ML algorithms, including Artificial Neural Networks and Random Forest Regression, enable the proactive forecasting of impending failures by constructing data-centric thermal models tailored for power electronics modules, thus averting catastrophic malfunctions such as air outlet blockages. Moreover, the integration of ML with the Proportional Hazards Model (PHM) enhances maintenance precision by prognosticating failure rates and delineating maintenance strategies based on multiple covariates. Deep learning paradigms such as deep belief networks and recurrent neural networks facilitate intelligent machining and tool health monitoring, ushering in data-driven smart manufacturing paradigms. Leveraging sensor data integration and machine learning frameworks, real- time monitoring of machine health status enables the prediction of mechanical wear and prevention of unforeseen downtime. The methodologies underscore the importance of incremental learning, adaptive health scoring, and robust statistical modeling to enable proactive maintenance initiatives before significant disruptions occur. However, challenges remain in ensuring data quality, model interpretability, and deployment complexities. By incorporating explainable artificial intelligence techniques, stakeholders can gain valuable insights into model decisions, fostering informed decision-making in maintenance operations. Overall, machine learning-driven condition monitoring and predictive maintenance offer a promising pathway towards enhanced operational efficiency, reduced downtime, and improved asset reliability in industrial domains.
Aliyeva Mahliyo (Fri,) studied this question.