Prediction of Vehicle Clutch Breakdown Based On Multiple Linear Regression
The clutch plays a significant role in all automobile vehicle operations. Breakdown of clutch for any vehicle directly affects the vehicle in operation and could impact on human safety. Many reasons could be the identity for clutch breakdown, for example, a vehicle carrying excessive weight, continuously engage of the clutch in the city traffic, and malpractices with the gear operations. Predicting the breakdown of the clutch is a high priority requirement which is currently not achieved through vehicle diagnosis. In this paper, the author is discussing multiple regression analysis to predict the clutch life with the help of numerous vehicle parameters such as transmission oil temp, vehicle speed, vehicle torque, vehicle engine speed, transmission oil level, accelerometer pedal position, parking brake status, contamination of oil. Consideration of multiple parameters contributes to increasing the accuracy of the prediction output. The detailed mathematical model and resultant graphs of the machine learning model has discussed in this paper. The predicted variable with the different status is defined, which can be easily inputted to any of the monitor display tools to convey the information in the Lehman language.