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1、Experiment presented in this research, used vibration data obtained from a four-stroke, a 295 diesel engine.Fault of the internal-combustion engine was detected by using the vibration signals of the cylinder head.The fau
2、lt diagnosis system was designed and constructed for inspecting the status and fault diagnosis of a diesel engine based on wavelet analysis and LabVIEW software.
The cylinder-head vibration signals were captured thr
3、ough 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, 1000 and 1300 rpm) and four loads (0, 15, 30 and 45 N·m).Data
4、 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
5、.Discrete wavelet transforms signal processing method on the engine cylinder head vibration signal with db5 and decomposition level 5 was used to decompose the signal into some of the details and approximations coefficie
6、nts.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
7、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 "ST
8、D", 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
9、 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 ex
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