Enhancing Predictive Maintenance in Energy Systems Using a Hybrid Kolmogorov-Arnold Network (KAN) with Short-Time Fourier Transform (STFT) Framework for Rotating Machinery

Abdul-Rasool Kareem Jweri, Ahmed Ali Farhan Ogaili, Samir Ali Amin, Mohammed I. Khalaf, Luttfi A. Al-Haddad, Alaa Abdulhady Jaber, Mustafa I. Karkhi

Abstract


This study proposes a hybrid predictive maintenance framework that integrates the Kolmogorov-Arnold Network (KAN) with Short-Time Fourier Transform (STFT) for intelligent fault diagnosis in industrial rotating machinery. The method is designed to address challenges posed by non-linear and non-stationary vibration signals under varying operational conditions. Experimental validation using the FALEX multispecimen test bench demonstrated a high classification accuracy of 97.5%, outperforming traditional models such as SVM, Random Forest, and XGBoost. The approach maintained robust performance across dynamic load scenarios and noisy environments, with precision and recall exceeding 95%. Key contributions include a hardware-accelerated KAN architecture, adaptive feature selection, and integration of explainable AI for interpretability. This framework enables real-time, transparent diagnostics in energy-critical, resource-constrained environments, supporting improved asset lifecycle management and reduced downtime. The study advances AI-based condition monitoring, bridging theoretical innovation with practical reliability in the context of sustainable industrial energy systems.

Keywords


Fault diagnosis; KAN; Rotating machinery; Short-Time Fourier Transform (STFT); Vibration signal analysis

Full Text:

PDF

References


Mezni, Z., Delpha, C., Diallo, D., and Braham, A. (2022). Performance of bearing ball defect classification based on the fusion of selected statistical features. Entropy, 24(9), 1251.

Altaf, M., Akram, T., Khan, M. A., Iqbal, M., Ch, M. M. I., and Hsu, C. H. (2022). A new statistical features-based approach for bearing fault diagnosis using vibration signals. Sensors, 22(5), 2012.

Jaber, A. A., and Bicker, R. (2016). Fault diagnosis of industrial robot gears based on discrete wavelet transform and artificial neural network. Insight-Non-Destructive Testing and Condition Monitoring, 58(4), 179-186.

Jaber, A. A., and Bicker, R. (2018). Development of a condition monitoring algorithm for industrial robots based on artificial intelligence and signal processing techniques. International Journal of Electrical and Computer Engineering (2088-8708), 8(2), 996-1009.

Dubaish, A. A., and Jaber, A. A. (2023). State-of-the-art review into signal processing and artificial intelligence-based approaches applied in gearbox defect diagnosis. Journal of Engineering and Technological Sciences, 10(9), 1-16.

Giuliano, R., Innocenti, E., Mazzenga, F., Vegni, A. M., and Vizzarri, A. (2021). IMPERSONAL: an IoT-aided computer vision framework for social distancing for health safety. IEEE Internet of Things Journal, 9(10), 7261-7272.

Cardarilli, G. C., Di Nunzio, L., Fazzolari, R., Re, M., Silvestri, F., and Spanò, S. (2018). Energy consumption saving in embedded microprocessors using hardware accelerators. TELKOMNIKA (Telecommunication Computing Electronics and Control), 16(3), 1019-1026.

Zhang, Y., Lv, Y., and Ge, M. (2021). Time–frequency analysis via complementary ensemble adaptive local iterative filtering and enhanced maximum correlation kurtosis deconvolution for wind turbine fault diagnosis. Energy Reports, 7, 2418-2435.

Zhang, J., Chen, J., Deng, H., and Hu, W. (2023). A novel framework based on adaptive multi-task learning for bearing fault diagnosis. Energy Reports, 9, 522-531.

Guo, Z., Yang, M., and Huang, X. (2022). Bearing fault diagnosis based on speed signal and CNN model. Energy Reports, 8, 904–913.

Tang, Z., Wang, M., Ouyang, T., and Che, F. (2022). A wind turbine bearing fault diagnosis method based on fused depth features in time–frequency domain. Energy Reports, 8, 12727-12739.

Qin, S., Tao, J., and Zhao, Z. (2023). Fault diagnosis of wind turbine pitch system based on LSTM with multi-channel attention mechanism. Energy Reports, 10, 4087–4096.

