Abstract
The performance evaluation of fault diagnosis algorithm is an indispensable link in the development and acceptance of the fault diagnosis system. Aiming at the stability evaluation of the fault diagnosis model based on the characteristic clustering, an image edge detection method based on the Elliptic Fourier Descriptor (EFDSE) is proposed to evaluate the stability of the fault diagnosis model, which applies similarity measurement of image to effective evaluation of faulty diagnosis algorithm. The quantitative evaluation index of the diagnostic capability of characterization based cluster fault diagnosis model is used to provide reference for the acceptance and reliability of the diagnosis results. Finally, the effectiveness of the stability evaluation is verified by the fault data of the motor bearings.
Highlights
With the development of modern industrial technology and information technology, manufacturing systems in various fields such as new energy, communication, computer, and industry are becoming more and more complex
This paper mainly studies the stability evaluation method of fault diagnosis model (EFDSE)
This paper shows that if the stability of the fault diagnosis model based on the feature clustering is good, and the cohesiveness of the class center is stronger
Summary
With the development of modern industrial technology and information technology, manufacturing systems in various fields such as new energy, communication, computer, and industry are becoming more and more complex. The main methods are relative deviation and residual squared sum method [4] which are error analysis method, grey correlation theory [5] and ED train based on statistical data, Confidence interval [6], etc. The confidence interval index is based on the hypothesis that the result of the training data group’s diagnosis conforms to the normal distribution It will produce a large number of errors in the case of small data, and the upper limit of confidence interval does not converge to 1 with the increase of the accuracy. It is not suitable for models with high accuracy in fault diagnosis. The application example of motor bearing diagnosis is compared and verified, which proves that the method proposed in this paper is effective
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