Gear Performance Degradation Evaluation Method Based on AR Model and Dictionary Learning

Abstract

The invention discloses a gear performance degradation evaluation method based on AR model and dictionary learning. First, use the autoregressive model (AR) to extract the characteristics of the non-faulty samples and the samples to be tested, and use the AR model coefficients of the non-faulty samples to construct an overcomplete dictionary model, and then input the AR model coefficients of the samples to be tested as feature vectors into the dictionary model Get the reconstructed AR model coefficients in . Finally, the autoregressive models are respectively constructed from the original AR model coefficients and the reconstructed AR model coefficients, and the time series modeling of the signal to be tested is completed respectively, and the root mean square error of the residual sequence obtained by the two autoregressive models is used as the performance degradation index The adaptive early warning threshold is set, and the experimental analysis shows that the evaluation index obtained by the performance degradation method proposed in this paper can monitor the performance degradation trend of the gear in real time and can detect the early failure of the gear in time.

Description

technical field [0001] The invention relates to a gear performance degradation evaluation method based on AR model and dictionary learning, and belongs to the technical field of mechanical product quality reliability evaluation and fault diagnosis. Background technique [0002] As one of the key components of rotating machinery, the gear's performance directly determines the performance of the equipment. Once it breaks down, it will directly affect the normal and safe operation of mechanical equipment and even cause major safety accidents. Therefore, how to realize in-service condition monitoring and performance degradation assessment of gears is of great significance. Reducing downtime costs and achieving near-zero downtime is the ultimate goal of pre-diagnostics. However, without an accurate prediction of the remaining service life before a failure actually occurs, it is impossible to realize the full benefits of pre-diagnostics. Inaccurate forecast information may lead...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to provide a gear performance degradation evaluation method based on AR model and dictionary learning, which can solve the problems of complex model and easy premature saturation in the probabilistic similarity measurement evaluation method, so as to carry out online state monitoring of gears and realize Maintenance and prevention of major accidents

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