State recognition and prediction method for spindle characteristic test bench based on deep learning

Abstract

The invention relates to a state recognition and prediction method for a spindle characteristic test bench based on deep learning, which comprises the steps of collecting vibration signals in the operating process of the spindle characteristic test bench, performing normalization processing on the vibration signals, performing noise reduction processing on the normalized vibration signals by adopting EEMD (Ensemble Empirical Mode Decomposition) to obtain IMF components, and reconstructing the obtained IMF components to form restored signals; enabling the restored signals to serve as input samples of a CNN, performing feature extraction on the restored signals to obtain feature vectors, carrying out CNN feature learning on the feature vectors to obtain training feature samples; coding timeinformation for the training feature samples through a multi-layer LSTM (Long Short Term Memory), carrying out classification through Softmax logistic regression to obtain prediction feature samples,and realizing prediction for the operating state; and performing Softmax logistic regression through the training feature samples and the prediction feature samples, carrying out classification on a logistic regression layer so as to judge the fault type of a rotor rotation test bench system, and realizing state recognition. The state recognition and prediction method has fast response performanceand tracking performance.

Description

technical field [0001] The invention relates to a method for diagnosing and predicting the operating state of mechanical equipment, in particular to a method for recognizing and predicting the state of a spindle characteristic test bench based on deep learning. Background technique [0002] The diagnosis and prediction of the operating status of mechanical equipment is of great significance to the production efficiency of modern enterprises, which is conducive to the condition based maintenance (CBM) and service life prediction (Remaining Useful Life, RUL) of the equipment, and can Significantly improve the efficiency of equipment use. The monitoring of the mechanical system includes building the mathematical model of the system and based on signal acquisition and analysis. Due to the complexity of the actual industrial environment and noise pollution, it is very difficult to establish an accurate mathematical model of the mechanical system; As well as the continuous develo...

AI Technical Summary

Problems solved by technology

It has a high recognition rate and prediction accuracy for small sample data sets, but the data information contained in small samples may be biased or insufficient

Images