Diesel Engine Fault Diagnosis Using Wavelet Transforms Method Based On LabVIEW Software

Experiment presented in this research, used vibration data obtained from a four-stroke, a295diesel engine. Fault of the internal-combustion engine was detected by using the vibration signals of the cylinder head. The fault diagnosis system was designed and constructed for inspecting the status and fault diagnosis of a diesel engine based on wavelet analysis and LabVIEEW software.The cylinder-head vibration signals were captured through a piezoelectric acceleration sensor that was attached to a surface of the cylinder head of the engine, while the engine was running at three speeds (620,1000and1300rpm) and four loads (0,15,30and45N-m). Data was gathered from five different conditions associated with the cylinder head, such as single cylinder shortage, double cylinders shortage, intake manifold obstruction, exhaust manifold obstruction, and normal condition. Discrete wavelet transforms signal processing method on the engine cylinder head vibration signal with db5and decomposition level5was used to decompose the signal into some of the details and approximations coefficients. Therefore, the energy was extracted from each frequency sub-band of normal and abnormal conditions as a feature of engine fault diagnosis. Thereby, the fault was distinguished by comparing the accumulations of energy in each sub-band of healthy and faulty conditions. The results showed that detection of fault by discrete wavelet analysis is practicable.Statistical parameters as time-domain analysis such as standard deviation "STD", root mean square "RMS" value, peak value "PV", crest factor "CF", shape factor "SF", skewness "SK", and the Kurtosis "KU" were calculated and their values were compared to the normal case, in order to diagnose diesel engine faults. Results showed that, the statistical parameters are more sensitive to the faults.Finally, two techniques, BPNN and SVM were applied to the signal that was collected from the diesel engine head. The experimental results showed that BPNN was more effective in fault diagnosis of the internal-combustion engine, with various fault conditions, than SVM.