, and the main information sources of the system comprise information such as equipment design, manufacturing, installation and decommissioning information (from files or manual input), equipment operation state abnormity information (from historical diagnosis, prediction and evaluation results), maintenance work order information in a maintenance information management system (from a database of the maintenance information management system) and equipment maintenance plans in a calculation management module (from equipment maintenance outline or production plan).
The system combines a standard data transmission protocol to establish a high-efficiency, safe, accurate and reasonable data transmission logical channel (non-physical channel) so as to realize the real-time transmission of the whole flow of the data stream. Meanwhile, aiming at the data information, a reasonable storage mode is designed for each data by utilizing the advantages of a structured database and an unstructured database so as to adapt to a large-capacity and diversified data storage scene.
(2) Data preprocessing and feature extraction
The system screens the monitoring data according to the data structures and the data lengths of the monitoring data and the DCS data information, fuses the monitoring data and the data acquired by the DCS by utilizing a preprocessing algorithm in an algorithm library, and stores the data or the data information in the database.
And filtering out the unqualified data, and writing the monitoring data or related information into a database to establish a corresponding index.
The system utilizes a state monitoring feature extraction algorithm, a fault diagnosis feature extraction algorithm and a fault prediction feature extraction algorithm in an algorithm library to perform feature transformation on the preprocessed monitoring data information, and generates a state monitoring feature, a fault diagnosis feature and a fault prediction feature which are respectively used as the input of a state monitoring node, a fault diagnosis node and a fault prediction node. Meanwhile, the trend visualization service of the main interface is used for calculating the characteristics needing trend visualization through a customized developed visualization characteristic extraction algorithm, and data and time information are used as the input of the trend visualization.
The system establishes a service deployment interface of a state monitoring feature extraction algorithm, a fault diagnosis feature extraction algorithm, a fault prediction feature extraction algorithm and a visual feature extraction algorithm, establishes a visual standard library, communicates upstream and downstream data of a service scene, and establishes a standard information transmission channel.
(3) Condition monitoring
Mainly comprising threshold value alarm, trend alarm and comprehensive alarm, and simultaneously serving as a prerequisite condition for starting fault diagnosis and fault prediction execution nodes, and a main logic diagram thereof is shown by referring to fig. 4.
The system establishes service deployment interfaces of threshold alarm, trend alarm and comprehensive alarm, gets through all data streams in the upper graph, establishes an alarm interaction page according to an alarm result, provides an over-limit characteristic name according to interaction information including alarm equipment and an alarm log, and writes the result into a state monitoring result database.
(4) Fault diagnosis
The method mainly comprises two parts of online fault diagnosis and result evaluation, wherein the online fault diagnosis process mainly diagnoses the type, position and degree of equipment faults by utilizing fault diagnosis characteristic data, the result evaluation process mainly judges the time required by serious equipment faults according to data obtained by equipment diagnosis results and historical trends, and the possibly generated secondary faults are judged through a fault association database.
The system establishes an empirical formula, a physical simulation model, a data driving model and a service deployment interface of a result evaluation algorithm, and deploys an incremental learning application framework. And moreover, all data streams in the graph are opened, an interactive page is established according to the fault diagnosis result, the interactive information comprises fault positions, fault degrees, fault types and time and secondary fault information required by the serious equipment fault degree, the result is written into a fault diagnosis result database, and the main characteristic trend before the fault is stored in a fault prediction standard database to prepare for subsequent fault prediction.
(5) Fault prediction
The method mainly comprises the steps of latent fault prediction and equipment residual life prediction, wherein the latent fault prediction is mainly to compare trends of fault prediction characteristic data with standard trends so as to judge whether the equipment is in latent fault or not, and the main type, the position and the possible fault time of the equipment. Meanwhile, by using a fault exception database, historical average time before failure is obtained, and time when a fault is likely to occur next time is given, and a logic diagram of the time before failure is shown in fig. 5.
