Improving Machine Failure Prediction with Grey Wolf, Whale Optimization, and Optuna Techniques

Year 2025, Volume: 12 Issue: 1, 154 - 174, 26.03.2025

Vahid Sinap

https://doi.org/10.54287/gujsa.1544942

Abstract

Machine failure prediction is crucial for minimizing downtime and optimizing maintenance strategies in industrial settings. This study aims to enhance the accuracy of machine failure prediction models by integrating advanced hyperparameter optimization techniques with feature selection methods. Various optimization techniques, including Optuna, Hyperopt, and Spearmint, were evaluated, along with feature selection methods utilizing Grey Wolf Optimization (GWO) and Whale Optimization Algorithm (WOA). The findings reveal that the CatBoost model optimized with GWO and Optuna achieved the highest performance, with an accuracy of 88.3%, an F1 score of 88.3%, and a Matthews Correlation Coefficient (MCC) of 76.7%. In comparison, WOA demonstrated competitive yet slightly lower results, with the best accuracy of 85.9% achieved using CatBoost and Optuna. The study also highlights that Linear Discriminant Analysis (LDA), optimized with Optuna, showed notable performance, with an accuracy of 86.0%, an F1 score of 85.8%, and an MCC of 74.6% without feature selection, which improved to 87.8%, 87.8%, and 76%, respectively, with GWO-based feature selection. The overall results indicate that GWO outperforms WOA in improving model performance, particularly when paired with advanced hyperparameter tuning techniques.

Keywords

Machine Failure Prediction, Grey Wolf Optimization, Whale Optimization Algorithm, Optuna

