A Systematic Review of Anomaly and Fault Detection Using Machine Learning for Industrial Machinery

Unplanned downtime in industrial machinery remains a major challenge, causing substantial economic losses and safety risks across sectors such as manufacturing, food processing, oil and gas, and transportation. This systematic review investigates the application of machine learning (ML) techniques for anomaly and fault detection within the broader context of predictive maintenance. Following a hybrid review methodology, relevant studies published between 2010 and 2025 were collected from major databases including IEEE Xplore, ScienceDirect, SpringerLink, Scopus, Web of Science, and arXiv. The review categorizes approaches into supervised, unsupervised, and hybrid paradigms, analyzing their pipelines from data collection and preprocessing to model deployment. Findings highlight the effectiveness of deep learning architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), autoencoders, and hybrid frameworks in detecting faults from time series and multimodal sensor data. At the same time, key limitations persist, including data scarcity, class imbalance, limited generalizability across equipment types, and a lack of interpretability in deep models. This review concludes that while ML-based predictive maintenance systems are enabling a transition from reactive to proactive strategies, future progress requires improved hybrid architectures, Explainable AI, and scalable real-time deployment.