Adaptive extraction and diagnosis method for degree features of mechanical fault through stack-type sparse automatic coding depth neural network

Abstract

The invention relates to an adaptive extraction and diagnosis method for degree features of a mechanical fault through a stack-type sparse automatic coding depth neural network, and belongs to the technical field of mechanical equipment state monitoring and reliability evaluation. The method aims at a problem of intelligent diagnosis of the degree of the mechanical fault, and comprises the steps: carrying out the stacking of sparse automatic coding, adding a classification layer, and constructing the stack-type sparse automatic coding depth neural network which integrates the adaptive learning and extraction of the degree features of the fault and fault recognition; employing the advantage that the sparse automatic coding can automatically learn the internal features of data, and adding noise coding to be integrated in the sparse automatic coding for improving the robustness of feature learning; carrying out the layer-by-layer no-supervision adaptive learning and supervision fine tuning of the original input complex data through multilayer sparse automatic coding, completing the automatic extraction and expression of the degree features of the mechanical fault and achieving the intelligent diagnosis of the degree of the fault. The method is used for the diagnosis of the degree of faults of rolling bearings under different work conditions, and obtains a good effect of feature extraction and diagnosis.

Description

technical field [0001] The invention belongs to the technical field of state monitoring and reliability evaluation of mechanical equipment, and relates to a method for adaptively extracting and diagnosing mechanical fault degree features of a stacked sparse automatic coding deep neural network. Background technique [0002] At present, the fault diagnosis of mechanical equipment is mostly focused on the fault classification research, which often only judges whether a fault occurs and the type of fault. It is difficult to carry out preventive maintenance on mechanical equipment in engineering. Only by accurately assessing and diagnosing the degree of failure can we effectively guide the maintenance of mechanical equipment, ensure the good operation of the equipment, prevent production accidents, and improve economic benefits. Therefore, it is particularly important to diagnose the fault degree of mechanical equipment. [0003] At present, the evaluation and diagnosis of faul...

AI Technical Summary

Problems solved by technology

Self-encoding only includes the input layer, hidden layer and output layer. It is a shallow network with limited learning ability. Especially in the case of high sample complexity, the feature learning effect is not good.

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