Fault Detection and Diagnosis of a Diesel Engine Valve Train

Abstract

One of the most commonly used mechanical systems is the internal combustion engine. Internal combustion engines dominate the automotive industry, and have numerous other applications in generation, transportation, etc. This thesis presents the development of a fault detection and diagnosis (FDD) system for use with an internal combustion engine valve train. A FDD system was developed with a focus on the valve impact amplitudes. Engine cycle averaging and band-pass filtering methods were tuned and utilized for improving the signal to noise ratio. A novel feature extraction method was developed that included a local RMS sliding window method and an adaptive threshold. Faults were seeded in the form of deformed valve springs, as well as abnormal valve clearances. The engine’s manufacturer specifies that a valve spring with 3 mm or more of deformation should be replaced. This thesis investigated the detection of a relatively small 0.5mm spring deformation. Valve clearance values were adjusted 0.1mm above and below the nominal clearance value (0.15mm) to test large clearance faults (0.25mm) and small clearance faults (0.05mm). The performance of the FDD system was tested using an instrumented diesel engine test bed. A comparison of numerous signal processing techniques and classification methods was performed.