TY - JOUR
T1 - Fault diagnosis using redundant data in analog circuits via slime module algorithm for support vector machine
AU - Yu, Danlu
AU - Zhang, Aihua
AU - Gao, Zhiwei
N1 - Funding information: This work is supported partially by National Natural Science Foundation of China (Project no. 61673074), Natural Science Foundation of Liaoning, China (Project no. 2019MS008), Education Committee Project of Liaoning, China (Project no. LJKZ1011,LJ2019003).
PY - 2023/10/1
Y1 - 2023/10/1
N2 - A large amount of redundant data brings challenges for fault diagnosis to achieve satisfactory performance. Therefore, it is particularly important to identify the fault quickly and accurately. In order to solve this problem, this paper applies a novel optimization algorithm, which is slime mould algorithm (SMA) combined with support vector machine (SVM), for fault diagnosis. Firstly, the experimental circuit is analyzed by Monte Carlo to obtain the voltage signals of different fault states. Then the collected voltage signal is subjected to wavelet packet transformation to extract the feature set of the data, and Principal Component Analysis (PCA) is used to reduce the dimension to eliminate redundant data. Finally, the SMA-SVM classifier is used for fault diagnosis and the results are analyzed. Two circuits are chosen as fault circuits, in order of complexity, the four-opamp second-order high-pass filter circuit and the Leapfrog filter circuit. Since SMA optimizes SVM to improve the performance of the classifier, the paper compares SMA with grid search method, particle swarm Optimization (PSO), genetic algorithm (GA), simulated annealing algorithm (SA), and ant colony algorithm (ACA) in terms of the results of the optimized parameters, the time of fault classification and the accuracy of diagnosis after optimizing the classifier. The results show that the SMA-SVM classifier not only demonstrates the advantages of SMA's excellent merit seeking ability and fast convergence, but also has better robustness.
AB - A large amount of redundant data brings challenges for fault diagnosis to achieve satisfactory performance. Therefore, it is particularly important to identify the fault quickly and accurately. In order to solve this problem, this paper applies a novel optimization algorithm, which is slime mould algorithm (SMA) combined with support vector machine (SVM), for fault diagnosis. Firstly, the experimental circuit is analyzed by Monte Carlo to obtain the voltage signals of different fault states. Then the collected voltage signal is subjected to wavelet packet transformation to extract the feature set of the data, and Principal Component Analysis (PCA) is used to reduce the dimension to eliminate redundant data. Finally, the SMA-SVM classifier is used for fault diagnosis and the results are analyzed. Two circuits are chosen as fault circuits, in order of complexity, the four-opamp second-order high-pass filter circuit and the Leapfrog filter circuit. Since SMA optimizes SVM to improve the performance of the classifier, the paper compares SMA with grid search method, particle swarm Optimization (PSO), genetic algorithm (GA), simulated annealing algorithm (SA), and ant colony algorithm (ACA) in terms of the results of the optimized parameters, the time of fault classification and the accuracy of diagnosis after optimizing the classifier. The results show that the SMA-SVM classifier not only demonstrates the advantages of SMA's excellent merit seeking ability and fast convergence, but also has better robustness.
KW - Fault diagnosis
KW - Principal component analysis
KW - Redundant data
KW - Slime mould algorithm
KW - Support vector machine
UR - http://www.scopus.com/inward/record.url?scp=85166965725&partnerID=8YFLogxK
U2 - 10.1007/s12652-023-04664-z
DO - 10.1007/s12652-023-04664-z
M3 - Article
AN - SCOPUS:85166965725
SN - 1868-5137
VL - 14
SP - 14261
EP - 14276
JO - Journal of Ambient Intelligence and Humanized Computing
JF - Journal of Ambient Intelligence and Humanized Computing
IS - 10
ER -