Data Analysis for Automobile Brake Fluid Fill Process Leakage Detection using Machine Learning Methods

Abstract

During the production of an automobile, various fluids such as steering fluid, brake fluid, radiator coolant, etc. that are required for the operation of an automobile, are filled to the vehicle via a specific process. Any problems such as leakage in the fluids systems should be identified during the filling process and necessary corrections must be made to the automobile before it goes forward in the production line. The fluid filling process consists of vacuuming step followed by filling step. This paper provides results of our research on the brake fluid system quality based on the sensor data, which is recorded during filling process. The filling dataset contains two time series data corresponding to the vacuuming and filling steps. First, we use this raw data to construct a dataset with 1-faulty/0-Not-faulty labels. Later we use this dataset to construct machine learning models, with classical methods, and convolutional neural network models. Results show that gradient boosting methods are better with the current settings, and we have improvement opportunities related to convolutional neural network architectures.