A Novel Method for Breakdown Prediction of Vehicle Clutch Using Multiple Linear Regression

Abstract

The clutch is an important component of the transmission system in all aspects of car vehicle operation. The failure of a clutch in any vehicle has a direct impact on the vehicle's operation and, in some cases, on human safety as well. There are a variety of factors that might contribute to a clutch failure, including an overloaded vehicle, the use of the clutch in city traffic on a constant basis, and mistakes made during the gear shifting process. A high-priority demand is the ability to predict clutch failure, which is currently not achievable through vehicle diagnosis. This positional paper contributes to the use of multiple regression analysis technique to predict clutch life with the help of numerous vehicle parameters such as transmission oil temperature, vehicle speed, vehicle torque, vehicle engine speed, transmission oil level, accelerometer pedal position, parking brake status, and oil contamination. The consideration of many parameters adds to enhancing the accuracy of the forecast output by expanding the number of parameters considered. The proposed system's performance, which has an accuracy of 94 percent, is considered satisfactory. This technology can be used to notify drivers in Lehman language about projected consequences depending on the information provided by the system.