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.
This is a preview of subscription content, log in to check access.
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.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
Selcuk S (2017) Predictive maintenance, its implementation and latest trends. Proc Inst Mech Eng, Part B: J Eng Manuf 231(9):1670–1679CrossRefGoogle Scholar
Wang Z-Y, Lu C, Zhou B (2018) Fault diagnosis for rotary machinery with selective ensemble neural networks. Mech Syst Signal Process 113:112–130CrossRefGoogle Scholar
Chen J, Zhou D, Lyu C, Lu C (2018) An integrated method based on CEEMD-SampEn and the correlation analysis algorithm for the fault diagnosis of a gearbox under different working conditions. Mech Syst Signal Process 113:102–111CrossRefGoogle Scholar
Kohavi R, John GH (1997) Wrappers for feature subset selection. Artif Intell 97(1–2):273–324CrossRefGoogle Scholar
Zainuddin Z, Lai KH, Ong P (2016) An enhanced harmony search based algorithm for feature selection: applications in epileptic seizure detection and prediction. Comput Electr Eng 53:143–162CrossRefGoogle Scholar
Ding C, Peng H (2005) Minimum redundancy feature selection from microarray gene expression data. J Bioinform Comput Biol 3(02):185–205CrossRefGoogle Scholar
Shen C, Wang D, Kong F, Peter WT (2013) Fault diagnosis of rotating machinery based on the statistical parameters of wavelet packet paving and a generic support vector regressive classifier. Measurement 46(4):1551–1564CrossRefGoogle Scholar
Li B, P-l Zhang, Tian H, Mi S-S, Liu D-S, Ren G-Q (2011) A new feature extraction and selection scheme for hybrid fault diagnosis of gearbox. Expert Syst Appl 38(8):10000–10009CrossRefGoogle Scholar
Zhang K, Li Y, Scarf P, Ball A (2011) Feature selection for high-dimensional machinery fault diagnosis data using multiple models and radial basis function networks. Neurocomputing 74(17):2941–2952CrossRefGoogle Scholar
Gonçalves AC, Silva JBC (2011) Predictive maintenance of a reducer with contaminated oil under an excentrical load through vibration and oil analysis. J Br Soc Mech Sci Eng 33(1):1–7CrossRefGoogle Scholar
Gangsar P, Tiwari R (2018) Multifault diagnosis of induction motor at intermediate operating conditions using wavelet packet transform and support vector machine. J Dyn Syst Meas Contr 140(8):081014CrossRefGoogle Scholar
Hsu C-W, Lin C-J (2002) A comparison of methods for multiclass support vector machines. IEEE Trans Neural Netw 13(2):415–425CrossRefGoogle Scholar