Abstract
The functional time series signals generated during the operation of electromechanical systems contain fault characteristic information. This study proposes a fault identification method for electromechanical systems based on functional data feature engineering and multi-layer kernel extreme learning machine (MLKELM) optimized by sparrow search algorithm (SSA). First, multiple time series signals under different fault conditions are functionalized under the B-spline basis function system, and the feature reduction space is constructed by functional principal component analysis (FPCA) and principal differential analysis (PDA) to extract fault features. Second, the minimum redundancy and maximum relevance (mRMR) method is performed on the initial feature set for feature selection. In addition, the size of the optimal feature subset is determined by the class separability of feature subset (CSFS) criterion. Finally, deep feature learning and fault identification are implemented by MLKELM and the pre-defined parameters are optimized based on the SSA in this process to improve its performance. The experimental results show that the proposed method can effectively extract the fault features of function time series signals, and then accurately identify the faults of electromechanical systems.
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This research is supported by the National Natural Science Foundation of China (Grant No. 52105022).
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Hao Wen received his B.S. in 2017 from Nan**g University of Science and Technology, Nan**g, China. Now he is pursuing a Ph.D. there. His main research interests include action reliability and fault diagnosis of electromechanical system.
Baolin Hou is a Professor at Nan**g University of Science and Technology, Nan**g, China. He received his Ph.D. in Mechanical Engineering from Nan**g University of Science and Technology in 2003. His research interests include dynamic analysis, robust control, reliability and fault diagnosis of electromechanical system.
**n ** is a lecturer at Nan**g University of Science and Technology, Nan**g, China. He received his Ph.D. in Mechanical Engineering from Nan**g University of Science and Technology in 2016. His research interests include data analysis, bionic, machine learning, and control.
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Wen, H., Hou, B. & **, X. Fault identification of a chain conveyor based on functional data feature engineering and optimized multi-layer kernel extreme learning machine. J Mech Sci Technol 37, 2289–2300 (2023). https://doi.org/10.1007/s12206-023-0405-x
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DOI: https://doi.org/10.1007/s12206-023-0405-x