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

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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.

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Acknowledgements

The authors would like to express the deepest appreciation to the Ministry of Education Malaysia, for funding this project through the Fundamental Research Grant Scheme (FRGS – Vot K070). Additional support from Universiti Tun Hussein Onn Malaysia (UTHM) in the form of IGSP Vot U671 is also gratefully acknowledged.

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Authors and Affiliations

  1. Faculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia, 86400, Parit Raja, Batu Pahat, Johor, Malaysia

    Pauline Ong, Tony Hieng Cai Tieh, Woon Kiow Lee & Maznan Ismon

  2. School of Mathematical Sciences, Sunway University, 47500, Petaling Jaya, Selangor, Malaysia

    Kee Huong Lai

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  1. Pauline Ong
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  2. Tony Hieng Cai Tieh
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Correspondence to Pauline Ong.

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Technical Editor: Wallace Moreira Bessa, D.Sc.

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Ong, P., Tieh, T.H.C., Lai, K.H. et al. Efficient gear fault feature selection based on moth-flame optimisation in discrete wavelet packet analysis domain. J Braz. Soc. Mech. Sci. Eng. 41, 266 (2019). https://doi.org/10.1007/s40430-019-1768-x

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