ON-SITE FAULT DIAGNOSIS FOR MECHANICAL EQUIPMENT BASED ON COMPRESSED SENSING OF MULTISOURCE DATA

被引：0

作者：

You X. ^{[1
]}

Yuan H. ^{[2
]}

Guo Y. ^{[3
]}

You X. ^{[1
]}

机构：

[1] College of Mechanical and Vehicle Engineering, Chongqing University

[2] College of Electrical Engineering, Henan University of Technology

[3] International Education College, Henan University of Science and Technology

来源：

International Journal of Mechatronics and Applied Mechanics | 2023年 / 2023卷 / 13期

关键词：

Compressed sensing; Fault diagnosis; Information fusion; Multisource data; Sparse representation;

D O I：

10.17683/ijomam/issue13.3

中图分类号：

学科分类号：

摘要：

With the development of the Internet of Things (IoT), on-site fault diagnosis of multisource sensor data is becoming more and more important. Thus, for on-site fault diagnosis implemented on edge computing platform, efficient multisource data fusion and low-cost computation is essential for fault diagnosis. In this study, a fault diagnosis scheme based on multisource sensor data com-pression is proposed, and its advantages include high data compression & fusion efficiency, low computational cost, and fast online training. The method includes reference matrix construction, compression and fusion, sparse vectors calculation, testing sample reconstruction & quality evaluation. First, a reference matrix is constructed with labelled multisource sensor data. Then, the reference matrix is compressed using a measurement matrix, meanwhile, the multisource data samples are fused. Later, the testing sample is sparsely represented based on batch matching pursuit algorithm, and outputs a sparse vector. Finally, based on reconstruction quality evaluation, the pattern of the testing sample is determined. Two cases are employed to validate the effective-ness of the proposed approach, including landfill gas power generator maintenance pattern recognition and multiple redundancy aileron actuator fault diagnosis, and the accuracy is 96.13% and 97.50%, respectively. © 2023, Cefin Publishing House. All rights reserved.

引用

页码：20 / 30

页数：10

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