Non-supervision feature extraction method based on self-coding neural network

Abstract

The invention provides a non-supervision feature extraction method based on a self-coding neural network. According to the method, firstly, training data matrix building is performed; then, each component value of a training data matrix is normalized to a position between [0,1]; next, parameter study is performed to obtain a self-coding neural network model; then, the output of a hidden layer is calculated; features are obtained; finally, the number of optimum hidden layer nerve cells is determined according to a halving value taking method; finally, a structure of the self-coding neural network is determined. In the network training study, the expected output of the self-coding neural network is specified to be equal to the input of the network; through such a study target, the providing of the expected network output by training data is not needed in the training process of the self-coding network training process. The method provided by the invention has the advantages that under the condition of being lack of priori knowledge, an internal rule of the equipment mass state data can be excavated and features can be extracted.

Description

technical field [0001] The invention relates to the technical field of mechanical fault diagnosis, in particular to an unsupervised feature extraction method based on an autoencoding neural network. Background technique [0002] Mechanical fault diagnosis is playing an increasingly important role in industrial production. Fault diagnosis is to grasp the operating status of the equipment during the operation of the equipment or basically without disassembling the equipment, and carry out the operation based on the useful information obtained from the test of the diagnosed object. Analysis and processing, judging whether the state of the diagnosed object is in an abnormal state or a fault state, judging the parts or parts where the deterioration state occurs, and determining the cause of the fault, and predicting the development trend of state deterioration, etc. Its purpose is to improve equipment efficiency and operational reliability, prevent problems before they happen, an...

AI Technical Summary

Problems solved by technology

However, for massive mechanical state signals, relevant prior knowledge is often difficult or even impossible to obtain

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