A novel deep copy stacked ensemble optimization technique for optimal predictive maintenance of air compressors

Eshita Ojha, Nonita Sharma, Monika Mangla

Abstract: Component or system failures are inevitable in the industrial sector, leading to production downtime and significant financial losses. Consequently, each manufacturing sector requires an automated and efficient predictive maintenance system to monitor operations and predict potential failure points. Timely identification of these failure points facilitates prompt repairs, thereby substantially reducing maintenance costs. In this research, the authors aim to develop an effective model for the predictive maintenance of air compressors, which are critical components in various industries, including automotive, aerospace, chemical manufacturing, electronics, and general manufacturing. To achieve this, the authors employ a deep copy stacking classifier that utilizes an ensemble model composed of Logistic Regression, Decision Tree, Support Vector Machine, K-Nearest Neighbors, and Naive Bayes as base models to assess the bearing status of the components. The experimental evaluation of the proposed model demonstrates impressive performance metrics, achieving accuracy, precision, and recall rates of 99.3%, 96.7%, and 100%, respectively. This enhanced precision enables the model to detect potential component failures in a timely manner, thereby improving predictive accuracy and robustness. The advancements presented in this study pave the way for the widespread implementation of the proposed model in real-world applications.