Induction motor fault diagnosis method based on discriminant convolutional feature learning

Abstract

The invention discloses an induction motor fault diagnosis method based on discriminant convolutional feature learning. The induction motor fault diagnosis method comprises the following steps that induction motor vibration signals of the known fault category are acquired and the motor vibration signals are marked according to the category so that training samples are acquired; discriminant convolutional feature learning is performed on the training samples, and all the training samples are represented by utilizing feature vectors obtained through learning; and tags are added on the feature vectors obtained through learning and an SVM classifier is trained by utilizing the feature vectors, and the optimal classification parameters of the SVM classifier are determined. The effective fault features of the motor vibration signals are learned in an unsupervised manner. Compared with the existing fault diagnosis technology and the machine learning method, the discriminant convolutional feature learning method is more intelligent, simple in model and less in connection parameters so that stability and practicality of an intelligent feature extraction method can be enhanced.

Description

technical field [0001] The invention belongs to the field of induction motor fault diagnosis, in particular to an induction motor fault diagnosis method and system based on a convolutional neural network model in deep learning. Background technique [0002] With the rapid development of science and technology and modern industry, modern machinery and equipment are increasingly large-scale, high-speed, integrated and automated, which provides a strong guarantee for the rapid development of my country's economy. However, catastrophic accidents caused by mechanical equipment failures occur frequently. If the abnormal state of the mechanical system can be detected in time and accurately, it will be of great significance to the safe operation of the mechanical system and avoid major catastrophic accidents. With the continuous improvement of signal processing technology, a variety of signal processing methods have been introduced into mechanical fault diagnosis and become mainstre...

AI Technical Summary

Problems solved by technology

This unsupervised learning method improves the shortcomings of traditional CNN model-based training to a certain extent, but it does not add category labels for guidance during the training process, so the effect of unsupervised CNN is generally not as good as supervised

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