Stacked noise reduction self-coding motor fault diagnosis method based on vibration and current signals

Abstract

The invention discloses a stacked noise reduction self-coding motor fault diagnosis method based on vibration and current signals, and the method comprises the following steps: 1, obtaining the time domain signals of the vibration and current of the motor during different faults, carrying out the preprocessing, and taking the processed signals as network input; 2, determining network parameters; 3, carrying out the layer by layer training, taking a hiding layer of an AE (Auto encoder) at an upper level as the input layer of an AE at a lower level, thereby obtaining a final feature code which is used for training a Softmax network; 4, carrying out the fine tuning of the whole network, judging whether the expected precision is met or not: finishing the training of the network if the expectedprecision is met, or else adjusting the network parameters, and repeatedly carrying out the step 3; 5, finishing the network construction. According to the invention, the multilayer SDAE network is constructed, and the vibration frequency domain signal and the current time domain signal are combined as the input. The SDAE network and a classifier are sequentially trained, and the supervised finetuning of the whole network is carried, thereby achieving the precise diagnosis of the fault of the motor.

Description

technical field [0001] The invention belongs to the technical field of fault diagnosis of motors in industrial production, and in particular relates to a fault diagnosis method for motor vibration signals of multi-source signals (vibration signals and current signals) and stacked noise-reduction self-encoding. Background technique [0002] Asynchronous motors are more and more widely used in the production system of contemporary society, and are the main driving equipment for industrial production activities. Once a failure occurs, it will bring huge economic losses. Asynchronous motor is a comprehensive electrical equipment composed of stator, rotor, bearing, frame and fan, etc. It contains multiple complex subsystems inside, which makes the motor faults appear diverse, and its characteristics are also very different; and the same symptom has It may be caused by different reasons, and the characteristics of the same fault are also different. There is no one-to-one correspo...

AI Technical Summary

Problems solved by technology

[0006] In order to solve the problems of asynchronous motor fault diagnosis difficulties caused by factors such as complex motor structure, non-stationary vibration signal, and large mechanical data, this invention introduces deep learning theory and proposes a motor fault diagnosis method based on stacked noise reduction autoencoder network

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