The system establishes a service deployment interface of a mathematical statistics algorithm, a data driving algorithm and a service life evaluation algorithm, opens all data streams in the upper graph, establishes an interactive page according to a fault prediction result, and writes the result into a fault prediction result database, wherein the interactive information comprises the actual trend of the potential fault characteristics, the standard trend comparison, the potential fault information, the next failure time and the remaining service life of the equipment.
(6) Intelligent decision making
The system mainly utilizes a state monitoring result, a fault diagnosis result and a fault prediction result database to comprehensively evaluate a diagnosis result, digs out the mapping relation between historical faults and a maintenance strategy according to a maintenance information management system, combines a plan management module to make a maintenance decision suggestion and a plan, generates an automatic report, gives a feedback result according to a maintenance result of a worker, writes the feedback result and abnormal information into an equipment information database together, and accumulates experience for subsequent health management, wherein a logic diagram of the system is shown in fig. 6.
The system establishes a service deployment interface of a maintenance work order information mining algorithm, a health state comprehensive evaluation algorithm and an optimal decision algorithm, establishes a real-time interaction interface of a plan management module, an equipment information database and a maintenance decision knowledge base, interfaces with the maintenance work order management system, puts through all data streams in a picture, autonomously generates a report according to a maintenance decision result, grades maintenance decision suggestions and plans according to a maintenance completion report, writes the result into the equipment information database, and automatically corrects a recommendation weight in the maintenance decision knowledge base.
(7) Plan management module and maintenance
The system establishes a rotary machine plan management module according to an equipment maintenance plan, performs unified management on the periodic maintenance time and the planned maintenance window of the rotary machine, realizes the entry of planned maintenance information through an interactive interface, and guides a time window of maintenance decision.
(8) Knowledge base and maintenance
The system establishes an equipment operation and maintenance knowledge base of the rotary machine according to professional knowledge and knowledge acquired from the maintenance management system, and performs unified management on expert experience, fault knowledge, maintenance schemes, operation and maintenance strategies and the like to form a core equipment operation and maintenance knowledge base.
The key equipment failure knowledge base and the maintenance knowledge base are regularized in a table and rich text mode, and the failure knowledge base, the maintenance knowledge base and field work are related through the relation of 'failure problem-suggested operation-maintenance feedback'. The key equipment failure knowledge base is mainly a failure tree stored according to failures. And when the equipment fails, traversing the fault tree according to the fault condition and the inference rule.
The maintenance knowledge base manages the maintenance schemes of the known faults of the key equipment, and is convenient for maintenance personnel to quickly search the corresponding maintenance schemes to carry out maintenance work when meeting the maintenance of the key equipment.
(9) Algorithm platform and visual standard library
The algorithm platform is used as an integration center of algorithm files on one hand and also used as a programming environment and a compiler for algorithm development on the other hand. Algorithms in the algorithm platform can be issued to each execution node in a service form, the system establishes a proper algorithm platform, provides mainstream languages (such as java, python (which can be accessed into a python third-party library) and C) and an algorithm module development interface for database modification and deletion, and provides standard algorithm modules (such as algorithms of signal processing, feature processing, intelligent classification, intelligent regression and the like) common to each process node, algorithm file modification, deletion, detection and issuing functions.
The visual standard library is used as an integration center of visual standard components, the visual standard components can be deployed in blank pages in a control form, and suppliers should establish a proper visual standard library according to requirements to provide mainstream visual standard components (including but not limited to bar charts, line graphs, area graphs, pie charts, ring graphs, scatter diagrams, bubble graphs, chord graphs, annual ring graphs, wrench graphs, survey graphs, radar graphs, rose graphs, word cloud graphs, network graphs, text flow graphs, migration graphs, cluster graphs, network node graphs, box graphs, circle graphs, thermodynamic diagrams, 3D graphs and the like), so that the visual common functions of multiple rows, multiple Y-axes, real-time updating and the like are supported.
