Journal of Modern Mechanical Systems and Machining ISSN: 3139-5066 (Online)

Abstract

Predictive maintenance has become an important strategy in modern manufacturing systems. Traditional maintenance practices such as corrective maintenance and preventive maintenance often lead to unnecessary downtime or unexpected machine failures. With the development of machine learning and industrial data collection systems, predictive maintenance algorithms are now widely used to predict equipment failures before they occur. These algorithms analyze historical and real time data obtained from sensors, machines and production systems to estimate the health condition of equipment. Machine learning techniques such as regression models, neural networks, decision trees and support vector machines help in identifying patterns related to machine degradation. This paper presents a review of predictive maintenance algorithms using machine learning techniques. The study discusses data acquisition methods, data preprocessing, different machine learning algorithms used for failure prediction and their applications in industrial environments. Advantages and limitations of these algorithms are also analyzed. Some examples of industrial implementations are discussed to show how predictive maintenance improves equipment reliability, reduces operational cost and increases production efficiency. The paper also includes tables summarizing different algorithms and their characteristics. Finally, future research directions are presented for improving predictive maintenance systems in smart manufacturing environments.

Keywords: Predictive maintenance, Machine learning, Industrial data analytics, Failure prediction, Smart manufacturing