Machine Learning Applications in Predictive Maintenance: A Focus on Clutch Failures

Abstract

This paper aims to present an overview of predictive maintenance in the automotive industry, focusing on machine learning (ML) techniques predicting failure of the Clutches. The paper also aims to address the pros and cons of such an approach for business. How it has and will impact the automotive industry in the coming future. Predictive maintenance is an important aspect of the automotive industry as it enables the proactive identification of potential failures in equipment and systems, reducing the risk of downtime and improving overall efficiency.
In recent years, machine learning techniques have emerged as powerful tools for predictive maintenance (PdM), enabling the development of more accurate and efficient predictive models. The paper will provide an overview of the various machine-learning techniques used in predictive maintenance for vehicle Clutch damage prediction. This research includes regression models, decision trees, and neural networks. Additionally, it will explore the challenges and opportunities associated with the implementation of predictive maintenance using machine learning in the automotive industry, including data quality, class imbalance, model interpretation, and organizational buy-in.
Additionally, the paper will present some case studies of predictive maintenance in the automotive industry that have successfully utilized machine learning techniques, highlighting the business benefits and potential of this approach. Moreover, our research
highlights various instances of predictive maintenance implementation within the industry, providing insightful and pertinent content for senior executives at manufacturing and transportation companies. These decision-makers can gain valuable knowledge about the advantages of predictive maintenance solutions and gain insight into the advancements made by their counterparts in this field. This paper contributes to the field of PdM by identifying and discussing significant research gaps in the field. Our analysis of the current literature highlights the need for further research in this area, and we propose several avenues for future investigation.