Therefore, the embodiment of the invention utilizes basic information, maintenance information and plan information of the rotating equipment, and additional sensors arranged on the equipment, equipment state information and offline test data in a DCS system, develops and continuously maintains a diagnosis algorithm by building a big data algorithm platform, and realizes the monitoring, early warning, fault diagnosis and prediction of the rotating machinery data; the operation and maintenance strategy support is provided through accumulation and management of monitoring data, diagnosis results and operation and maintenance knowledge, decision basis is provided for production and maintenance, and the association management of operation, maintenance and maintenance of the rotary machine is realized, so that the maintenance technology and the maintenance system are continuously improved, and the safety and the economy of the nuclear power station are improved.
According to the embodiment of the invention, the management of the whole service life of the nuclear power equipment can be realized by constructing an equipment whole-life data center and integrating the operation data of the rotating equipment in the nuclear power station; four modes of maintenance of the rotating equipment, namely after maintenance, planned maintenance, state maintenance and predicted maintenance, are realized. Continuously improving the maintenance technology and the maintenance system; data monitoring and early warning, fault diagnosis, fault analysis and early warning of the rotating machinery are realized; and the relevance management of operation, maintenance and repair of the rotary machine is realized.
In a second aspect, an embodiment of the present invention provides a health management system for a rotating machine of a nuclear power plant, which is shown in fig. 2, and includes:
the acquisition unit is used for acquiring monitoring data and DCS data of the rotary machine;
the preprocessing unit is used for preprocessing the monitoring data and the DCS data;
the extraction unit is used for extracting the feature data of the preprocessed monitoring data and the DCS data to generate state monitoring feature data, fault diagnosis feature data and fault prediction feature data;
the diagnosis and prediction unit is used for carrying out state monitoring, fault diagnosis and fault prediction on the rotary machine according to the state monitoring characteristic data, the fault diagnosis characteristic data and the fault prediction characteristic data; and
and the judging unit is used for judging the health state of the rotary machine of the nuclear power station according to the state monitoring data, the fault diagnosis data, the fault prediction data, the historical condition of the rotary machine and the prior knowledge.
In a third aspect, an embodiment of the present invention provides a health management system for a rotating machine of a nuclear power plant, including:
the edge end is used for being arranged on the rotary machine and used for acquiring monitoring data and DCS data of the rotary machine; carrying out data preprocessing on the monitoring data and the DCS data; performing characteristic data extraction on the preprocessed monitoring data and DCS data to generate state monitoring characteristic data, fault diagnosis characteristic data and fault prediction characteristic data; performing state monitoring, fault diagnosis and fault prediction on the rotary machine according to the state monitoring characteristic data, the fault diagnosis characteristic data and the fault prediction characteristic data; the system is used for judging the health state of the rotary machine of the nuclear power station according to state monitoring data, fault diagnosis data, fault prediction data, the historical condition of the rotary machine and priori knowledge; and
and the cloud end is used for establishing, maintaining, pushing and deploying various algorithms by using the key data and the processing result returned by the edge end, and evaluating and deciding the comprehensive health state of the equipment.
Optionally, the cloud is used for mainly establishing, maintaining, pushing and deploying various algorithms (alarming, diagnosing, predicting and the like) by using the key data and the processing result returned by the edge, and evaluating and deciding the comprehensive health state of the equipment.
The principle and concept of the health management system are the same as the method described above.
Exemplary, as shown with reference to fig. 3. The health management system of the nuclear power station rotating machinery adopts a health management mode combining an edge end and a cloud end, and completes most of work such as data and information receiving, data preprocessing and feature extraction, state monitoring, fault diagnosis, fault prediction and the like by deploying edge end hardware near the rotating machinery, and returns key data and processing results to the cloud end through a network; at the cloud end, establishment, maintenance and push deployment of various algorithms (alarming, diagnosis, prediction and the like) are mainly performed, and evaluation and decision-making are performed on the comprehensive health state of the equipment.
