Predictive Maintenance in Wheelbase Assembly Using Python

Abstract

In ten years of the life cycle of automobiles, 6% of the time is spent on service or maintenance. Maintenance is often done in earlier stages, without utilizing the part's full potential. This results in an unwanted increase in the overall cost of owning a vehicle. By using predictive maintenance, the cost of automobile maintenance can be reduced by more than 50 percent. With the help of Predictive maintenance, unexpected failures can be averted. Required data is obtained from sensors, and a dataset is made from collected data. Then machine learning techniques and models are employed such that the failure of the product is predicted. In this work, we focused on predicting the malfunctions that are caused during the maintenance which can impact the client with vibrations of wheelbase as the particular use case.