AI-Based Predictive Maintenance of Industrial Equipment Using Sensor Data and Machine Learning

Abstract

Unexpected equipment failure is a primary driver of unscheduled downtime and catastrophic financial loss in modern manufacturing environments. While high-criticality assets are frequently monitored by expensive proprietary systems, the vast majority of secondary “balance of plant” auxiliary machinery remains completely unmonitored due to high sensor costs. This study addresses this critical gap by developing an ultra-low-cost, edge-compatible Predictive Maintenance (PM) system designed for continuous 24/7 condition monitoring. The novelty of this research lies in the integration of a multi-variable sensory framework, combining a Micro-Electro-Mechanical Systems (MEMS) digital accelerometer and a digital thermal sensor controlled by an ESP32 microcontroller with a customized ensemble learning architecture optimized for safety-critical deployment. A localized Random Forest model was trained on distinct experimental failure states to map multi-variable fault signatures. The system demonstrated high classification accuracy, successfully executing closed-loop real-time visual alerts on a dynamic Human-Machine Interface (HMI) with an operational latency of 200ms. The results validate that combining budget-accessible IoT hardware with intelligent data-smoothing backend algorithms provides small-to-medium enterprises with a reliable, scalable diagnostic alternative to commercial-grade asset monitoring suites.