A deep network feature identification method for intelligent rotating machinery faults

Abstract

The invention discloses an intelligent rotating machinery fault depth network feature identification method. By arranging a vibration sensor at the part of the rotating machinery to be detected in the train rolling bearing, the original vibration sequence of the rolling bearing is collected, and then the original vibration sequence is decomposed by a singular spectrum analysis method. Reconstruct and extract the root mean square value, standard deviation, skewness index and peak value of the reconstructed vibration sequence, use the rotating machinery fault location diagnosis model trained by the support vector machine to judge the fault location, and then assemble the reconstructed vibration sequence Empirical mode decomposition calculates the permutation entropy values ​​of a set of decomposed intrinsic modal components, uses the permutation and combination of permutation entropy values ​​as detection features, and uses the rotating machinery fault type diagnosis model trained by support vector machine to judge the fault type. The invention can detect the fault position and fault type of the rotary machine in time, and improves the accuracy and reliability of fault diagnosis.

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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