An intelligent rotary machine fault depth network feature identification method

Abstract

The invention discloses an intelligent rotary machine fault depth network feature identification method. A vibration sensor is arranged at a to-be-detected rotating mechanical part of a train rollingbearing; collecting an original vibration sequence when the rolling bearing works; decomposing and reconstructing the original vibration sequence through a singular spectrum analysis method; extracting a root-mean-square value of the reconstructed vibration sequence; standard deviation, skewness index and peak value; a fault position is judged by using a rotary machine fault position diagnosis model obtained by training of a support vector machine; and then, carrying out ensemble empirical mode decomposition on the reconstructed vibration sequence, calculating the permutation entropy values ofa group of decomposed intrinsic mode components, taking the permutation combination of the permutation entropy values as a detection characteristic, and judging the fault type by using a rotary machine fault type diagnosis model obtained by training of a support vector machine. The fault position and the fault type of the rotary machine can be detected more timely, and the fault diagnosis accuracy and reliability are improved.

Description

technical field [0001] The invention relates to the field of fault identification of mechanical systems, in particular to a deep network feature identification method for faults of intelligent rotating machinery. Background technique [0002] With the continuous improvement of high-speed railway technology and the proposal of the intelligent high-speed railway plan, the safety of high-speed railway operation has attracted more and more attention. The composition of high-speed railways is complex, and rotating machinery occupies an important position in it, such as bogie motors, and traction braking devices contain a large number of rotating machinery. However, during the long-term use of rotating machinery, it is extremely prone to various degrees of wear and tear and various failures. If it is not found in time, it will lead to the accumulation of mechanical failures, which may cause economic losses due to late accidents, or cause safety hazards and safety accidents. [00...

AI Technical Summary

Problems solved by technology

However, the abnormal changes of the vibration signal in the early stage of the fault are extremely small, and the conventional diagnostic methods are often ineffective

In the existing diagnostic algorithm, the diagnostic site and diagnostic type are detected at the same time, resulting in high dimensionality of the training samples, and the pre-processing of the original data is relatively simple, and the effect is mediocre, so the final model detection accuracy is not high

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