EngineFaultDB: A Novel Dataset for Automotive Engine Fault Classification and Baseline Results

Abstract

This paper introduces EngineFaultDB, a novel dataset capturing the intricacies of automotive engine diagnostics. Centered around the widely represented C14NE spark ignition engine, data was collected under controlled laboratory conditions, simulating various operational states, including normal and specific fault scenarios. Utilizing tools such as an NGA 6000 gas analyzer and a USB 6008 data acquisition card from National Instruments, we were able to monitor and capture a comprehensive range of engine parameters, from throttle position and fuel consumption to exhaust gas emissions. Our dataset, comprising 55,999 meticulously curated entries across 14 distinct variables, provides a holistic picture of engine behavior, making it an invaluable resource for automotive researchers and practitioners. For evaluation, several classifiers, including logistic regression, decision trees, random forests, support vector machines, k-nearest neighbors, and a feed-forward neural network, were trained on this dataset. Their performance, under standard configurations and a simple neural network architecture, offers foundational benchmarks for future explorations. Results underscore the dataset’s potential in fostering advanced diagnostic algorithms. As a testament to our commitment to open research, EngineFaultDB is freely available for academic use. Future work involves expanding the dataset’s diversity, exploring deeper neural architectures, and integrating real-world automotive conditions.

Keywords

• Engine fault
• automotive engine
• spark ignition engine
• fault classification
• machine learning
• deep learning