References

• Abdallah, M., Lee, W. J., Raghunathan, N., Mousoulis, C., Sutherland, J. W., & Bagchi, S. (2021). Anomaly detection through transfer learning in agriculture and manufacturing IoT systems. https://doi.org/10.48550/arXiv.2102.05814
• Akiba, T., Sano, S., Yanase, T., Ohta, T., & Koyama, M. (2019, July). Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining (pp. 2623-2631). https://doi.org/10.1145/3292500.3330701
• Archetti, F., & Candelieri, A. (2019). Software Resources. In: Bayesian Optimization and Data Science (pp. 97-109). https://doi.org/10.1007/978-3-030-24494-1_6
• Ayvaz, S., & Alpay, K. (2021). Predictive maintenance system for production lines in manufacturing: A machine learning approach using IoT data in real-time. Expert Systems with Applications, 173, 114598. https://doi.org/10.1016/j.eswa.2021.114598
• Campos, J. R., Costa, E., & Vieira, M. (2019). Improving failure prediction by ensembling the decisions of machine learning models: a case study. IEEE Access, 7, 177661-177674. https://doi.org/10.1109/ACCESS.2019.2958480
• Celikmih, K., Inan, O., & Uguz, H. (2020). Failure prediction of aircraft equipment using machine learning with a hybrid data preparation method. Scientific Programming, 2020(1), 8616039. https://doi.org/10.1155/2020/8616039
• Chen, T., Chen, X., Chen, W., Wang, Z., Heaton, H. W., Liu, J., & Yin, W. (2022). Learning to optimize: A primer and a benchmark. The Journal of Machine Learning Research, 23(1), 8562-8620.
• El-Kenawy, E.-S., & Eid, M. (2020). Hybrid gray wolf and particle swarm optimization for feature selection. International Journal of Innovative Computing, Information and Control, 16(3), 831-844. http://doi.org/10.24507/ijicic.16.03.831
• Hassanali, M., Soltanaghaei, M., Javdani Gandomani, T., & Zamani Boroujeni, F. (2024). Software development effort estimation using boosting algorithms and automatic tuning of hyperparameters with Optuna. Journal of Software: Evolution and Process, 36(9), e2665. https://doi.org/10.1002/smr.2665
• Hatta, N. M., Zain, A. M., Sallehuddin, R., Shayfull, Z., & Yusoff, Y. (2019). Recent studies on optimisation method of Grey Wolf Optimiser (GWO): a review (2014–2017). Artificial Intelligence Review, 52, 2651-2683. https://doi.org/10.1007/s10462-018-9634-2
• Jin, X., Chen, Y., Wang, L., Han, H., & Chen, P. (2021). Failure prediction, monitoring and diagnosis methods for slewing bearings of large-scale wind turbine: A review. Measurement, 172, 108855. https://doi.org/10.1016/j.measurement.2020.108855
• Khaire, U. M., & Dhanalakshmi, R. (2022). Stability investigation of improved whale optimization algorithm in the process of feature selection. IETE Technical Review, 39(2), 286-300. https://doi.org/10.1080/02564602.2020.1843554
• Lasotte, Y. B., Garba, E. J., Malgwi, Y. M., & Buhari, M. A. (2022). An ensemble machine learning approach for fake news detection and classification using a soft voting classifier. European Journal of Electrical Engineering and Computer Science, 6(2), 1-7. https://doi.org/10.24018/ejece.2022.6.2.409
• Leukel, J., González, J., & Riekert, M. (2021). Adoption of machine learning technology for failure prediction in industrial maintenance: A systematic review. Journal of Manufacturing Systems, 61, 87-96. https://doi.org/10.1016/j.jmsy.2021.08.012
• Li, J., King, S., & Jennions, I. (2023). Intelligent fault diagnosis of an aircraft fuel system using machine learning—A literature review. Machines, 11(4), 481. https://doi.org/10.3390/machines11040481
• Luo, G. (2016). A review of automatic selection methods for machine learning algorithms and hyper-parameter values. Network Modeling Analysis in Health Informatics and Bioinformatics, 5, 18. https://doi.org/10.1007/s13721-016-0125-6
• Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in engineering software, 95, 51-67. https://doi.org/10.1016/j.advengsoft.2016.01.008
• Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in engineering software, 69, 46-61. https://doi.org/10.1016/j.advengsoft.2013.12.007
• Mishra, K., & Manjhi, S. K. (2018, November). Failure prediction model for predictive maintenance. In: 2018 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM) (pp. 72-75). https://doi.org/10.1109/CCEM.2018.00019
• Mohammed, B., Awan, I., Ugail, H., & Younas, M. (2019). Failure prediction using machine learning in a virtualised HPC system and application. Cluster Computing, 22, 471-485. https://doi.org/10.1007/s10586-019-02917-1
• Parra-Ullauri, J., Zhang, X., Bravalheri, A., Nejabati, R., & Simeonidou, D. (2023, March). Federated Hyperparameter Optimisation with Flower and Optuna. In: Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing (pp. 1209-1216). https://doi.org/10.1145/3555776.3577847
• Pellegrini, A., Di Sanzo, P., & Avresky, D. R. (2015, May). A machine learning-based framework for building application failure prediction models. In: 2015 IEEE International Parallel and Distributed Processing Symposium Workshop (pp. 1072-1081). https://doi.org/10.1109/IPDPSW.2015.110
• Ponemon Institute. (2011, February). Cost of data center outages. https://www.ponemon.org/local/upload/file/2011%20Cost_of_Data_Center_Outages.pdf
• Qaraad, M., Amjad, S., Hussein, N. K., & Elhosseini, M. A. (2022). Large scale salp-based grey wolf optimization for feature selection and global optimization. Neural Computing and Applications, 34(11), 8989-9014. https://doi.org/10.1007/s00521-022-06921-2
• Rajadurai, H., & Gandhi, U. D. (2022). A stacked ensemble learning model for intrusion detection in wireless network. Neural computing and applications, 34(18), 15387-15395. https://doi.org/10.1007/s00521-020-04986-5
• Rzayeva, L., Myrzatay, A., Abitova, G., Sarinova, A., Kulniyazova, K., Saoud, B., & Shayea, I. (2023). Enhancing LAN Failure Predictions with Decision Trees and SVMs: Methodology and Implementation. Electronics, 12(18), 3950. https://doi.org/10.3390/electronics12183950
• Seyyedabbasi, A., & Kiani, F. (2021). I-GWO and Ex-GWO: improved algorithms of the Grey Wolf Optimizer to solve global optimization problems. Engineering with Computers, 37(1), 509-532. https://doi.org/10.1007/s00366-019-00837-7
• Shen, C., & Zhang, K. (2022). Two-stage improved Grey Wolf optimization algorithm for feature selection on high-dimensional classification. Complex & Intelligent Systems, 8(4), 2769-2789. https://doi.org/10.1007/s40747-021-00452-4
• Tørring, J. O., & Elster, A. C. (2022, May). Analyzing search techniques for autotuning image-based gpu kernels: The impact of sample sizes. In: 2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) (pp. 972-981). https://doi.org/10.1109/IPDPSW55747.2022.00155
• Tweney, D. (2013, August). Amazon website goes down for 40 minutes, costing the company $5 million. VentureBeat. https://venturebeat.com/business/amazon-website-down/
• Wahid, A., Breslin, J. G., & Intizar, M. A. (2022). Prediction of machine failure in industry 4.0: a hybrid CNN-LSTM framework. Applied Sciences, 12(9), 4221. https://doi.org/10.3390/app12094221
• Wang, Z., Qin, C., Wan, B., & Song, W. W. (2021). A comparative study of common nature-inspired algorithms for continuous function optimization. Entropy, 23(7), 874. https://doi.org/10.3390/e23070874
• Wang, Z., Zhang, M., Wang, D., Song, C., Liu, M., Li, J., Lou, L., & Liu, Z. (2017). Failure prediction using machine learning and time series in optical network. Optics Express, 25(16), 18553-18565. https://doi.org/10.1364/OE.25.018553
• Young, M. T., Hinkle, J., Ramanathan, A., & Kannan, R. (2018, September). Hyperspace: Distributed bayesian hyperparameter optimization. In: 2018 30th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD) (pp. 339-347). https://doi.org/10.1109/CAHPC.2018.8645954
• Yu, T., & Zhu, H. (2020). Hyper-parameter optimization: A review of algorithms and applications. https://doi.org/10.48550/arXiv.2003.05689
• Zonta, T., Da Costa, C. A., da Rosa Righi, R., de Lima, M. J., da Trindade, E. S., & Li, G. P. (2020). Predictive maintenance in the Industry 4.0: A systematic literature review. Computers & Industrial Engineering, 150, 106889. https://doi.org/10.1016/j.cie.2020.106889