Intelligent Mechanisms for PdM in Automotive Machinery: A Comprehensive Analysis using ML/DL

Abstract

Predictive maintenance (PdM) technique involves analyzing and utilizing data to identify problems before they happen. It can help prevent costly repairs and downtime. In the past few years, the use of intelligent tools for PdM in automotive machinery has been increasing. These tools can be used to analyze and collect data from various sources, such as cloud computing and sensors. In the prediction of failures, this data can be used in combination with machine learning algorithms. With the help of advanced technologies, such as machine learning and sensors, PdM has become a viable option to maintain machinery while minimizing costs and downtime. The paper presents a comprehensive analysis of the various components of the intelligent tools that are used for PdM. It starts by exploring the different kinds of sensors and their functions in monitoring the condition of the equipment. The paper then explores the synergistic relationship between machine learning and data analytics, demonstrating how these technologies can help identify potential issues, predict the remaining useful life of the equipment, and detect early anomalies. The paper reviews the literature on the use of intelligent tools and sensors for PdM in automotive machinery. It delves into the diverse kinds of mechanisms that have been employed for this type of PdM, the pros and cons of using such tools, as well as the possible directions in this domain. Despite the various challenges that have been presented, the potential of implementing intelligent tools in automotive machinery is still immense. They can help prevent equipment downtime and improve the safety and efficiency of the operations of the machinery. As the technology matures, we can expect the adoption of such mechanisms to increase. The report emphasizes the significant contribution of intelligent tools and sensors to the optimization of the maintenance schedules and the reduction of unplanned downtime in automotive machinery. The findings of this study provide a roadmap for practitioners, researchers, and industrial organizations looking to harness the potential of such mechanisms to guarantee the longevity of their assets.