Motor fault diagnosis method for deep learning network of data fusion

Abstract

The invention provides a motor fault diagnosis method for deep learning network of data fusion. A neural network includes a data compression network, a feature extraction network and a classificationnetwork. A determination and training methods comprises the steps of (1) collecting current signals of A and B phases of a motor and a vibration signal of a motor end bearing, performing data standardization, obtaining a spectrum sequence by Hilbert-Huang transform, and establishing a data set of the neural network, (2) establishing a deep neural network, determining a network structure and initializing parameters, (3) inputting a training set into the neural network, calculating loss functions of different neural networks, and updating neural network parameters by using a loss value, and (4)inputting the data of a test set into a neural network, calculating an accurate rate, and repeating the step (3) until the accurate rate satisfies a requirement, wherein the neural network can map inputted current and vibration data to a feature plane after training, and a classification network can predict whether the motor is failed according to a fault state corresponding to a region where thenetwork is located.

Description

technical field [0001] The invention relates to the technical field of fault diagnosis of electrical equipment, in particular to a motor fault diagnosis method of a deep learning network for data fusion. By changing the structure of the deep learning neural network, an automatic encoder is used for data fusion to realize the use of current and vibration data. Realize comprehensive judgment of motor failure. Background technique [0002] Motor is the most commonly used driving equipment in modern industry, production and life, and its application range is very wide. In the case of long-term service in industrial production, the motor may have failures such as bearing fatigue wear, short circuit between stator turns, and broken rotor bars. Through motor status monitoring and fault diagnosis, it can be repaired in time to ensure the reliable operation of the motor. [0003] With the development of deep learning and the improvement of hardware computing speed in recent years, ...

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Problems solved by technology

However, since the neural network usually adopts a multi-layer fully c

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