AI-Driven Predictive Diagnostic Maintenance for Lifespan and Efficiency of Massage Chairs

We develop and evaluate an AI-driven predictive diagnostic maintenance system for massage chair motors, utilizing real-time data acquisition, cloud-based analytics, and machine learning algorithms. The system integrates mechanica and electrical lifespan metrics, assessing variables under varying operational conditions. A machine learning model, pretrained with historical motor performance, failure records, and maintenance logs, predicts motor failure patterns and estimates Remaining Useful Life (RUL). Data from sensors monitoring voltage, current, temperature, and rotational speed are encrypted and transmitted to a cloud platform for analysis. We employ the Weibull distribution for failure time modeling optimizing maintenance schedules based on simulated data. The results demonstrate the system’s effectiveness in enhancing motor lifespan, reducing maintenance costs, and improving operational efficiency. This approach showcases the potential of AI and IoT technologies in advancing predictive maintenance strategies for consumer products.