The system utilizes basic information, maintenance information and plan information of the rotating equipment, additional sensors arranged on the equipment, equipment state information and off-line test data in a DCS system, and establishes a complete rotating machinery health management system through intelligent data preprocessing, feature extraction, state early warning, fault diagnosis, fault prediction and intelligent maintenance decision.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, where instructions are stored, and when the instructions are executed on a computer, the method for health management of a rotating machine in a nuclear power plant is performed.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are merely exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. A health management method for a rotating machine of a nuclear power plant is characterized by comprising the following steps:
acquiring monitoring data and DCS data of the rotating machinery;
carrying out data preprocessing on the monitoring data and the DCS data;
performing characteristic data extraction on the preprocessed monitoring data and DCS data to generate state monitoring characteristic data, fault diagnosis characteristic data and fault prediction characteristic data;
performing state monitoring, fault diagnosis and fault prediction on the rotary machine according to the state monitoring characteristic data, the fault diagnosis characteristic data and the fault prediction characteristic data;
and judging the health state of the rotary machine of the nuclear power station according to the state monitoring data, the fault diagnosis data, the fault prediction data, the historical condition of the rotary machine and the prior knowledge.
2. The method for health management of rotating machinery of a nuclear power plant as recited in claim 1, wherein the monitoring data includes: vibration monitoring data and acoustic emission data of the rotating machine; the DCS data comprises the operation condition of the unit and the operation parameters of the rotating machinery.
3. The method for health management of rotating machinery in nuclear power plants of claim 1, wherein the monitoring data and DCS data are pre-processed; the method comprises the following steps:
and screening and filtering the monitoring data and the DCS data according to the data structures and the data lengths of the monitoring data and the DCS data information, and fusing the filtered monitoring data and the filtered DCS data to obtain the preprocessed monitoring data and the preprocessed DCS data.
4. The health management method for the rotating machinery of the nuclear power plant as claimed in claim 3, wherein the state monitoring, fault diagnosis and fault prediction are performed on the rotating machinery according to the state monitoring characteristic data, the fault diagnosis characteristic data and the fault prediction characteristic data; the method comprises the following steps:
judging whether to carry out threshold value alarm, trend alarm and/or comprehensive alarm according to the state monitoring characteristic data; if yes, visually displaying alarm information and an alarm log according to a three-dimensional result; if comprehensive alarming is judged according to the state monitoring characteristic data, whether fault diagnosis and fault prediction are started is judged; if so, carrying out fault diagnosis and fault prediction.
5. The method for health management of rotating machinery of a nuclear power plant as claimed in claim 4, wherein the fault diagnosis includes:
diagnosing the type, position and degree of equipment fault according to the fault diagnosis characteristic data to obtain diagnosis data;
and judging whether the time required by serious equipment failure and secondary failure occur or not according to the diagnostic data, the failure correlation data and the historical trend.
6. The method for health management of rotating machinery of a nuclear power plant as claimed in claim 5, wherein the fault prediction includes:
comparing the trend of the fault prediction characteristic data with the standard trend, and judging whether the rotary machine has a potential fault, a fault type, a fault position and fault time;
acquiring historical average time before failure by using a failure abnormity database to predict the time of next failure generation;
and comparing the trend of the characteristics of the potential fault with the standard trend to generate potential fault information, the next fault time and the residual life of the rotary machine.
7. The health management method for the rotating machinery of the nuclear power plant as claimed in claim 6, wherein the health status of the rotating machinery of the nuclear power plant is judged according to the status monitoring data, the fault diagnosis data, the fault prediction data, the history of the rotating machinery, and the prior knowledge; the method comprises the following steps:
and comprehensively evaluating the health state of the rotating machine by using the state monitoring result, the fault diagnosis result, the fault prediction result, the maintenance result and the prior knowledge.
8. A nuclear power plant rotating machinery health management system, comprising:
the acquisition unit is used for acquiring monitoring data and DCS data of the rotary machine;
the preprocessing unit is used for preprocessing the monitoring data and the DCS data;
the extraction unit is used for extracting characteristic data of the preprocessed monitoring data and the preprocessed DCS data to generate state monitoring characteristic data, fault diagnosis characteristic data and fault prediction characteristic data;
the diagnosis and prediction unit is used for carrying out state monitoring, fault diagnosis and fault prediction on the rotary machine according to the state monitoring characteristic data, the fault diagnosis characteristic data and the fault prediction characteristic data; and
and the judging unit is used for judging the health state of the rotary machine of the nuclear power station according to the state monitoring data, the fault diagnosis data, the fault prediction data, the historical condition of the rotary machine and the prior knowledge.
