Artificial Intelligence Model for Failure Prediction and Safety Enhancement in Manufacturing Plants

Abstract

Ensuring reliable equipment operations are critical for production efficiency and safety compliance in manufacturing industries. Unexpected machine breakdowns not only disrupt operations but also increase maintenance costs and safety risk. Traditional approaches — whether reactive or preventive — often fail to incorporate real-time equipment data and often overlook early fault indicators. Predictive maintenance strategies based on artificial intelligence models, address these shortcomings by monitoring machine conditions in real time, detect anomalies and forecast failures. Thus, this study proposes a refined deep neural network to classify machine health and estimate failure probability, thereby emphasizing Remaining Useful Life (RUL) prediction as a strategy for optimizing maintenance scheduling. In this study, the AI4I 2020 Predictive Maintenance dataset, which contains 10,000 records of machine operating conditions, including air and process temperatures, rotational speed, torque, tool wear, product type, and failure status was used for the evaluation of the proposed model. To improve the proposed model's performance, detailed preprocessing steps were deployed on the dataset. These steps include some preliminary categorical encoding and feature standardization on the dataset while class weighting, SMOTE and focal loss were deployed for handling class imbalance issues. According to the results achieved, the proposed model performed better across all metrics considered in comparison with similar models including baseline models. By integrating data-driven AI techniques with predictive maintenance strategies, this research study demonstrates how manufacturing plants can reduce downtime, extend machine lifespan, and minimize unnecessary maintenance interventions.