Machine learning-driven condition monitoring for predictive maintenance - Overview

Aug 3, 2026

Machine learning methods, including Artificial Neural Networks and Random Forest Regression, support proactive failure forecasting by building data-centric thermal models for power electronics modules, helping prevent critical malfunctions such as air outlet blockages. Combining machine learning with Proportional Hazards Models improves maintenance accuracy through failure-rate prognostics and maintenance planning using multiple covariates. Sensor fusion and deep learning enable real-time monitoring for predicting mechanical wear and avoiding unplanned downtime, while incremental learning, adaptive health scoring, and robust statistics remain central.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages8

Reading Time20 min

AccessFree to download

1 Introduction

Machine learning in condition monitoring

Explainable AI and black-box opacity

Proportional Hazards Model integration

Deep learning for machining and tool health

Challenges and practical considerations

Machine learning-driven condition monitoring for predictive maintenance Aliyeva Mahliyo * Tashkent State Technical University, Tashkent, Uzbekistan Abstract. 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 andrecurrent neural networks facilitate intelligent machining and tool health monitoring, ushering in data-driven smart manufacturing paradigms. Leveraging sensor data integration and machine learning frameworks, realtime 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. 1 Introduction Machine learning (ML) [1] has emerged as a cornerstone in the realm of condition monitoring systems, significantly augmenting predictive maintenance capabilities within industrial domains. ML algorithms exhibit the capacity to forecast impending failures proactively, leveraging extant data reservoirs [2] to construct data-centric thermal models [3] tailored for power electronics modules [5]. This paradigm facilitates the discernment of aberrations such as air outlet blockages, thus mitigating the risk of catastrophic malfunctions. Notably, ML methodologies [6], encompassing Artificial Neural Networks and Random Forest Regression [7], are harnessed for the estimation of road surface roughness, a pivotal aspect of road safety * Corresponding author: [tdtu2024.uz@gmail.com](tdtu2024.uz@gmail.com) © The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)). surveillance. Despite the proficiency of ML techniques in anomaly detection and diagnostic endeavors for condition-based maintenance, the opacity inherent in black-box models poses a notable challenge. The integration of explainable artificial intelligence holds promise in furnishing invaluable insights conducive to informed decision-making [8] within maintenance frameworks. In various industrial sectors, machine learning assumes a pivotal role in the realms of condition monitoring and prognostics, facilitating predictive maintenance endeavors. Through the scrutiny of machine-generated data and the identification of anomalies, machine learning frameworks facilitate the prognostication of maintenance requisites, thereby augmenting operational efficiency while curtailing downtime. The amalgamation of machine learning with the Proportional Hazards Model (PHM) [9] affords the capability to prognosticate failure rates and delineate maintenance strategies predicated upon multiple covariates, thereby enhancing the precision of maintenance interventions. Furthermore, deep learning paradigms such as deep belief networks and recurre

FAQs

How does machine learning enable predictive maintenance in industrial systems?

Machine learning uses existing sensor and operational data to build data-centric models that forecast impending failures and identify anomalies. This enables maintenance planning before faults cause downtime.

What role do thermal models play in forecasting failures?

Thermal models constructed from data help predict failures in power electronics modules and reduce the risk of catastrophic events such as air outlet blockages.

Why is explainable AI important for maintenance decisions?

Black-box models can be difficult to interpret, limiting trust in diagnostics. Explainable AI techniques provide insights into model decisions, supporting informed maintenance actions.

Aug 3, 2026

Uploaded by River Wang