PredictiveOps : Advanced Predictive Maintenance System for Industrial Machinery

Abstract

Predictive maintenance is transforming industrial equipment management through the application of machine learning (ML) and the Internet of Things (IoT) for pre-failure detection. This paper investigates the PredictiveOps system that minimizes downtime, lowers maintenance expenditures, and increases operational effectiveness. This research puts the IoT sensors' function for the monitoring of such key parameters as vibration, temperature and rotational speed into the limelight, making condition-based maintenance possible. Anomaly detection with explainable AI ensures actionable output for maintenance staff. Predictive models also ensure minimal spare parts inventory, preventing undue stockholding. Findings indicate that predictive maintenance improves equipment reliability, prolongs asset life, and minimizes industrial waste. Challenges such as sensor noise and data imbalance still exist, calling for additional research. Through the transition from reactive to proactive measures, PredictiveOps is advancing Industry 4.0, promoting efficiency and sustainability.