复杂工况下的直升机行星传动轮系故障诊断

doi:10.7527/S1000-6893.2021.25480

Abstract

Abstract: The planetary transmission system as the core component of the helicopter power transmission system is an important monitoring object for the helicopter health and use monitoring system. In view of the problem of fault diagnosis of the helicopter planetary transmission wheel system under complex working conditions, a method for fault diagnosis of the adaptive domain against deep migration combined with domain adversity and deep coding networks is proposed. Inputting normalized spectrum data and establishing a training load and test load coding network based on the stack shrink automatic encoding network, this method extracts high-quality in-depth fault characteristics from the training domain data with supervised learning, and the network from the test domain data by unsupervised self-adaptively optimized test domain in-depth fault characteristics combining parameter migration and the anti-learning strategy to adapt to the differences in sample data distribution caused by changes in load conditions and the impact of sample data fluctuations caused by harsh noisy environments. The validity and robustness of the method are verified by comparison analysis through fault injection tests on the experimental platform of the helicopter planetary transmission wheel system.

Key words: helicopters, planetary gear transmission, variable conditions, fault diagnosis, deep learning, domain-adversarial transfer

CLC Number:

V240

SUN Canfei, HUANG Linran, SHEN Yong. Fault diagnosis of helicopter planetary gear transmission system under complex operating conditions[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(8): 625480-625480.

References

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Fault diagnosis of helicopter planetary gear transmission system under complex operating conditions

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