9. A nuclear power plant rotating machinery health management system, comprising:
the edge end is used for being arranged on the rotary machine and used for acquiring monitoring data and DCS data of the rotary machine; carrying out data preprocessing on the monitoring data and the DCS data; performing characteristic data extraction on the preprocessed monitoring data and DCS data to generate state monitoring characteristic data, fault diagnosis characteristic data and fault prediction characteristic data; performing state monitoring, fault diagnosis and fault prediction on the rotary machine according to the state monitoring characteristic data, the fault diagnosis characteristic data and the fault prediction characteristic data; the system is used for judging the health state of the rotary machine of the nuclear power station according to state monitoring data, fault diagnosis data, fault prediction data, the historical condition of the rotary machine and priori knowledge; and
and the cloud end is used for establishing, maintaining, pushing and deploying various algorithms by utilizing the key data and the processing result returned by the edge end, and evaluating and deciding the comprehensive health state of the equipment.
10. A computer-readable storage medium having stored thereon instructions that, when executed on a computer, perform a method for health management of rotating machinery of a nuclear power plant as recited in any one of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211130386.9A CN115469643A (en) | 2022-09-15 | 2022-09-15 | Nuclear power station rotating machinery health management method, system and medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211130386.9A CN115469643A (en) | 2022-09-15 | 2022-09-15 | Nuclear power station rotating machinery health management method, system and medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115469643A true CN115469643A (en) | 2022-12-13 |
Family
ID=84334103
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211130386.9A Pending CN115469643A (en) | 2022-09-15 | 2022-09-15 | Nuclear power station rotating machinery health management method, system and medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115469643A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117590149A (en) * | 2023-11-14 | 2024-02-23 | 南方电网调峰调频发电有限公司检修试验分公司 | Fault solution generation method, device and equipment based on big data technology |
Citations (20)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5210704A (en) * | 1990-10-02 | 1993-05-11 | Technology International Incorporated | System for prognosis and diagnostics of failure and wearout monitoring and for prediction of life expectancy of helicopter gearboxes and other rotating equipment |
| CN1514209A (en) * | 2003-08-01 | 2004-07-21 | 重庆大学 | Rotary machine failure intelligent diagnosis method and device |
| CN105809255A (en) * | 2016-03-07 | 2016-07-27 | 大唐淮南洛河发电厂 | IoT-based heat-engine plantrotary machine health management method and system |
| CN106124982A (en) * | 2016-06-14 | 2016-11-16 | 都城绿色能源有限公司 | Automatic expert's resultant fault diagnostic system of a kind of Wind turbines and diagnostic method |
| CN110764493A (en) * | 2019-11-14 | 2020-02-07 | 中国国家铁路集团有限公司 | PHM application system, method and storage medium suitable for high-speed railway |
| CN110766277A (en) * | 2018-10-24 | 2020-02-07 | 中国核电工程有限公司 | Health assessment and diagnosis system and mobile terminal for nuclear industry field |
| CN110782190A (en) * | 2019-12-04 | 2020-02-11 | 江苏方天电力技术有限公司 | Phase modulator remote diagnosis system based on ubiquitous power internet of things technology |
| CN111255674A (en) * | 2020-01-21 | 2020-06-09 | 武汉瑞莱保能源技术有限公司 | System and method for detecting state of rotating mechanical equipment |
| CN111753603A (en) * | 2019-03-29 | 2020-10-09 | 北京市劳动保护科学研究所 | EDG fault diagnosis system of emergency generator set |
