Real-Time Predictive Maintenance with ML-Enhanced IoT Sensor Data Processing
DOI:
https://doi.org/10.22399/ijcesen.4255Keywords:
Predictive Maintenance, Real-Time Analytics, IoT Sensors, Machine Learning, Edge Computing, Federated LearningAbstract
Introduction of machine learning (ML) and Internet of Things (IoT) sensor data has transformed the concept of predictive maintenance (PdM) allowing the industries to shift to real-time and intelligent decision-making systems rather than reactive ones.This review examines the modern real time PdM landscape, highlighting the relevant ML approaches, architectures, deployment models, and applications. The work is a synthesis of the results of the last ten years, comparing such algorithms as Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and Echo State Networks and evaluates them in the conditions of the real world when it is necessary to consider the time of work. The concept of a hybrid edge-cloud architecture has been put forward to meet the requirement of low-latency inference, scalability, and data privacy. The review ends with the named challenges including model interpretability, unlabeled data, and cybersecurity and provides the directions promising to be successful in the future, including federated learning, explainable AI, and adaptive transfer learning. The presented insights can be a guide to researchers, practitioners, and policy-makers who want to create resilient and intelligent maintenance infrastructures during the Industry 4.0 era.
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