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Research on feature selection for rotating machinery based on Supervision Kernel Entropy Component Analysis with Whale Optimization Algorithm
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DOI：10.1016/j.asoc.2020.106245.png)
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Abstract
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Aimed at finding a scientific and effective method to diagnose faults in rotating machinery, the algorithm based on Supervision Kernel Entropy Component Analysis with Whale Optimization Algorithm (WOSKECA) for feature selection has been proposed in this paper. Firstly, for ensure sufficient information gathering from the rotating machinery, multi-features parameters of the time domain, frequency domain, time-frequency domain and entropy domain are extracted, and a high-dimensional feature matrix is constructed from these features. Secondly, the WOSKECA for feature selection is applied to eliminate any redundant information. The algorithm takes the class information as the supervised information to improve the recognition accuracy of Kernel Entropy Component Analysis (KECA) and can extract low-dimensional features with discriminative ability from the high-dimensional feature space. Meanwhile, the Whale Optimization Algorithm (WOA) as a new meta-heuristic optimization algorithm is applied to optimize the kernel parameters in KECA, which reduces the interference of subjective factors and reduces the professionalism of obtaining fault information. Finally, Support Vector Machine based on the Particle Swarm Optimization (PSOSVM) is used to classify the fault type as well as assess the severity of the faults. The feature extraction algorithm is entirely evaluated through experimentation and comparative. The results show that the proposed method is able to detect and classify the faults of rotating machinery more successfully and more accurately than traditional manifold learning. (C) 2020 Elsevier B.V. All rights reserved.
Keywords:
Multi-features
WOSKECA
PSOSVM
Fault diagnosis
Feature selection
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