Artificial Intelligence for Fault Early Warning in Equipment Manufacturing
Artificial intelligence can identify equipment deterioration, but accurate fault classification does not by itself establish effective early warning. This narrative review examines how condition monitoring can support maintenance decisions in machinery-manufacturing plants. It connects diagnostic and prognostic research with data quality, operating context, uncertainty, and alert evaluation. A decision-oriented framework is developed through synthesis, linking observations to a prediction target, an event-level warning, a maintenance response, and subsequent verification. Two illustrative applications, machining-centre spindle monitoring and hydraulic press monitoring, explain possible implementation choices without implying completed field trials. The review argues that practical evaluation should consider useful warning time, missed events, false-alert burden, and maintenance consequences alongside model performance. Controlled faults and simulated degradation provide valuable methodological evidence, but their relevance to factory operation requires separate assessment. No new experimental results are reported.
[1] Jardine AKS, Lin D, Banjevic D, 2006, A Review on Machinery Diagnostics and Prognostics Implementing Condition-Based Maintenance. Mechanical Systems and Signal Processing, 20(7): 1483–1510.
[2] Carvalho TP, Soares FAAMN, Vita R, et al., 2019, A Systematic Literature Review of Machine Learning Methods Applied to Predictive Maintenance. Computers & Industrial Engineering, 137: 106024.
[3] Zonta T, da Costa CA, da Rosa Righi R, et al., 2020, Predictive Maintenance in Industry 4.0: A Systematic Literature Review. Computers & Industrial Engineering, 150: 106889.
[4] Mallioris P, Aivazidou E, Bechtsis D, 2024, Predictive Maintenance in Industry 4.0: A Systematic Multi-Sector Mapping. CIRP Journal of Manufacturing Science and Technology, 50: 80–103.
[5] Lei Y, Yang B, Jiang X, et al., 2020, Applications of Machine Learning to Machine Fault Diagnosis: A Review and Roadmap. Mechanical Systems and Signal Processing, 138: 106587.
[6] Zhao R, Yan R, Chen Z, et al., 2019, Deep Learning and Its Applications to Machine Health Monitoring. Mechanical Systems and Signal Processing, 115: 213–237.
[7] Fink O, Wang Q, Svensen M, et al., 2020, Potential, Challenges and Future Directions for Deep Learning in Prognostics and Health Management Applications. Engineering Applications of Artificial Intelligence, 92: 103678.
[8] Vashishtha G, Chauhan S, Sehri M, et al., 2025, A Roadmap to Fault Diagnosis of Industrial Machines via Machine Learning: A Brief Review. Measurement, 242: 116216.
[9] Susto GA, Schirru A, Pampuri S, et al., 2015, Machine Learning for Predictive Maintenance: A Multiple Classifier Approach. IEEE Transactions on Industrial Informatics, 11(3): 812–820.
[10] Si XS, Wang W, Hu CH, et al., 2011, Remaining Useful Life Estimation: A Review on the Statistical Data-Driven Approaches. European Journal of Operational Research, 213(1): 1–14.
[11] Tobon-Mejia DA, Medjaher K, Zerhouni N, et al., 2012, A Data-Driven Failure Prognostics Method Based on Mixture of Gaussians Hidden Markov Models. IEEE Transactions on Reliability, 61(2): 491–503.
[12] Lessmeier C, Kimotho JK, Zimmer D, et al., 2016, Condition Monitoring of Bearing Damage in Electromechanical Drive Systems by Using Motor Current Signals of Electric Motors: A Benchmark Data Set for Data-Driven Classification. PHM Society European Conference, 3(1): 1577.
[13] Saxena A, Goebel K, Simon D, et al., 2008, Damage Propagation Modeling for Aircraft Engine Prognostics. NASA Technical Reports Server, Record 20090029214.
[14] Ganin Y, Ustinova E, Ajakan H, et al., 2016, Domain-Adversarial Training of Neural Networks. Journal of Machine Learning Research, 17(59): 1–35.
[15] Guo C, Pleiss G, Sun Y, et al., 2017, On Calibration of Modern Neural Networks. Proceedings of the 34th International Conference on Machine Learning, PMLR 70: 1321–1330.
[16] Ribeiro MT, Singh S, Guestrin C, 2016, "Why Should I Trust You?": Explaining the Predictions of Any Classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
[17] Lundberg SM, Lee SI, 2017, A Unified Approach to Interpreting Model Predictions. Advances in Neural Information Processing Systems, 30.
