Predictive Maintenance Using Machine Learning for Engineering Systems Through Real-Time Sensor Data and Anomaly Detection Models

Abstract

The proliferation of connected industrial assets and the rising demand for operational efficiency have positioned predictive maintenance (PdM) as a pivotal strategy in modern engineering systems. Traditional maintenance paradigms reactive and time-based approaches often result in unplanned downtimes or unnecessary maintenance, leading to increased costs and operational disruptions. Predictive maintenance, driven by the integration of machine learning (ML) and real-time sensor technologies, presents a data-centric alternative that enables proactive decision-making. By harnessing historical and streaming sensor data, ML algorithms can learn complex degradation patterns, predict potential failures, and optimize maintenance schedules. This study explores the deployment of machine learning techniques including supervised, unsupervised, and deep learning models for anomaly detection and failure prediction in complex engineering systems such as manufacturing plants, aerospace engines, and transportation infrastructure. It discusses the architectural requirements for real-time PdM frameworks, including the role of edge computing, sensor fusion, and IoT-enabled telemetry in capturing high-frequency operational data. Emphasis is placed on the implementation of anomaly detection models, such as Isolation Forests, Autoencoders, and Long Short-Term Memory (LSTM) networks, which are capable of identifying subtle deviations from normal behavior before catastrophic failure occurs. Furthermore, the paper addresses challenges such as data imbalance, sensor drift, model interpretability, and the integration of domain knowledge into ML workflows. Case studies from industrial sectors highlight measurable improvements in asset reliability, maintenance cost reduction, and risk mitigation. The paper concludes by advocating for a hybrid PdM strategy that combines real-time analytics with probabilistic modeling to deliver scalable and explainable maintenance solutions for critical engineering applications.