Artificial Intelligence-Based Predictive Maintenance Framework for Industrial Automation Systems

Abstract

Predictive maintenance has become a key requirement in industrial automation because conventional corrective and time-based preventive maintenance may either react too late or replace components before their useful life is exhausted. This paper proposes an artificial intelligence (AI)-based predictive maintenance framework that integrates multi-sensor condition monitoring, data preprocessing, feature engineering, machine-learning classification, maintenance decision support, and feedback to programmable logic controller (PLC) or supervisory control and data acquisition (SCADA) environments. The methodology is designed for rotating and process equipment monitored through vibration, temperature, current, pressure, acoustic, operating-load, and runtime variables. A controlled simulation is used to demonstrate the evaluation procedure without presenting synthetic measurements as real plant data. Four models—logistic regression, support vector machine, random forest, and a multilayer perceptron neural network—are evaluated using accuracy, precision, recall, and F1-score. The proposed workflow emphasizes explainable maintenance triggers, periodic retraining, and integration with existing industrial automation architecture. The study indicates that ensemble and neural approaches are suitable for detecting nonlinear degradation patterns, while data quality, class imbalance, model drift, and integration with maintenance practice remain important deployment constraints.