Fault Detection and Diagnosis of Engine Spark Plugs Using Deep Learning Techniques

Fault Detection and Diagnosis (FDD) is playing an increasingly important role in the automotive sector as it moves toward Advanced Technology Vehicles. Reducing the cost of sensory equipment to detect faults in Internal Combustion Engines (ICEs) has always been a common desire for automotive researchers. This article offers an Artificial Intelligence approach for detecting engine combustion faults related to spark plugs using existing sensors. The study investigates two deep learning models that are capable of learning different fault conditions from historical sensory data. The two customized models, one Long Short-Term Memory (LSTM) neural network and one Convolutional Neural Networks (CNN) model, are proposed to tackle this task. The LSTM model processes the filtered sensor data in time series, while the CNN model uses the frequency map that is novel in the learning-based engine diagnosis field. A comprehensive engine fault dataset is collected and includes a variety of operating conditions in relation to engine speed, engine load, and test time. Evaluation results using this dataset show successful detection of the fault conditions with high accuracy. In the meantime, the results also reveal some unstable performance outside of given operating conditions.