AI-Powered Predictive Maintenance System

Unplanned industrial equipment failures result in significant downtime, increased maintenance costs, and reduced operational efficiency. This paper presents the design and implementation of an AI powered predictive maintenance system for early fault detection in industrial machinery. The proposed framework integrates Internet of Things (IoT) sensors to continuously acquire operational parameters including temperature, vibration, current, and acoustic signals. The collected data is processed using machine learning algorithms to identify anomalous patterns associated with potential failures. Unlike conventional threshold-based monitoring systems, the proposed approach incorporates explainable artificial intelligence (XAI) techniques to improve interpretability and provide insights into the root cause of predicted faults. A recommendation module suggests corrective actions such as lubrication, recalibration, or component replacement based on detected fault conditions. In addition, a digital twin model is utilized to simulate machine behavior under varying operating scenarios, enabling performance optimization and reduction of unplanned downtime. Experimental evaluation demonstrates improved fault prediction accuracy and reduced maintenance response time compared to traditional preventive maintenance methods. The proposed system provides a scalable and efficient framework for intelligent condition monitoring in industrial environments.