CN103743477B - A kind of mechanical fault detection diagnostic method and equipment thereof - Google Patents
A kind of mechanical fault detection diagnostic method and equipment thereof Download PDFInfo
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- CN103743477B CN103743477B CN201310734996.4A CN201310734996A CN103743477B CN 103743477 B CN103743477 B CN 103743477B CN 201310734996 A CN201310734996 A CN 201310734996A CN 103743477 B CN103743477 B CN 103743477B
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Abstract
The invention discloses a kind of mechanical fault detection diagnostic method, its concrete steps are: in detected plant equipment, install multiple mechanical vibration sensor; Collection machinery equipment runs well respectively, and the vibration signal operated under different faults type condition; Generate the svm classifier rule of mechanical fault detection; SVM training aids is trained, the training rules of adjustment SVM training aids; By vibration transducer collection machinery vibration signal, and carry out fault Preliminary detection by SVM classifier; Application multisample Voting Algorithm, carries out ballot to the result of Preliminary detection and analyzes, obtain final failure detection result.A kind of mechanical fault detection equipment, is characterized in that it comprises SVM training aids and multiple mechanical oscillation signal pick-up transducers.A kind of mechanical fault detection diagnostic method proposed by the invention and equipment thereof, ensure that the accuracy of testing result, can to typical mechanical fault detection and identification, and the fault detection and diagnosis of the plant equipment to multiple variety classes, different performance feature can be adapted to, improve the precision of mechanical fault detection.
Description
Technical field
The present invention relates to a kind of mechanical fault detection method and checkout equipment thereof.
Background technology
Current mechanical failure diagnostic method has a lot, the existing mechanical failure diagnostic method based on static state, also having based on dynamic mechanical failure diagnostic method, is wherein the mechanical fault detection method of detected object with mechanical vibration, is a kind of main stream approach in current mechanical fault detection field.But based on mechanical vibration fault detection method to the diagnostic accuracy of fault and Fault Identification kind, largely depend on the treatment and analysis ability to mechanical oscillation signal.Current existing mechanical failure diagnostic method, most all by the gather and analysis algorithm focusing on mechanical oscillation signal of research, this wherein also comprises and in a large number carries out difference to collected mechanical oscillation signal and convert, or the process such as signal conversion, to improve the recognition capability of mechanical oscillation signal.But this series of typical mechanical failure diagnostic method at present, the fault-time designed by it and diagnosis algorithm, largely depend on statistics or probability analysis, very not clear and definite Fault Identification foundation.Also Just because of this, current proposed mechanical fault detection method has stronger limitation, and namely for different scenes, different detected objects, the ability of its fault detect and effect often difference are very large.
Summary of the invention
Technical matters to be solved by this invention is to provide a kind of mechanical fault detection diagnostic method and equipment thereof, to typical mechanical fault detection and identification, and can adapt to the fault detection and diagnosis of the plant equipment to multiple variety classes, different performance feature.
For solving the problems of the technologies described above, the invention provides a kind of mechanical fault detection diagnostic method, its concrete steps are:
The first step: install multiple mechanical vibration sensor in detected plant equipment, gathers the mechanical oscillation signal of number of different types for multiple different oscillation point;
Second step: allow detected plant equipment start running, collection machinery equipment runs well respectively, and the vibration signal operated under different faults type condition, obtains the vibration raw data of detected plant equipment;
3rd step: application SVM training aids train its to the mechanical oscillation signal obtained of sample train, the svm classifier generating mechanical fault detection is regular;
4th step: using the input data of mechanical fault detection result as SVM training aids, SVM training aids is trained, the training rules of adjustment SVM training aids;
5th step: when to detected mechanical fault detection, first by vibration transducer collection machinery vibration signal, and carry out fault Preliminary detection by SVM classifier;
6th step: after multiple SVM classifier detection obtains the preliminary failure detection result of many groups, application multisample Voting Algorithm, carries out ballot to the result of Preliminary detection and analyzes, obtain final failure detection result.
The testing result obtained each time, all by the input data as SVM training aids, realize mechanical fault diagnosis classifying rules adjustment, the present invention is proposed mechanical failure diagnostic method possess self-learning capability, the detection data correction fault detection method of history can be utilized; Mechanical failure diagnostic method, to organize vibration signal as the data source detected more, can cover the detected multiple vibration signal of mechanical object, and comprehensively carry out sampling and analyzing process, ensure that the accuracy of testing result.