| CN112152698A (en) * | 2020-09-24 | 2020-12-29 | 中国科学院空天信息创新研究院 | Health monitoring system and method for remote sensing satellite ground station |
| CN113112038A (en) * | 2021-04-13 | 2021-07-13 | 中国铁道科学研究院集团有限公司 | Intelligent monitoring and diagnostic analysis system, device, electronic equipment and storage medium |
| CN113125135A (en) * | 2021-03-31 | 2021-07-16 | 中石化石油工程技术服务有限公司 | Fault diagnosis method for rotary machine, storage medium, and electronic device |
| CN113391612A (en) * | 2020-03-13 | 2021-09-14 | 株洲中车时代电气股份有限公司 | Fault prediction and health management device and fault prediction method for vehicle transmission control system |
| CN113532848A (en) * | 2021-06-24 | 2021-10-22 | 南京工业大学 | Fault diagnosis system for planetary gear box |
| CN113776794A (en) * | 2021-08-13 | 2021-12-10 | 昆明理工大学 | Fault diagnosis method, device and system for embedded edge computing |
| CN114169549A (en) * | 2021-12-04 | 2022-03-11 | 中国船舶工业综合技术经济研究院 | Fault diagnosis and prediction system for water jet propulsion device based on distribution |
| CN114167842A (en) * | 2021-12-08 | 2022-03-11 | 中国船舶科学研究中心 | Fault prediction and health management method based on vibration active control system |
| CN114254779A (en) * | 2021-12-23 | 2022-03-29 | 九号智能科技(上海)有限公司 | Production equipment health management system based on predictive maintenance |
| CN114841190A (en) * | 2022-03-16 | 2022-08-02 | 台州守敬应用科技研究院有限公司 | Bearing health assessment method based on composite model |
| CN115034248A (en) * | 2021-03-03 | 2022-09-09 | 斯凯孚公司 | Automatic diagnostic method, system and storage medium for equipment |
-
2022
- 2022-09-15 CN CN202211130386.9A patent/CN115469643A/en active Pending
Patent Citations (20)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5210704A (en) * | 1990-10-02 | 1993-05-11 | Technology International Incorporated | System for prognosis and diagnostics of failure and wearout monitoring and for prediction of life expectancy of helicopter gearboxes and other rotating equipment |
| CN1514209A (en) * | 2003-08-01 | 2004-07-21 | 重庆大学 | Rotary machine failure intelligent diagnosis method and device |
| CN105809255A (en) * | 2016-03-07 | 2016-07-27 | 大唐淮南洛河发电厂 | IoT-based heat-engine plantrotary machine health management method and system |
| CN106124982A (en) * | 2016-06-14 | 2016-11-16 | 都城绿色能源有限公司 | Automatic expert's resultant fault diagnostic system of a kind of Wind turbines and diagnostic method |
| CN110766277A (en) * | 2018-10-24 | 2020-02-07 | 中国核电工程有限公司 | Health assessment and diagnosis system and mobile terminal for nuclear industry field |
| CN111753603A (en) * | 2019-03-29 | 2020-10-09 | 北京市劳动保护科学研究所 | EDG fault diagnosis system of emergency generator set |
| CN110764493A (en) * | 2019-11-14 | 2020-02-07 | 中国国家铁路集团有限公司 | PHM application system, method and storage medium suitable for high-speed railway |
| CN110782190A (en) * | 2019-12-04 | 2020-02-11 | 江苏方天电力技术有限公司 | Phase modulator remote diagnosis system based on ubiquitous power internet of things technology |
| CN111255674A (en) * | 2020-01-21 | 2020-06-09 | 武汉瑞莱保能源技术有限公司 | System and method for detecting state of rotating mechanical equipment |
| CN113391612A (en) * | 2020-03-13 | 2021-09-14 | 株洲中车时代电气股份有限公司 | Fault prediction and health management device and fault prediction method for vehicle transmission control system |
| CN112152698A (en) * | 2020-09-24 | 2020-12-29 | 中国科学院空天信息创新研究院 | Health monitoring system and method for remote sensing satellite ground station |
| CN115034248A (en) * | 2021-03-03 | 2022-09-09 | 斯凯孚公司 | Automatic diagnostic method, system and storage medium for equipment |
| CN113125135A (en) * | 2021-03-31 | 2021-07-16 | 中石化石油工程技术服务有限公司 | Fault diagnosis method for rotary machine, storage medium, and electronic device |
| CN113112038A (en) * | 2021-04-13 | 2021-07-13 | 中国铁道科学研究院集团有限公司 | Intelligent monitoring and diagnostic analysis system, device, electronic equipment and storage medium |
| CN113532848A (en) * | 2021-06-24 | 2021-10-22 | 南京工业大学 | Fault diagnosis system for planetary gear box |
