AI-Based Predictive Maintenance in Manufacturing Systems

Abstract

Digital In order to guarantee the operational effectiveness and dependability of production systems, maintenance is essential. Reactive and preventative techniques, two traditional maintenance approaches, frequently lead to more downtime and needless expenses. With the development of Industry 4.0, predictive maintenance—a more intelligent solution—is made possible by the combination of artificial intelligence (AI) and predictive analytics. The usefulness of AI-driven methods for anticipating equipment failures before they happen is being investigated in the current study. These methods include machine learning algorithms, real-time sensor data processing, and anomaly detection models. In order to evaluate trends in machine health characteristics such as vibration, temperature, and pressure, the suggested system is modeled and simulated using Python-based machine learning frameworks.Manufacturers can minimize downtime and maximize operational efficiency by using these information to guide appropriate maintenance activities. Performance, accuracy, and cost-effectiveness are assessed by comparing AI-based predictive systems with conventional maintenance techniques. Incorporating AI into predictive maintenance enhances asset lifespan and makes manufacturing a more intelligent, proactive setting.