ON-SITE FAULT DIAGNOSIS FOR MECHANICAL EQUIPMENT BASED ON COMPRESSED SENSING OF MULTISOURCE DATA
International Journal of Mechatronics and Applied Mechanics · 2023
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.