Fuzzy Interference Referee Model of Fault Diagnoses for Automobile Engine

Abstract:

According to the engine condition and the complexity, uncertainty and nonlinear of the corresponding parameters, it puts forward an engine failure evaluation model based on the comprehensive decision-making fuzzy inference. This method takes all the corresponding characteristic parameters such as engine oil, vibration, and performance parameters as the study factors. By analyzing the sensitivity degree of these parameters to the change of the engine and its trend over time, different membership functions will be established based on various factors by means of statistical analysis and inference. Entropy-based data fusion and expertise can determine the levels of weight distribution of each factor in the comprehensive evaluation, thus, the engine failure evaluation model based on the comprehensive decision-making fuzzy inference can be established.

• Periodical: Applied Mechanics and Materials (Volumes 138-139)
• Pages: 321-326
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.138-139.321
• Online since: November 2011
• Authors: Zhe Wang, Li Fang Kong, Wei Kui Wang
• Keywords: Data Fusion, Entropy, Fault Diagnosis, Fuzzy Inference

References

[1] Liang Jiuzhen. Functional Procedure Neural Network[J]. Dynamic of Continuous Discrete and Impulsive Systems-Series B-Applications & Algorithms, 2005, Sp. Iss. SI, 1: 27-31.

[2] Zheng， H.Z. Li， and X. Chen， Gear fault diagnosis based on continuous wavelet transform. Mechanical System and Signal Processing， 2002，16(2-3): 447~457.

DOI: 10.1006/mssp.2002.1482

[3] Nikolaou， N.G. and I.A. Antoniadis， Rolling element bearing fault diagnosis using wavelet packets. Ndt& E International，2002，35(3): 197~205.

DOI: 10.1016/s0963-8695(01)00044-5

[4] Twiddle， J. A， and N. B Jones， Fuzzy model –based condition monitoring and fault diagnosis of a diesel engine cooling system， Procceedings of the Institution of Mechanical Engineers Part 1-Journal of Systems and Control Engineering ，2002，216(13): 215~224.

DOI: 10.1177/095965180221600301

[5] Zweiri， Y. H. ，J. F. Whidbone， and L. D. Seneviratne， Detailed analytical model of a single-cylinder diesel endg in the crank angle domain，Processing of the Institution of Mechanical Engineers Part D-Journal of Automobile Engineering ，2001， 215(D11): 1197~1216.

DOI: 10.1243/0954407011528734

[6] K. Logan， B. Inozu， P. Roy， J.F. Helet，P. Chesse， and X. Tauzia， Real-time marine diesel engine simulation for fault diagnosis， Marie Technology and S name News， 2002， 39: 21~28.

DOI: 10.5957/mt1.2002.39.1.21

[7] N. Lawrence and H. Y. P. Kortekaas， DECSIM-A PC-based diesel engine cycle and cooling system simulation program， Mathematical and Computer Modeling ，2001，33: 65~575.

DOI: 10.1016/s0895-7177(00)00262-4

[8] Liang Jiuzhen, Gaojianghua. Kernel Function Clustering Algorithm Optimized Parameters[A]. In: The Forth International Conference on Machine Learning and Cybernetics[C]. Guangzhou, China, 2005, 7: 4400-4404.

DOI: 10.1109/icmlc.2005.1527713

[9] Liang Jiuzhen, Han Jianmin. Complex Number Procedure Neural Networks[A]. In: The First International Conference on Natural Computation[C]. Changsha, China, 2005. Proceedings: Springer Press, 2005, 1: 336-339.

DOI: 10.1007/11539087_40

[10] Liu Wei, Gao Wei-cheng, Sun Yi, Xu Min-jian. Optimal sensor placement for spatial lattice structure based on genetic algorithms[J]. Joural of sound and viberation. 2008. 317(5), 175-189.

DOI: 10.1016/j.jsv.2008.03.026

[11] Pacheco J, Casado S, Nez L. A variable selection method based on tabu search for logistic regression models. European Journal of Operational Research, 2009, 199 (2): 506-511.

DOI: 10.1016/j.ejor.2008.10.007