Feature extraction using wavelet analysis with application to machine fault diagnosis

Two different approaches have been used to diagnose faults in machinery such as internal combustion engines. In the first approach, a mathematical model of the specific engine or component under investigation is developed and a search for causes of change in engine performance is conducted based on the observations made in the system output. In the second approach, the specific engine or component is considered a black box. Then, by observing some sensory data, such as cylinder pressure, cylinder block vibrations, exhaust gas temperatures, and acoustic emissions, and analyzing them, fault(s) can be traced and detected. In this research the latter approach is employed in which vibration data is used for the detection of malfunctions in reciprocating internal combustion engines. The objective of this thesis is to develop effective data-driven methodologies for fault detection and diagnosis. The main application is the detection and characterization of combustion related faults in reciprocating engines; faults such as knock, improper ignition timing, loose intake and exhaust valves, and improper valve clearances. To perform fault diagnosis in internal combustion engines, cylinder head vibration data are used for characterizing the underlying mechanical and combustion processes. Fault diagnosis includes two main stages: feature extraction and classification. In the feature extraction stage, we have utilized wavelets for the analysis of acceleration data acquired at the cylinder head to capture meaningful features that include necessary information about the state of the engine. Wavelets have shown to provide suitable signal