Fault diagnosis method for rotary machine based on angle resampling and ROC (Receiver Operating Characteristic)-SVM (Support Vector Machine

Abstract

The invention discloses a fault diagnosis method for a rotary machine based on angle resampling and an ROC (Receiver Operating Characteristic)-SVM (Support Vector Machine) and belongs to the field offault diagnosis of mechanical equipment. The method comprises the steps of eliminating fluctuation of a rotation speed by use of an angle resampling technology; performing characteristic value extraction from the dimensions of a time domain and a time-frequency domain; and implementing characteristic selection and fault diagnosis of the rotary machine by use of the ROC-SVM. According to the faultdiagnosis method for the rotary machine based on angle resampling and the ROC-SVM, the change of the number of sampling points of a vibration signal in unit time caused by the fluctuation of the rotation speed can be effectively eliminated by use of the angle resampling method, thereby improving the quality of the subsequent extraction characteristic value; the time domain and the time-frequency domain are combined to achieve wider characteristic extraction and obtain sufficient vibration signal information; the characteristic selection and fault diagnosis are performed by use of the ROC-SVM,the best characteristics are selected to prevent poor characteristics from reducing the effect of a fault classifier; the accuracy and effectiveness of the bearing fault diagnosis can be improved, thediagnosis speed can be accelerated, and a new concept is provided for solving the problem of the bearing fault diagnosis.

Description

technical field [0001] The invention belongs to the field of fault diagnosis of mechanical equipment, and more specifically relates to an angle resampling technology for rotational speed fluctuation signals and a method and equipment for fault diagnosis of rotating machinery based on ROC-SVM. Background technique [0002] At present, rotating machinery has become an important part of industrial equipment systems, and its operating status directly affects the stable operation of the entire system. The failure of rotating machinery will reduce the reliability of the system and reduce the service life of the system, and even cause serious casualties and economic losses. Therefore, it is very necessary to carry out fault diagnosis on rotating machinery. [0003] Most of the traditional fault diagnosis methods for rotating machinery are based on time-domain analysis or frequency-domain analysis or time-frequency domain analysis. , frequency domain, and time-frequency domain ana...

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Problems solved by technology

[0003] Most of the traditional fault diagnosis methods for rotating machinery are based on time-domain analysis or frequency-domain analysis or time-frequency domain analysis. , frequency domain, and time-frequency domain analysis are not optimal for accurate evaluation

[0004] In addition, the support vector machine (Support vector machine, SVM) can realize the classification of damaged and non-damaged eigenvalues, but for the classifier, the size and quality of the input eigenvalue matrix are the most affecting the classification effect. Many classifications based on SVM There is no good feature extraction method

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