Predictive Analytics for Equipment Failure: Implementation and Outcomes of the Equipment Predisposed Tool

Abstract

This article presents a comprehensive investigation of the Equipment Predisposed Tool (EPT), a predictive analytics system designed to forecast equipment failures in industrial environments. It examines how advanced data analytics and machine learning techniques can transform maintenance operations from reactive to proactive paradigms. It details the data infrastructure, analytical methods, system architecture, and implementation strategies that underpin successful predictive maintenance initiatives. The article analyzes how the integration of sensor data, operational metrics, maintenance records, and environmental information can provide early detection of potential failures. It further explores various analytical techniques including time series analysis, machine learning classification, and reliability engineering models that collectively enable accurate prediction. Additionally, the analysis documents the operational benefits, financial returns, and key insights gained from the implementation while identifying future development directions in areas such as deep learning, digital twins, and prescriptive analytics.