Vakharia, V., Gupta, V. K., and Kankar, P. K. (2016). A comparison of feature ranking techniques for fault diagnosis of ball bearing. Soft Computing, 20, 1601–1619.

Ogaili, A. A., Hamzah, M. N., and Jaber, A. A. (2022). Free vibration analysis of a wind turbine blade made of composite materials. International Middle Eastern Simulation and Modeling Conference, 2022, 27–29.

Ogaili, A. A. F., Hamzah, M. N., and Jaber, A. A. (2024). Enhanced fault detection of wind turbine using extreme gradient boosting technique based on nonstationary vibration analysis. Journal of Failure Analysis and Prevention, 24(2), 877-895.

Zhang, X., Wang, B., and Chen, X. (2015). Intelligent fault diagnosis of roller bearings with multivariable ensemble-based incremental support vector machine. Knowledge-Based Systems, 89, 56–85.

Gunerkar, R. S., Jalan, A. K., and Belgamwar, S. U. (2019). Fault diagnosis of rolling element bearing based on artificial neural network. Journal of Mechanical Science and Technology, 33, 505–511.

Senthilnathan, N., Babu, T. N., Varma, K. S. D., Rushmith, S., Reddy, J. A., Kavitha, K. V. N., and Prabha, D. R. (2024). Recent advancements in fault diagnosis of spherical roller bearing: A short review. Journal of Vibration Engineering and Technologies, 12(4), 6963-6977.

Shandookh, A. A., Farhan Ogaili, A. A., and Al-Haddad, L. A. (2024). Failure analysis in predictive maintenance: Belt drive diagnostics with expert systems and Taguchi method for unconventional vibration features. Heliyon, 10, e34202.

Ogaili, A. A. F., Jaber, A. A., and Hamzah, M. N. (2023). A methodological approach for detecting multiple faults in wind turbine blades based on vibration signals and machine learning. Curved and Layered Structures, 10(1), 20220214.

Canese, L., Cardarilli, G. C., Di Nunzio, L., Fazzolari, R., Re, M., and Spanò, S. (2023). A hardware-oriented qam demodulation method driven by aw-som machine learning. In 2023 57th Asilomar Conference on Signals, Systems, and Computers, 2023, 937-941.

Cardarilli, G. C., Di Nunzio, L., Fazzolari, R., Giardino, D., Re, M., Ricci, A., and Spano, S. (2022). An FPGA-based multi-agent reinforcement learning timing synchronizer. Computers and Electrical Engineering, 99, 107749.

Lei, Y., Yang, B., Jiang, X., Jia, F., Li, N., and Nandi, A. K. (2020). Applications of machine learning to machine fault diagnosis: A review and roadmap. Mechanical Systems and Signal Processing, 138, 106587.

Ogaili, A. A. F., Jaber, A. A., and Hamzah, M. N. (2023). Statistically optimal vibration feature selection for fault diagnosis in wind turbine blade. International Journal of Renewable Energy Research (IJRER), 13, 1082–1092.

Haryanto, A., and Telaumbanua, M. (2020). Application of artificial neural network to predict biodiesel yield from waste frying oil transesterification. Indonesian Journal of Science and Technology, 5(1), 62-74.

Paranjay, O.A., and Rajeshkumar, V. (2020). A neural network aided real-time hospital recommendation system. Indonesian Journal of Science and Technology, 5(2), 217-235.

El Brahmi, A., Abderafi, S., and Ellaia, R. (2021). Artificial neural network analysis of sulfide production in a Moroccan sewerage network. Indonesian Journal of Science and Technology, 6(1), 193-204.

Caraka, R.E., Chen, R.C., Yasin, H., Suhartono, S., Lee, Y., and Pardamean, B. (2021). Hybrid vector autoregression feedforward neural network with genetic algorithm model for forecasting space-time pollution data. Indonesian Journal of Science and Technology, 6(1), 243-266.

Herath, H.M.M.N. (2025). Evolution and advancements from neural network to deep learning. ASEAN Journal of Educational Research and Technology, 4(1), 59-80.

Zhao, D., Wang, T., and Chu, F. (2019). Deep convolutional neural network-based planet bearing fault classification. Computers in Industry, 107, 59–66.

Wang, J., Dong, Z., and Zhang, S. (2024). KAN-HyperMP: An enhanced fault diagnosis model for rolling bearings in noisy environments. Sensors (Basel, Switzerland), 24(19), 6448.

