Research Article | OPEN ACCESS
Research on Fault Diagnosis System of a Diesel Engine Based on Wavelet Analysis and LabVIEW Software
1, 2Eidam Ahmed Hebiel, 1Zhu Zhou, 1Dong Sheng Wang, 1Liu Jie, 1Mohamed Ahmed Elbashier, 1Wen Dongdong, 1Li Peng and 1Li Xiaoyu
1Department of Agricultural Engineering Automation and Measurement Technology Research Section, College of Engineering, Huazhong Agricultural University, Wuhan, China
2Department of Agricultural Engineering, Faculty of Natural Resources and Environmental Studies, University of Western Kordofan, Elnahoud, Sudan
Research Journal of Applied Sciences, Engineering and Technology 2014 18:3821-3836
Received: November 04, 2013 | Accepted: November 13, 2013 | Published: May 10, 2014
Abstract
Experiment presented in this study, used vibration data obtained from a four-stroke, 295 diesel 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 discrete wavelet analysis and LabVIEW 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 two speeds (620 and 1300 rpm) and two loads (15 and 45 N·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. After decomposing the vibration signals into some of the details and approximations coefficients with db5 mother wavelet and decomposition level 5, the energies were extracted from each frequency sub-band of healthy and unhealthy conditions as a feature of engine fault diagnosis. By doing so, normal and abnormal conditions behavior could be effectively distinguished by comparing the energy accumulations of each sub-band. The results showed that detection of fault by discrete wavelet analysis is practicable. Finally, two techniques, Back-Propagation Neural Network (BPNN) and Support Victor Machine (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.
Keywords:
Diesel engine, fault detection, internal-combustion engine, LabVIEW, vibration signal, wavelet analysis,
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Competing interests
The authors have no competing interests.
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