Efficient gear fault feature selection based on moth‑flame optimisation in discrete wavelet packet analysis domain

Ong, Pauline and Tieh, Tony Hieng Cai and Lai, Kee Huong and Lee, Woon Kiow and Ismon, Maznan (2019) Efficient gear fault feature selection based on moth‑flame optimisation in discrete wavelet packet analysis domain. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 41. pp. 1-14. ISSN 1678-5878

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Abstract

Rotating machinery—a crucial component in modern industry, requires vigilant monitoring such that any potential malfunction of its electromechanical systems can be detected prior to a fatal breakdown. However, identifying faulty signals from a defective rotating machinery is challenging due to complex dynamical behaviour. Therefore, the search for features which best describe the characteristic of different fault conditions is often crucial for condition monitoring of rotating machinery. For this purpose, this study used the intensification and diversification properties of the recently proposed moth-flame optimisation (MFO) algorithm and utilised the algorithm in the proposed feature selection scheme. The proposed method consisted of three parts. First, the vibration signals of gear with different fault conditions were decomposed by a fourth-level discrete wavelet packet transform, and the statistical features at all constructed nodes were derived. Second, the MFO algorithm was utilised to select the optimal discriminative features. Lastly, the MFO-selected features were used as the input for a support vector machine (SVM) diagnostic model to identify fault patterns. To further demonstrate the superiority of the proposed method, other feature selection approaches were applied, including randomly selected features and complete features, and other diagnostic models, namely the multilayer perceptron neural network and k-nearest neighbour. Comparative experiments demonstrated that SVM with the MFO-selected features outperformed the others, with the classification accuracy of 99.60%, thus validating its effectiveness.

Item Type:	Article
Uncontrolled Keywords:	Discrete wavelet packet transform; Fault diagnosis; Feature extraction; Moth-flame optimisation; Rotary machinery; Support vector machine
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA76.75-76.765 Computer software
Divisions:	Faculty of Mechanical and Manufacturing Engineering > Department of Mechanical Engineering
Depositing User:	Miss Afiqah Faiqah Mohd Hafiz
Date Deposited:	18 Oct 2021 08:10
Last Modified:	18 Oct 2021 08:12
URI:	http://eprints.uthm.edu.my/id/eprint/2301

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