Machine Learning-Driven Condition Monitoring and Fault Detection in Manufacturing

Abstract

The manufacturing industry has witnessed a surge in the adoption of machine learning (ML) techniques to enhance various aspects of production processes. One critical application of ML in manufacturing is condition monitoring and fault detection, which play a pivotal role in ensuring product quality, minimizing downtime, and maximizing operational efficiency. This paper presents a comprehensive review of the use of machine learning for condition monitoring and fault detection in manufacturing environments. It also discusses the importance of data preprocessing, feature engineering, and model selection in developing robust and reliable ML-based condition monitoring systems. Furthermore, the paper addresses the case studies, challenges and future trends associated with deploying ML-driven condition monitoring, such as data quality, model interpretability, and integration with existing manufacturing systems. It also highlights emerging trends and future research directions in this domain, including the integration of edge computing, digital twins, and advanced analytics for real-time, predictive, and prescriptive maintenance strategies.