Described multisample Voting Algorithm is a kind of Voting Algorithm based on weight size.
A kind of mechanical fault detection equipment, it comprises SVM training aids and multiple mechanical oscillation signal pick-up transducers, mechanical oscillation signal sampling sensor is arranged on multiple different measuring point in detected plant equipment, thus obtains the vibration signal of multiple different detected object in machine operation process, each group mechanical oscillation signal is sent into SVM training aids by mechanical oscillation signal pick-up transducers respectively, by training and the analysis of a large amount of mechanical oscillation signals, form the classifying rules of SVM training aids, when really carrying out mechanical fault diagnosis, passage is deployed in many group mechanical vibration survey sensors in plant equipment, vibration signal respectively during machinery of sampling running, and realize the detection and Identification to mechanical oscillation signal by SVM classifier, after the detection of each SVM classifier obtains a testing result, the many groups testing result by ballot selector switch, detection obtained again, carry out analyzing and vote and obtain final testing result.
A kind of mechanical fault detection diagnostic method proposed by the invention and equipment thereof, to organize vibration signal as the data source detected more, the detected multiple vibration signal of mechanical object can be covered, and comprehensively carry out sampling and analyzing process, ensure that the accuracy of testing result, can to typical mechanical fault detection and identification, and can adapt to multiple variety classes, the fault detection and diagnosis of the plant equipment of different performance feature, improve the precision of mechanical fault detection, it also possesses self-learning capability simultaneously, the detection data correction fault detection method of history can be utilized.
Accompanying drawing explanation
Fig. 1 is a kind of mechanical fault detection diagnostic method schematic flow sheet proposed by the invention.
Fig. 2 is that mechanical fault detection result is to sorter correction schematic diagram.
Fig. 3 is multisample Voting Algorithm process flow diagram.
Embodiment
See accompanying drawing, a kind of mechanical fault detection diagnostic method, its concrete steps are:
The first step: install multiple mechanical vibration sensor in detected plant equipment, gathers the mechanical oscillation signal of number of different types for multiple different oscillation point;
Second step: allow detected plant equipment start running, collection machinery equipment runs well respectively, and the vibration signal operated under different faults type condition, obtains the vibration raw data of detected plant equipment;
3rd step: application SVM training aids train its to the mechanical oscillation signal obtained of sample train, the svm classifier generating mechanical fault detection is regular;
4th step: using the input data of mechanical fault detection result as SVM training aids, SVM training aids is trained, the training rules of adjustment SVM training aids;
5th step: when to detected mechanical fault detection, first by vibration transducer collection machinery vibration signal, and carry out fault Preliminary detection by SVM classifier;
6th step: after multiple SVM classifier detection obtains the preliminary failure detection result of many groups, application multisample Voting Algorithm, carries out ballot to the result of Preliminary detection and analyzes, obtain final failure detection result.
The testing result obtained each time, all by the input data as SVM training aids, realize mechanical fault diagnosis classifying rules adjustment, the present invention is proposed mechanical failure diagnostic method possess self-learning capability, the detection data correction fault detection method of history can be utilized; Mechanical failure diagnostic method, to organize vibration signal as the data source detected more, can cover the detected multiple vibration signal of mechanical object, and comprehensively carry out sampling and analyzing process, ensure that the accuracy of testing result.
Described multisample Voting Algorithm is a kind of Voting Algorithm based on weight size.
A kind of mechanical fault detection equipment, it comprises SVM training aids and multiple mechanical oscillation signal pick-up transducers, mechanical oscillation signal sampling sensor is arranged on multiple different measuring point in detected plant equipment, thus obtains the vibration signal of multiple different detected object in machine operation process, each group mechanical oscillation signal is sent into SVM training aids by mechanical oscillation signal pick-up transducers respectively, by training and the analysis of a large amount of mechanical oscillation signals, form the classifying rules of SVM training aids, when really carrying out mechanical fault diagnosis, passage is deployed in many group mechanical vibration survey sensors in plant equipment, vibration signal respectively during machinery of sampling running, and realize the detection and Identification to mechanical oscillation signal by SVM classifier, after the detection of each SVM classifier obtains a testing result, the many groups testing result by ballot selector switch, detection obtained again, carry out analyzing and vote and obtain final testing result.