Zheng, J., Li, M., Tian, Y., and Wang, X. (2024). Fault Diagnosis of Suspension Controllers Based on KAN-ResNet. In 2024 5th International Conference on Machine Learning and Computer Application (ICMLCA), 2024, 391-395.

Cabral, T. W., Gomes, F. V., de Lima, E. R., Filho, J. C., and Meloni, L. G. (2024). Kolmogorov–arnold network in the fault diagnosis of oil-immersed power transformers. Sensors, 24(23), 7585.

Wang, L., Ai, Q., Yan, H., Hao, M., and Li, X. (2024). Advanced Bearing Fault Diagnosis Using Cuckoo Optimization and KAN Algorithms. 2024 4th International Conference on Electronic Information Engineering and Computer Science (EIECS), 2024, 98-102.

Li, Y., Hu, M., Yang, X., and Ma, M. (2024). Fault Prediction of Firefighting Unmanned Aerial Vehicles Based on VMD-KAN-LSTM. 2024 5th International Conference on Computer Engineering and Intelligent Control (ICCEIC), 2024, 67-70.

Li, Y., Gu, X., and Wei, Y. (2024). A Deep Learning-Based Method for Bearing Fault Diagnosis with Few-Shot Learning. Sensors, 24(23), 7516.

Del Rosario, C. A., Camaclang, R. C., Prieto-Araujo, E., and Gomis-Bellmunt, O. (2024). Kolmogorov-Arnold network for machine learning-based motor condition diagnosis. In 2024 IEEE 7th International Conference on Electrical, Electronics and System Engineering (ICEESE), 2024, 1-5.

Ji, T., Hou, Y., and Zhang, D. (2024). A comprehensive survey on kolmogorov arnold networks (kan). arXiv preprint arXiv, 2407, 11075.

Liu, Z., Wang, Y., Vaidya, S., Ruehle, F., Halverson, J., Soljačić, M., Hou, T. Y., and Tegmark, M. (2024). Kan: Kolmogorov-arnold networks. arXiv preprint arXiv, 2404, 19756.

Kolmogorov, A. N. (1957). On the representations of continuous functions of many variables by superposition of continuous functions of one variable and addition. In Dokl. Akad. Nauk USSR, 114, 953-956.

Bodner, A. D., Tepsich, A. S., Spolski, J. N., and Pourteau, S. (2024). Convolutional kolmogorov-arnold networks. arXiv preprint arXiv, 2406, 13155.

Abueidda, D. W., Pantidis, P., and Mobasher, M. E. (2025). Deepokan: Deep operator network based on Kolmogorov Arnold networks for mechanics problems. Computer Methods in Applied Mechanics and Engineering, 436, 117699.

Bunyan, S. T., Khan, Z. H., Al-Haddad, L. A., Dhahad, H. A., Al-Karkhi, M. I., Ogaili, A. A. F., and Al-Sharify, Z. T. (2025). Intelligent thermal condition monitoring for predictive maintenance of gas turbines using machine learning. Machines, 13(5), 401.

Kankar, P. K., Sharma, S. C., and Harsha, S. P. (2011). Fault diagnosis of ball bearings using continuous wavelet transform. Applied Soft Computing, 11, 2300–2312.

Farhan Ogaili, A. A., Mohammed, K. A., Jaber, A. A., and Al-Ameen, E. S. (2024). Automated wind turbines gearbox condition monitoring: A comparative study of machine learning techniques based on vibration analysis. FME Transactions, 52(3), 471–485.

Kannan, V., Zhang, T., and Li, H. (2024). A review of the intelligent condition monitoring of rolling element bearings. Machines, 12(7), 484.

Mehala, N., and Dahiya, R. (2008). A comparative study of FFT, STFT and wavelet techniques for induction machine fault diagnostic analysis. Proceedings of the 7th WSEAS international conference on computational intelligence, man-machine systems and cybernetics, Cairo, Egypt, 2931, 203-208.

Antoni, J. (2006). The spectral kurtosis: A useful tool for characterising non-stationary signals. Mechanical Systems and Signal Processing, 20(2), 282-307.

Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., Yen, N., Tung, C. C., and Liu, H. H. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, 454(1971), 903-995.

