Device life prediction method based on multiple long/short-term memory network and experience bayesian

Abstract

The invention discloses a device life prediction method based on multiple long/short-term memory network and experience bayesian, which belongs to the technical field of mechanical equipment state monitoring and life prediction. The method comprises the following steps: 1) a plurality of monitoring signals of the mechanical equipment are obtained, and the collected monitoring signals are preprocessed; 2) the monitoring signals are screened, and signals which can well reflect the degradation process of the mechanical equipment can be selected; 3) a plurality of long-term memory network models are constructed to realize multiple network synchronous training; 4) the signals collected by the sensors in real time are input into the multiple-length short-term memory network model, a plurality ofnetworks are subjected to parallel prediction, and a prediction result is obtained; 5) the probability distribution of the prediction result is estimated through an empirical bayesian algorithm, andthe most probable service life of the equipment is deduced. According to the invention, the remaining service life of the mechanical equipment can be accurately predicted in real time, so that the faults of the mechanical equipment can be sensed in advance, the safety, stability and long-period running of the mechanical equipment can be guaranteed.

Description

technical field [0001] The invention belongs to the technical field of mechanical equipment state monitoring and life prediction, and more specifically relates to a method for equipment life prediction based on multiple long-short-term memory networks and empirical Bayesian. Background technique [0002] With the development of the manufacturing industry in the direction of automation, networking, greening, and intelligence, the structure of mechanical equipment is becoming more and more complex, and the relationship between the components is getting higher and higher, and the functions are complex. Once it fails, it will be difficult to diagnose the cause of the failure in a timely and accurate manner. In addition, the operating environment of mechanical equipment is complex and the working conditions are changeable, and its component units may fail to varying degrees, resulting in damage to mechanical equipment and even major safety accidents. Therefore, it is necessary t...

AI Technical Summary

Problems solved by technology

However, most of the existing literature expresses the use of a single network model to predict, ignoring the diversity of sample performance caused by the slight changes i

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