Feature Extraction and Machine Learning Classifier Development for Fault Detection and Diagnosis

Abstract

In this research, a fault detection and diagnosis strategy for internal combustion engine is developed using measurements that are readily available in engine testing environment to monitor abnormal combustion. The FDD strategy is designed to monitor the engine on a cycle-by-cycle basis using measurements that are accessible on a running vehicle. Pressure measurements are easily accessible in a testing facility that provide useful insight into the quality of the combustion occurring inside the engine. However, due to its cost and complex installation procedures, it is not feasible to obtain in-cylinder pressure measurements from an in-vehicle engine. Faults of a mechanical system are often investigated using vibration. Due to the low cost and non-invasive nature of accelerometers, vibration measurement is used to monitor the in-vehicle engine. However, as vibration behaviors of complex system such as an engine is hard to characterize, in cylinder pressure measurement is used during the development of the FDD strategy to assist in characterizing the vibration measurement. Upon data acquisition, features are extracted from the vibration measurements using Extended-MSPCA for better characterization and data reduction with a multi-baseline technique. Pressure measurements are analyzed using thermodynamic theories to assess the combustion quality of each cycle. The vibration measurements are labelled corresponding to the pressure analysis. An artificial neural network classifier is developed using the extracted and labelled features. Developed classifier detected the fault and its location with an overall accuracy of 96.3%.