A kind of mechanical fault detection diagnostic method proposed by the invention and equipment thereof, to organize vibration signal as the data source detected more, the detected multiple vibration signal of mechanical object can be covered, and comprehensively carry out sampling and analyzing process, ensure that the accuracy of testing result, can to typical mechanical fault detection and identification, and can adapt to multiple variety classes, the fault detection and diagnosis of the plant equipment of different performance feature, improve the precision of mechanical fault detection, it also possesses self-learning capability simultaneously, the detection data correction fault detection method of history can be utilized.
Claims (2)
1. a mechanical fault detection diagnostic method, its concrete steps are:
The first step: install multiple mechanical vibration sensor in detected plant equipment, gathers the mechanical oscillation signal of number of different types for multiple different oscillation point;
Second step: allow detected plant equipment start running, collection machinery equipment runs well respectively, and the vibration signal operated under different faults type condition, obtains the vibration raw data of detected plant equipment;
3rd step: application SVM training aids train its to the mechanical oscillation signal obtained of sample train, the svm classifier generating mechanical fault detection is regular;
4th step: using the input data of mechanical fault detection result as SVM training aids, SVM training aids is trained, the training rules of adjustment SVM training aids;
5th step: when to detected mechanical fault detection, first by vibration transducer collection machinery vibration signal, and carry out fault Preliminary detection by SVM classifier;
6th step: after multiple SVM classifier detection obtains the preliminary failure detection result of many groups, application multisample Voting Algorithm, carries out ballot to the result of Preliminary detection and analyzes, obtain final failure detection result.
2. a kind of mechanical fault detection diagnostic method according to claim 1, is characterized in that, described multisample Voting Algorithm is a kind of Voting Algorithm based on weight size.
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| CN106595850A (en) * | 2016-12-21 | 2017-04-26 | 潘小胜 | Mechanical oscillation signal fault analysis method |
| CN106969928A (en) * | 2017-03-28 | 2017-07-21 | 辽宁机电职业技术学院 | A kind of mechanical fault detection diagnostic method |
| CN110197194A (en) * | 2019-04-12 | 2019-09-03 | 佛山科学技术学院 | A kind of Method for Bearing Fault Diagnosis and device based on improvement random forest |
| CN115656700B (en) * | 2022-12-09 | 2023-04-14 | 广东美的暖通设备有限公司 | Detection method, training method, electric appliance, monitoring system and storage medium |
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| CN101067930A (en) * | 2007-06-07 | 2007-11-07 | 深圳先进技术研究院 | Intelligent audio frequency identifying system and identifying method |
| CN102661866A (en) * | 2012-05-11 | 2012-09-12 | 天津工业大学 | Engine fault identification method based on time-domain energy and support vector machine |
| CN102944416A (en) * | 2012-12-06 | 2013-02-27 | 南京匹瑞电气科技有限公司 | Multi-sensor signal fusion technology-based fault diagnosis method for wind turbine blades |
| CN103336243A (en) * | 2013-07-01 | 2013-10-02 | 东南大学 | Breaker fault diagnosis method based on separating/closing coil current signals |
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| WO2003041562A2 (en) * | 2001-11-14 | 2003-05-22 | Whitehead Institute For Biomedical Research | Molecular cancer diagnosis using tumor gene expression signature |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN101067930A (en) * | 2007-06-07 | 2007-11-07 | 深圳先进技术研究院 | Intelligent audio frequency identifying system and identifying method |
| CN102661866A (en) * | 2012-05-11 | 2012-09-12 | 天津工业大学 | Engine fault identification method based on time-domain energy and support vector machine |
| CN102944416A (en) * | 2012-12-06 | 2013-02-27 | 南京匹瑞电气科技有限公司 | Multi-sensor signal fusion technology-based fault diagnosis method for wind turbine blades |
| CN103336243A (en) * | 2013-07-01 | 2013-10-02 | 东南大学 | Breaker fault diagnosis method based on separating/closing coil current signals |
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Application publication date: 20140423 Assignee: Liuzhou Zhuode Machinery Technology Co.,Ltd. Assignor: LIUZHOU VOCATIONAL & TECHNICAL College Contract record no.: X2023980053806 Denomination of invention: A Method and Equipment for Mechanical Fault Detection and Diagnosis Granted publication date: 20160113 License type: Common License Record date: 20231225 |
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