A method for predicting the remaining life of gearbox bearings

Abstract

The invention discloses a method for predicting the remaining life of a gear box bearing. An acceleration sensor is used to collect multi-channel vibration signals of a gear box bearing, and a current clamp is used to obtain a multi-channel stator current signal from an output end of a generator to obtain an original multi-channel vibration signal. and multi-channel stator current signals, and perform data preprocessing; design a convolutional network spatial feature extraction module to extract the spatial features of vibration signals and current signals, and splice them in the channel dimension; design a dynamic weighted fusion layer to fuse vibration signals and the spatial characteristics of the current signal; then the time series features are extracted from the fused time series feature vector sequence through the two-way long short-term memory network, and finally the remaining life of the bearing is predicted through the regression layer. The invention can self-adaptively learn and dynamically fuse the time-space correlation feature information between the vibration and the current, thereby improving the degraded feature extraction ability and life prediction accuracy.

Description

technical field [0001] The invention relates to the technical field of remaining life prediction of gearbox bearings of wind turbines, in particular to a method for predicting the remaining life of gearbox bearings. Background technique [0002] Gearbox is an important part of large-scale doubly-fed wind turbine, and it is the key to realize energy transmission and conversion. In practice, it is not only one of the key systems for the safe operation of wind turbines, but also one of the main sources of high-frequency failures of wind turbines. The internal structure of the gearbox is complex, and under complex working conditions such as low speed, heavy load, alternating load and strong gust impact for a long time, key components such as bearings are prone to wear and performance degradation, and even evolve into serious failures until failure. Dealing with the failure of key components such as bearings may trigger a chain reaction, leading to the shutdown of the entire sys...

AI Technical Summary

Problems solved by technology

[0004] Most of the existing studies only focus on the single-channel signal of a single sensor to extract the degradation features of components, without considering that the degradation of components is the result of the coupling of various parts. for a comprehensive information space

The current bearing life prediction research based on multi-channel signals only performs feature extraction and degradation state modeling analysis based on a single vibration, without considering other types of sensors such as current signals. However, only relying on a single type of sensor signal is not enough to accurately describe Potential degradation mechanisms of the system, leading to inaccurate lifetime predictions

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