Fault Diagnosis and Prediction in Automotive Systems with Real-Time Data Using Machine Learning

In the automotive industry, a Malfunction Indicator Light (MIL) is commonly employed to signify a failure or error in a vehicle system. To identify the root cause that has triggered a particular fault, a technician or engineer will typically run diagnostic tests and analyses. This type of analysis can take a significant amount of time and resources at the cost of customer satisfaction and perceived quality. Predicting an impending error allows for preventative measures or actions which might mitigate the effects of the error. Modern vehicles generate data in the form of sensor readings accessible through the vehicle’s Controller Area Network (CAN). Such data is generally too extensive to aid in analysis and decision making unless machine learning-based methods are used. This paper proposes a method utilizing a recurrent neural network (RNN) to predict an impending fault before it occurs through the use of CAN data. Methods to pre-process the vehicle data for dimensionality reduction are also presented. The RNN utilizes the processed data through long short-term memory (LSTM) to learn the system variables and user inputs that contribute to the error over time. We present our approach on the data collected from the A/C compressor system of a production vehicle highlighting the benefits of the proposed approach. Avenues for future work are also identified.