Predictive Maintenance in Manufacturing: Utilizing Machine Learning for Equipment Health Monitoring

Abstract

Predictive maintenance utilizing machine learning is crucial for optimizing equipment health in manufacturing environments. This research presents a novel hybrid machine learning model that combines Support Vector Machines (SVM) and Recurrent Neural Networks (RNN) to predict equipment failures accurately. The model is built on a foundation of systematically collected sensor data and operational metrics, which undergo extensive preprocessing to ensure data quality and integrity. The SVM component is adept at classifying current equipment health states, while the RNN, particularly its Long Short-Term Memory (LSTM) networks, excels in analyzing temporal sequences of sensor data to predict future equipment conditions. This dual approach enables the model to achieve a high prediction accuracy of 91.4%. The implementation of this predictive maintenance model in a manufacturing plant has yielded significant operational benefits. Specifically, the model's real-time monitoring and alert system facilitated a 25% reduction in equipment downtime. Moreover, by enabling timely and accurate maintenance interventions, the model contributed to a 15% decrease in maintenance costs. The architecture of the developed system is robust and comprehensive, encompassing real-time data acquisition from IoT sensors, centralized data storage, and rigorous data processing. The continuous monitoring feature ensures that maintenance personnel are promptly alerted to potential issues, allowing for proactive measures that prevent equipment failures and minimize unplanned downtime. These results highlight the effectiveness of the hybrid SVM-RNN model in enhancing the reliability and efficiency of manufacturing operations. By leveraging advanced machine learning techniques, this predictive maintenance strategy demonstrates significant improvements in operational performance and cost savings. This study underscores the potential of integrating machine learning into maintenance practices to achieve greater precision and efficiency in manufacturing settings.