Ismail, M. A., Bierig, A., and Sawalhi, N. (2018). Automated vibration-based fault size estimation for ball bearings using Savitzky–Golay differentiators. Journal of Vibration and Control, 24(18), 4297-4315.

Ismail, M. A., Windelberg, J., Bierig, A., Bravo, I., and Arnaiz, A. (2023). Ball bearing vibration data for detecting and quantifying spall faults. Data in Brief, 47, 109019.

Ogaili, A. A. F., Jaber, A. A., and Hamzah, M. N. (2023). Wind turbine blades fault diagnosis based on vibration dataset analysis. Data in Brief, 49, 109414.

Mohammed, K. A., Al-Sabbagh, M. N. M., Ogaili, A. A. F., and Al-Ameen, E. S. (2020). Experimental analysis of hot machining parameters in surface finishing of crankshaft. Journal of Mechanical Engineering Research and Developments, 43(4), 105-114.

Al-Haddad, L. A., and Jaber, A. A. (2023). Improved UAV blade unbalance prediction based on machine learning and Relief supreme feature ranking method. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 45(9), 463.

Al-Haddad, L. A., and Jaber, A. A. (2023). An intelligent fault diagnosis approach for multirotor UAVs based on deep neural network of multi-resolution transform features. Drones, 7(2), 82.

Al-Haddad, L. A., Jaber, A. A., Neranon, P., and Al-Haddad, S. A. (2023). Investigation of frequency-domain-based vibration signal analysis for UAV unbalance fault classification. Engineering and Technology Journal, 41(7), 1-9.

Ismail, M. A., Windelberg, J., Bierig, A., Bravo, I., and Arnaiz, A. (2023). Ball bearing vibration data for detecting and quantifying spall faults. Data in Brief, 47, 109019.

Ismail, M. A., and Sawalhi, N. (2017). Vibration response characterisation and fault-size estimation of spalled ball bearings. Insight-Non-Destructive Testing and Condition Monitoring, 59(3), 149-154.

Ogaili, A. A. F., Al-Sharify, Z. T., Abdulhady, A., and Abbas, F. (2024, September). Vibration-based fault detection and classification in ball bearings using statistical analysis and random forest. In Fifth International Conference on Green Energy, Environment, and Sustainable Development (GEESD 2024), 13279, 518-526.

Abdul-Zahra, A. S., Ghane, E., Kamali, A., and Farhan Ogaili, A. A. (2024). Power forecasting in continuous extrusion of pure titanium using Naïve Bayes algorithm. Terra Joule Journal, 1(1), 2.

Mahdi, N. M., Jassim, A. H., Abulqasim, S. A., Basem, A., Ogaili, A. A. F., and Al-Haddad, L. A. (2024). Leak detection and localization in water distribution systems using advanced feature analysis and an Artificial Neural Network. Desalination and Water Treatment, 320, 100685.

Qu, J., Zhang, Z., and Gong, T. (2016). A novel intelligent method for mechanical fault diagnosis based on dual-tree complex wavelet packet transform and multiple classifier fusion. Neurocomputing, 171, 837-853.

Metteb, Z. W., Ogaili, A. A. F., Mohammed, K. A., Alsayah, A. M., Hamzah, M. N., Al-Sharify, Z. T., Jaber, A. A., and Njim, E. K. (2025). Optimization of hybrid core designs in 3D-printed PLA+ sandwich structures: An experimental, statistical, and computational investigation completed with bibliometric analysis. Indonesian Journal of Science and Technology, 10(2), 207-236.

Kankar, P. K., Sharma, S. C., and Harsha, S. P. (2011). Fault diagnosis of ball bearings using continuous wavelet transform. Applied Soft Computing, 11(2), 2300-2312.

Sharma, A., Amarnath, M., and Kankar, P. K. (2016). Feature extraction and fault severity classification in ball bearings. Journal of Vibration and Control, 22(1), 176-192.

Vakharia, V., Gupta, V. K., and Kankar, P. K. (2017). Efficient fault diagnosis of ball bearing using ReliefF and Random Forest classifier. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 39(8), 2969-2982.




DOI: https://doi.org/10.17509/ajse.v5i2.89023

Refbacks

  • There are currently no refbacks.


Copyright (c) 2025 Universitas Pendidikan Indonesia

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

ASEAN Journal of Science and Engineering (AJSE) is published by UPI 

View My Stats