Intelligent fault diagnosis method for rotating machine at time-varying rotating speed

Abstract

The invention discloses an intelligent fault diagnosis method for a rotating machine at a time-varying rotating speed. Angular domain resampling is carried out on a time-domain vibration signal of a rolling bearing and a non-stable time-domain signal is converted into a stable angular domain signal, to so that the impact on the analysis of the vibration signal from the change of the rotating speedis eliminated; gabor transformation capable of well describing transient characteristics of drastically changing signals is used for a rotating speed estimation method based on time-frequency spectrum ridge line fitting; a keyless phase order tracking method without installing a rotating speed sensor is suitable for occasions where the rotating speed sensor cannot be installed. An LSTM model capable of adaptively extracting time sequence features without expert experience and domain knowledge is adopted, and due to the existence of a BN generalization layer, convergence of the model can be accelerated, over-fitting is prevented, the generalization ability of the model is improved, and adaptive intelligent fault diagnosis and identification of the rotating machine under the variable-rotating-speed working condition are realized.

Description

technical field [0001] The invention relates to a mechanical fault diagnosis method, in particular to an intelligent fault diagnosis method for rotating machinery under time-varying rotating speed conditions lacking expert experience and prior domain knowledge, and belongs to the technical field of mechanical fault detection and diagnosis. Background technique [0002] In the current research on fault diagnosis of rotating machinery, machine learning methods are mainly used for intelligent adaptive diagnosis. In recent years, the deep learning method has been widely used with stronger fault classification performance, but the existing intelligent adaptive diagnosis method It has a good application mainly in the fault diagnosis of rotating machinery under constant working conditions, but there are few related studies on the fault diagnosis of rotating machinery under variable speed conditions, especially under the condition of irregular speed and time-varying, the deep learnin...

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Problems solved by technology

[0002] In the current research on fault diagnosis of rotating machinery, machine learning methods are mainly used for intelligent adaptive diagnosis. In recent years, the deep learning method has been widely used with stronger fault classification performance, but the existing intelligent adaptive diagnosis method It has a good application mainly in the fault diagnosis of rotating machinery under constant working conditions, but there are few related studies on the fault diagnosis of rotating machinery under variable speed conditions, especially under the condition of irregular speed and time-varying, the deep learning method cannot solve the problem. Troubleshooting Problems

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