Abstract
Ensuring smooth machine operation and safety is crucial in most engineering plant, and fault diagnosis plays a critical role in achieving these goals. In recent years, machine learning techniques already being utilized extensively in the research of bearing fault diagnosis. One of the recent methods that has gained popularity is the extreme learning machine (ELM) method. This method offers several advantages, such as fast learning rate, generalization performance efficient, and easy to use. However, it is important to note that the ELM method can result in inaccurate diagnosis, if the values for input weight, hidden layer bias, and number of neurons are not selected properly. This paper introduces a new approach for bearing fault diagnosis, named GMO-ELM, which utilizes the ELM method and the geometric mean optimizer (GMO) to optimize ELM parameters. The proposed method was tested using sets of bearing vibration signal from Case Western Reserve University (CWRU) with four different operating conditions, including healthy baseline, outer race fault signal, inner race fault signal, and ball fault signal. Based on the result, the proposed method is able to provide 12% better performance by comparing to the conventional ELM and competitive diagnosis performance by comparing to other recent diagnosis model.
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References
Saufi SR et al (2019) Challenges and opportunities of deep learning models for machinery fault detection and diagnosis: a review. IEEE Access 7:122644–122662
Li Z et al (2018) Multi-dimensional variational mode decomposition for bearing-crack detection in wind turbines with large driving-speed variations. Renew Energy 116:55–73
Zhang X et al (2018) Bearing fault diagnosis using a whale optimization algorithm-optimized orthogonal matching pursuit with a combined time–frequency atom dictionary. Mech Syst Signal Process 107:29–42
Li J et al (2017) Rolling bearing fault diagnosis based on time-delayed feedback monostable stochastic resonance and adaptive minimum entropy deconvolution. J Sound Vib 401:139–151
Dong W et al (2021) Intelligent fault diagnosis of rolling bearings based on refined composite multi-scale dispersion q-complexity and adaptive whale algorithm-extreme learning machine. Measurement. 176:108977
Isham MF et al (2019) Optimized ELM based on whale optimization algorithm for gearbox diagnosis. MATEC Web Conf 255
Isham MF et al (2019) Intelligent wind turbine gearbox diagnosis using VMDEA and ELM. Wind Energy 22(6)
Isham MF et al (2023) Bearing fault diagnosis using extreme learning machine based on artificial gorilla troops optimizer. Adv Intell Manuf Mechatron 87–103
Bai R et al (2021) Rolling bearing fault diagnosis based on multi-channel convolution neural network and multi-scale clipping fusion data augmentation. Measurement. 184:109885
Zhang T et al (2021) A novel feature adaptive extraction method based on deep learning for bearing fault diagnosis. Measurement 185:110030
Bin Huang G et al (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1–3):489–501
Huang G-B et al (2012) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern Part B Cybern 42(2):513–529
Liang M et al (2018) A novel faults diagnosis method for rolling element bearings based on ELCD and extreme learning machine. Shock Vib (2018)
Wang D et al (2017) A novel hybrid model for air quality index forecasting based on two-phase decomposition technique and modified extreme learning machine. Sci Total Environ 580:719–733
Chen Y et al (2018) Mixed kernel based extreme learning machine for electric load forecasting. Neurocomputing 312:90–106
Benkedjouh T, Rechak S (2016) Intelligent prognostics based on empirical mode decomposition and extreme learning machine. Model Identif Control (ICMIC), 2016 8th Int Conf 943–947
Shariati M et al (2020) A novel hybrid extreme learning machine–grey wolf optimizer (ELM-GWO) model to predict compressive strength of concrete with partial replacements for cement. Eng Comput
Zhou J et al (2019) Forecasting the carbon price using extreme-point symmetric mode decomposition and extreme learning machine optimized by the grey wolf optimizer algorithm. Energies 12(5)
Xiao J et al (2018) Identification of shaft orbit based on the grey wolf optimizer and extreme learning machine. In: 2nd IEEE advanced information management, communicates, electronic and automation control conference (IMCEC), pp 1147–1150
Sales AK et al (2021) Urmia lake water depth modeling using extreme learning machine-improved grey wolf optimizer hybrid algorithm. Theor Appl Climatol 146(1):833–849
Li H et al (2020) Research on multi-domain fault diagnosis of gearbox of wind turbine based on adaptive variational mode decomposition and extreme learning machine algorithms. Energies 13(6)
Yao G et al (2021) A hybrid gearbox fault diagnosis method based on GWO-VMD and DE-KELM. Appl Sci 11(11)
Nayak DR et al (2017) Pathological brain detection using extreme learning machine trained with improved whale optimization algorithm. In: Ninth international conference on advances in pattern recognition (ICAPR), pp 1–6
Sun W, Wang Y (2021) Prediction and analysis of CO2 emissions based on regularized extreme learning machine optimized by adaptive whale optimization algorithm. Pol J Environ Stud 30(3):2755–2767
Rezaei F et al (2023) GMO: geometric mean optimizer for solving engineering problems, vol 0123456789. Springer, Berlin
Youcef Khodja A et al (2020) Rolling element bearing fault diagnosis for rotating machinery using vibration spectrum imaging and convolutional neural networks. Int J Adv Manuf Technol 106(5):1737–1751
Mallikarjuna PB et al (2021) Aircraft gearbox fault diagnosis system: an approach based on deep learning techniques. 30(1):258–272
Ali MZ et al (2020) Single- and multi-fault diagnosis using machine learning for variable frequency drive-fed induction motors. IEEE Trans Ind Appl 56(3):2324–2337
Acknowledgements
The author would like to extend their greatest gratitude to UTM Encouragement Research Grant Scheme (UTM-ER), Q.J130000.3824.31J20.
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Isham, M.F., Saufi, M.S.R., Waziralilah, N.F., Talib, M.H.A., Hasan, M.D.A., Saad, W.A.A. (2024). Optimized-ELM Based on Geometric Mean Optimizer for Bearing Fault Diagnosis. In: Mohd. Isa, W.H., Khairuddin, I.M., Mohd. Razman, M.A., Saruchi, S.'., Teh, SH., Liu, P. (eds) Intelligent Manufacturing and Mechatronics. iM3F 2023. Lecture Notes in Networks and Systems, vol 850. Springer, Singapore. https://doi.org/10.1007/978-981-99-8819-8_11
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