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Abnormal detection method of integrated transmission device condition monitoring data under complex working conditions

Published:2023-10-30 author:NING Zijun, CHEN Tao, XU Feng, et al. Browse: 974 Check PDF documents

Abnormal detection method of integrated transmission device condition

monitoring data under complex working conditions

NING Zijun1, CHEN Tao1, XU Feng3, WANG Liyong1, JIA Ran1

(1.The Ministry of Education Key Laboratory of Modern Measurement and Control Technology, Beijing Information Science and

Technology University, Beijing 100192, China;

2.Unit 32184 of the People's Liberation Army, Beijing 100075, China)

Abstract: Aiming at the problem that it was difficult to effectively distinguish the abnormal data caused by the change of working condition and the fault state of the integrated transmission device during its operation, a method for the abnormal detection of the condition monitoring data of the integrated transmission device under complex working conditions was proposed. Firstly, density-based spatial clustering of applications with noise(DBSCAN)method was used to cluster the associated variables of the state monitoring data to eliminate the interference of non-associated data on the accuracy of data reconstruction. Then, a reconstruction model of the state monitoring data was constructed by deep denoising auto coding network to obtain the deviation features which was sensitive to abnormal data. Finally, support vector data description(SVDD)algorithm was used to construct a hypersphere with deviation characteristics of normal condition monitoring data, and the abnormal detection of the condition monitoring data of the integrated transmission device under complex working conditions was completed. In order to verify the effectiveness of the method for anomaly detection of comprehensive transmission device condition monitoring data, a comprehensive transmission device was taken as the research object, and the anomaly detection was verified and analyzed on several groups of comprehensive transmission device oil leakage experimental data. The experimental results show that the method realizes the purpose of detecting the abnormal condition monitoring data under the condition of different degrees of oil leakage failure of integrated transmission device, and the accuracy is higher than 92%. The results show that the proposed method can effectively detect the early abnormal operating state of the integrated transmission and lay a foundation for the health management and deterioration evaluation of the integrated transmission.

Key words: integrated transmission device; mechanical transmission; anomaly detection; data reconstruction; data association; density-based spatial clustering of applications with noise(DBSCAN); deep denoising auto coding

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