| CN113776794A (en) * | 2021-08-13 | 2021-12-10 | 昆明理工大学 | Fault diagnosis method, device and system for embedded edge computing |
| CN114169549A (en) * | 2021-12-04 | 2022-03-11 | 中国船舶工业综合技术经济研究院 | Fault diagnosis and prediction system for water jet propulsion device based on distribution |
| CN114167842A (en) * | 2021-12-08 | 2022-03-11 | 中国船舶科学研究中心 | Fault prediction and health management method based on vibration active control system |
| CN114254779A (en) * | 2021-12-23 | 2022-03-29 | 九号智能科技(上海)有限公司 | Production equipment health management system based on predictive maintenance |
| CN114841190A (en) * | 2022-03-16 | 2022-08-02 | 台州守敬应用科技研究院有限公司 | Bearing health assessment method based on composite model |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117590149A (en) * | 2023-11-14 | 2024-02-23 | 南方电网调峰调频发电有限公司检修试验分公司 | Fault solution generation method, device and equipment based on big data technology |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN101770219B (en) | Knowledge acquisition method of fault diagnosis knowledge library of turn-milling combined machine tool | |
| CN101446827B (en) | Process fault analysis device of process industry system and method therefor | |
| Merkt | On the use of predictive models for improving the quality of industrial maintenance: An analytical literature review of maintenance strategies | |
| CN112257988A (en) | Complex accident feature identification and risk early warning system and method for nuclear power plant | |
| CN116862442A (en) | Intelligent equipment management method and system based on big data analysis | |
| MX2013000577A (en) | Machine learning for power grids. | |
| CN104392752A (en) | Real-time on-line nuclear reactor fault diagnosis and monitoring system | |
| CN101833324B (en) | Intelligent fault diagnosis system in tread extrusion process and diagnosis method thereof | |
| CN101859128A (en) | Knowledge-based fault prediction expert system for complex milling machine tool | |
| CN117930815A (en) | Wind turbine generator remote fault diagnosis method and system based on cloud platform | |
| CN112487058A (en) | Numerical control machine tool fault monitoring and diagnosing system based on data mining | |
| CN114519923B (en) | Intelligent diagnosis and early warning method and system for power plant | |
| CN109492790A (en) | Wind turbines health control method based on neural network and data mining | |
| CN103926490A (en) | Power transformer comprehensive diagnosis method with self-learning function | |
| CN116522088B (en) | Nuclear power plant operation data analysis method and system based on machine learning | |
| CN107767056A (en) | A kind of condition monitoring for power station coal pulverizer with it is health management system arranged | |
| CN117989074A (en) | Intelligent monitoring method for offshore wind turbine based on sensing calculation coordination | |
| CN118244709A (en) | Associated fault equipment and influence factor acquisition method, device and exception handling system | |
| CN115469643A (en) | Nuclear power station rotating machinery health management method, system and medium | |
| CN102929241B (en) | Safe operation guide system of purified terephthalic acid device and application of safe operation guide system | |
| CN114118222A (en) | Coal mill state early warning system and fault identification and diagnosis method | |
| CN117370818B (en) | Intelligent diagnosis method and intelligent environment-friendly system for water supply and drainage pipe network based on artificial intelligence | |
| KR20220089853A (en) | Method for Failure prediction and prognostics and health management of renewable energy generation facilities using machine learning technology | |
| CN116611523B (en) | Method and system for predicting interpretable faults of turbofan engine | |
| CN112145375A (en) | Fan variable pitch system fault diagnosis method based on Neo4j and Bayes |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |