Rotating machinery reliability assessment based on improved extreme learning machine and hippopotamus optimization algorithm

• Autorzy: Isham Muhammad Firdaus Bin , Kamal Muhammad Harith Mohd , Raheimi Amirulaminnur , Saufi Mohd Syahril Ramadhan Mohd , Lim Meng Hee , Leong Muhd Salman , Waziralilah Nur Fathiah

Identyfikatory

DOI

10.12913/22998624/195928

• Języki publikacji: EN

Abstrakty

EN

The utilisation of rotating machinery across diverse industrial applications underscores the critical importance of evaluating its reliability to ensure the safe operation of these systems. Bearings, as fundamental components within engineering facilities, hold particular significance; their malfunction can result in severe safety incidents, heightened maintenance expenditures, and considerable economic consequences. Extreme learning machine (ELM) represents a machine learning approach that proficiently addresses numerous challenges inherent in conventional machine learning algorithms. Nonetheless, the efficacy of the ELM may deteriorate and yield inaccurate results due to an inappropriate use of its parameters, which include input weights, biases, and the number of hidden neurons. This paper proposes an improved ELM (IELM) model that incorporates the Hippopotamus optimization algorithm (HOA) to optimise the parameters and enhance the performance of the ELM in rotating machinery reliability assessment. Initially, the HOA method is employed to identify optimised parameter values for the ELM. Subsequently, these optimised values are integrated into the proposed IELM-HOA framework for the purpose of fault classification. This study utilises time-domain statistical features to extract significant information from the vibration signals. The dataset comprises vibration signals derived from both online bearing datasets and experimental bearing datasets. The findings indicate that the proposed IELM-HOA method enhances the performance of the ELM technique. Furthermore, it demonstrates the capability to exceed and compete with recently introduced fault diagnosis methodologies

Słowa kluczowe

EN

reliability assessment; ELM; extreme learning machine; bearing; vibration signal; meta-heuristic

• Wydawca: Lublin University of Technology ; Polish Society of Ecological Engineering (PTIE), Branch of PTIE in Lublin
• Czasopismo: Advances in Science and Technology. Research Journal
• Rocznik: 2025
• Tom: Vol. 19, no. 2
• Strony: 82--94
• Opis fizyczny: Bibliogr. 21 poz., fig., tab.

Twórcy

autor

Isham Muhammad Firdaus Bin

• Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, Johor, 81310, Malaysia

• https://orcid.org/mfirdausisham%40gmail.com

autor

Kamal Muhammad Harith Mohd

• Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, Johor, 81310, Malaysia

• https://orcid.org/0009-0004-9311-6287

autor

Raheimi Amirulaminnur

• Institute of Noise and Vibration, Universiti Teknologi Malaysia, Kuala Lumpur, 54100, Malaysia

• https://orcid.org/0009-0005-6483-1449

autor

Saufi Mohd Syahril Ramadhan Mohd

• Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, Johor, 81310, Malaysia

autor

Lim Meng Hee

• Institute of Noise and Vibration, Universiti Teknologi Malaysia, Kuala Lumpur, 54100, Malaysia

autor

Leong Muhd Salman

• Institute of Noise and Vibration, Universiti Teknologi Malaysia, Kuala Lumpur, 54100, Malaysia

autor

Waziralilah Nur Fathiah

• Mechanical Engineering Department, Alfaisal University, Riyadh, 11533, Saudi Arabia

Bibliografia

• 1. Zhang Y, Shang L, Gao H, He Y, Xu X, Chen Y. A new method for diagnosing motor bearing faults based on Gramian angular field image coding and improved CNN-ELM. IEEE Access 2023; 11: 11337–11349.
• 2. Chen Y, Yuan Z, Chen J, Sun K. A novel fault diagnosis method for rolling bearing based on hierarchical refined composite multiscale fluctuation-based dispersion entropy and PSO-ELM. Entropy. 2022. https://doi.org/10.3390/e24111517.
• 3. Hu Y, Wei R, Yang Y, Li X, Huang Z, Liu Y, He C, Lu H. Performance degradation prediction using LSTM with optimized parameters. Sensors. 2022. https://doi.org/10.3390/s22062407.
• 4. Meng Z, Zhang Y, Zhu B, Pan Z, Cui L, Li J, Fan F. Research on rolling bearing fault diagnosis method based on ARMA and optimized MOMEDA. Measurement (Lond). 2022. https://doi.org/10.1016/j.measurement.2021.110465.
• 5. Wei H, Zhang Q, Shang M, Gu Y. Extreme learning Machine-based classifier for fault diagnosis of rotating Machinery using a residual network and continuous wavelet transform. Measurement (Lond). 2021. https://doi.org/10.1016/j.measurement.2021.109864.
• 6. Qiu Z, Yuan X, Wang D, Fan S, Wang Q. Physical model driven fault diagnosis method for shield Machine hydraulic system. Measurement (Lond). 2023. https://doi.org/10.1016/j.measurement.2023.113436.
• 7. Wang S, Tian J, Liang P, Xu X, Yu Z, Liu S, Zhang D. Single and simultaneous fault diagnosis of gearbox via wavelet transform and improved deep residual network under imbalanced data. Eng Appl Artif Intell. 2024. https://doi.org/10.1016/j.engappai.2024.108146.
• 8. Sun C, Wang Y, Sun G. A multi-criteria fusion feature selection algorithm for fault diagnosis of helicopter planetary gear train. Chinese Journal of Aeronautics 2020; 33: 1549–1561.
• 9. Guo J, Li X, Lao Z, Luo Y, Wu J, Zhang S. Fault diagnosis of industrial robot reducer by an extreme learning machine with a level-based learning swarm optimizer. Advances in Mechanical Engineering. 2021. https://doi.org/10.1177/16878140211019540.
• 10. Liu X, Huang H, Xiang J. A personalized diagnosis method to detect faults in gears using numerical simulation and extreme learning machine. Knowl Based Syst 2020; 195: 105653.
• 11. Huang G Bin, Zhu QY, Siew CK. Extreme learning machine: Theory and applications. Neurocomputing 2006; 70: 489–501.
• 12. Deo A, Pandey I, Khan SS, Mandlik A, Doohan NV, Panchal B. Deep learning-based red blood cell classification for sickle cell anemia diagnosis using hybrid CNN-LSTM model. Traitement du Signal 2024; 41: 1293–1301.
• 13. Sheini Dashtgoli D, Taghizadeh S, Macconi L, Concli F. Comparative analysis of machine learning models for predicting the mechanical behavior of bio-based cellular composite sandwich structures. Materials. 2024. https://doi.org/10.3390/ma17143493.
• 14. Yousuf SM, Khan MA, Ibrahim SM, Sharma AK, Ahmad F, Kumar P. A comparison of experimental and computational methods for circular footing response on lime-treated geotextile reinforced silty sand. Model Earth Syst Environ. 2024; 10: 1281–1303.
• 15. Cao L, Sun W. Research on Bearing Fault Identification of Wind Turbines’ Transmission System Based on Wavelet Packet Decomposition and Probabilistic Neural Network. Energies, 2024. https://doi.org/10.3390/en17112581.
• 16. Meng L, Liu M, Wei P, Qin H. Rolling Bearing Fault Diagnosis Based on Improved VMD And GA-ELM. In: Peng C, Sun J (eds) 2021 Proceedings of the 40th Chinese Control Conference (CCC). 2021, 4414–4419.
• 17. Guo L, Qian J. Rolling Bearing Fault Diagnosis Based on QGA Optimized DBN-ELM Model. In: 2023 35TH Chinese control and decision conference, CCDC. 2023; 5466–5473.
• 18. Isham MF, Leong MS, Lim MH, Ahmad ZAB Optimized ELM based on Whale Optimization Algorithm for gearbox diagnosis. MATEC Web Conf. 2019; 255.
• 19. Isham MF, Saufi MSR, Waziralilah NF, Talib MHAb, Hasan MDA, Saad WAA Optimized-ELM Based on Geometric Mean Optimizer for Bearing Fault Diagnosis. In: Mohd. Isa WH, Khairuddin IMohd, Mohd. Razman MohdA, Saruchi S ’Atifah, Teh S-H, Liu P (eds) Intelligent Manufacturing and Mechatronics. Springer Nature Singapore, Singapore, 2024; 125–139.
• 20. Wang J, Zhang Y, Zhang F, Li W, Lv S, Jiang M, Jia L. Accuracy-improved bearing fault diagnosis method based on AVMD theory and AWPSO-ELM model. Measurement 2021; 181: 109666.
• 21. Sun Z, Mu L, Li F, Wei N, Wang Y, Wu S, Lei M, Liu Q. Vibration Fault Diagnosis of Circuit Breaker Based on CGWO-VMD and ELM Combined with PCA. In: 2022 4th International Conference on Smart Power & Internet Energy Systems (SPIES). 2022; 1237–1242.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).