Method for recognizing early fault of bearing based on long and short-term memory recurrent neural network

Abstract

The invention discloses a method for recognizing an early fault of a bearing based on a long and short-term memory recurrent neural network, which comprises the steps of collecting full life vibrationsignals of the bearing, and then extracting common time domain characteristics; constructing waveform entropy characteristics, and verifying the validity of the waveform entropy according to a squaredemodulation method; building a characteristic data set by using the time domain characteristics and the entropy characteristics, and selecting a normal data set and a deep fault data set; taking thenormal data set and the deep fault data set to serve as training samples to train the LSTM (Long and Short-Term Memory) recurrent neural network; and performing time domain characteristic and entropycharacteristic extraction on an online bearing vibration signal, and then inputting the online bearing vibration signal into the trained LSTM recurrent neural network so as to recognize the fault occurrence time. The traditional characteristics and the entropy characteristics of the vibration signals are combined, so that the current state of the bearing is accurately reflected under the condition of ensuring the physical meaning of the vibration characteristics. The adopted recurrent neural network can effectively apply the degraded historical data so as to perform effective recognition on the fault occurrence time of the bearing.

Description

technical field [0001] The invention belongs to the field of intelligent fault diagnosis, in particular to a bearing early fault identification method based on a long-short-term memory cyclic neural network. Background technique [0002] As an important part of rotating machinery, bearings need to be effectively monitored for their health status. Fault diagnosis is an important research content in the field of bearing fault intelligent diagnosis. Constructing an effective degradation index and using the historical data of fault development to judge the fault state is the key to this work. The vibration signal generated by the bearing after defect damage is an important basis for bearing fault diagnosis, and the time-frequency domain characteristics of the vibration signal are also an important means of traditional fault diagnosis methods. [0003] For the data-driven intelligent diagnosis method, on the one hand, compared with the traditional time-frequency domain features...

AI Technical Summary

Problems solved by technology

The current bearing degradation evaluation research still has the following problems: lack of characteristic quantities that can fully reflect the bearing degradation trend; insufficient utilization of historical signals of the degradation process

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