New Intelligent Condition Monitoring and Fault Diagnosis System for Diesel Engines Using Embedded System

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Diesel engines are power source of various vehicles. Their complex structures make it difficult to detect pending faults in a timely manner. In fact, simultaneous existence of different faults is frequently found in diesel engines. To ensure the security of diesel engines, it is involved with two issues. One is the on-line condition monitoring, and the other is the intelligent fault diagnosis. However, the investigations have been addressed to one of the two problems extensively in previous work while very limited work has been done to develop an on-line intelligent condition monitoring and fault diagnosis (CMFD) system. For this reason, a new intelligent CMFD system is proposed to on-line analyze the vibration signals using the Hessian-based locally linear embedding (HLLE) and support vector machine (SVM) in this work to detect the diesel engine faults and to prevent the malfunction of the machines. The vibration signals were firstly recorded on-line by an embedded system. Then the HLLE was used to reduce the dimensions of original vibration signals to extract useful fault features, and lastly the SVM was adopted for faults detection. Experimental tests have been implemented to evaluate the performance of the on-line intelligent CMFD system. The diagnosis results demonstrate that the proposed method is very effective for the fault diagnosis of diesel engines. The faults can be detected on-line with a high rate of 90%

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Periodical:

Edited by:

Yuning Zhong

Pages:

408-412

Citation:

H. F. Xu "New Intelligent Condition Monitoring and Fault Diagnosis System for Diesel Engines Using Embedded System", Applied Mechanics and Materials, Vol. 235, pp. 408-412, 2012

Online since:

November 2012

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$38.00

[1] Z. Li, X. Yan, C. Yuan, Z. Peng, L. L, Virtual prototype and experimental research on gear multi-faults diagnosis using wavelet-autoregressive model and principal component analysis method, Mechanical Systems and Signal Processing, 25 (2011).

DOI: https://doi.org/10.1016/j.ymssp.2011.02.017

[2] Z. Li, X. Yan, C. Yuan, Z. Peng, A new intelligent fusion method of multi-dimensional sensors and its application to tribo-system fault diagnosis of marine diesel engines, Tribology Letters, 47 (2012) 1–15.

DOI: https://doi.org/10.1007/s11249-012-9948-1

[3] Z. Li, X. Yan, Y. Jiang, L. Qin, J. Wu, A new data mining approach for gear crack level identification based on manifold learning, Mechanika, 18 (2012) 29–34.

DOI: https://doi.org/10.5755/j01.mech.18.1.1276

[4] Z. Li, X. Yan, C. Yuan, J. Zhao, Z. Peng, Fault detection and diagnosis of the gearbox in marine propulsion system based on bispectrum analysis and artificial neural networks, Journal of Marine Science and Application, 10 (2011) 17–24.

DOI: https://doi.org/10.1007/s11804-011-1036-7

[5] Z. Li, X. Yan, C. Yuan, Z. Peng, Intelligent fault diagnosis method for marine diesel engines using instantaneous angular speed, Journal of Mechanical Science and Technology, 26 (2012) 1–11.

DOI: https://doi.org/10.1007/s12206-012-0621-2

[6] Z. Li, X. Yan, Z. Tain, C. Yuan, Z. Peng, Blind vibration component separation and nonlinear feature extraction applied to the nonstationary vibration signals for the gearbox multi-fault diagnosis, Measurement, 2012, http: /dx. doi. org/10. 1016/j. measurement. 2012. 06. 013.

DOI: https://doi.org/10.1016/j.measurement.2012.06.013

[7] V. Sugumaran and K. Ramachandran, Effect of number of features on classification of roller bearing faults using SVM and PSVM, Expert Systems with Applications, 38 (2011) 4088–4096.

DOI: https://doi.org/10.1016/j.eswa.2010.09.072

[8] W. Xu and L. Zhang, On-line fault diagnosis system for network communication of digital substation, Electric Power Automation Equipment, 30 (2010) 121–125.

[9] R. Chen, PSO based on-line fault diagnosis approach for power electronic systems, Proceedings of the Chinese Society of Electrical Engineering, 28 (2008) 70–74.

[10] M. Hu, On-line fault diagnosis of the injectors based on genetic algorithm optimized fuzzy neural network, Journal of Vibration, Measurement and Diagnosis, 31 (2011) 464–467.

[11] C. Zhao, X. Sun, S. Sun, T. Jiang, Fault diagnosis of sensor by chaos particle swarm optimization algorithm and support vector machine, Expert Systems with Applications, 38 (2011) 9908–9912.

DOI: https://doi.org/10.1016/j.eswa.2011.02.043

[12] T. Xia, X. Wang, H. Zhao, S. Liang, Extracting fault features of a diesel engine's crankshaft bearing based on high-order cumulation, Journal of Vibration and Shock, 30 (2011) 77–80.

[13] S. Delvecchio, G. D'Elia, E. Mucchi, G. Dalpiaz, Advanced Signal Processing Tools for the Vibratory Surveillance of Assembly Faults in Diesel Engine Cold Tests, Journal of Vibration and Acoustics, Transactions of the ASME, 132 (2010).

DOI: https://doi.org/10.1115/1.4000807

[14] Y. Xiao, H. Li, B. Wang, G. Cheng, W. Zhang, A. Si, Bispectrum analysis of fault diagnostics of piston-pin of diesel engine, Transactions of CSICE, 26 (2008) 369–372.

[15] M. Maurya, P. Paritosh, R. Rengaswamy, V. Venkatasubramanian, A framework for on-line trend extraction and fault diagnosis, Engineering Applications of Artificial Intelligence, 23 (2010) 950–960.

DOI: https://doi.org/10.1016/j.engappai.2010.01.027

[16] S. Li and D. Zhang, Distributed localization based on hessian local linear embedding algorithm in wireless sensor networks, Information Technology Journal, 6 (2007) 885–893.

DOI: https://doi.org/10.3923/itj.2007.885.890

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