Abstract
This paper is devoted to the fault diagnosis of electronic systems by combining logical signals, such as built-in test output, and analog signals, such as voltage, current and temperature. First, the basic inference principles of dependency matrix (D matrix) diagnosis and fuzzy diagnosis are introduced, and the characteristics of their inference operators are summarized. Then, the similarities and differences between the two diagnostic methods are analyzed. Based on the judgement of close degree, a new enhanced inference operator is defined to suit both the D matrix and the fuzzy relation matrix (R matrix). A DR matrix is defined to describe the mixed relationships between faults and the two types of signals. Based on the enhanced inference operator and the DR matrix, a new hybrid diagnostic method is established. Finally, a signal modulating circuit is used to verify the effectiveness of the enhanced inference operator on the D matrix, the R matrix and the DR matrix, which demonstrates the high efficiency of the enhanced inference operator and the feasibility of the new hybrid diagnostic method.
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Y.C. Ao, Y.B. Shi, W. Zhang, Y.J. Li, An approximate calculation of ratio of normal variables and its application in analog circuit fault diagnosis. J. Electron. Test. 29, 555–565 (2013)
M. Awadallah, M. Morcos, Automatic diagnosis and location of open-switch fault in brushless dc motor drives using wavelets and neuro-fuzzy systems. IEEE Trans. Energy Convers. 21, 104–111 (2006)
R. Berrios, F. Núñez, A. Cipriano, Fault tolerant measurement system based on takagi-sugeno fuzzy models for a gas turbine in a combined cycle power plant. Fuzzy Sets Syst. 174, 114–130 (2011)
S.Y. Chang, C.R. Lin, C.T. Chang, A fuzzy diagnosis approach using dynamic fault trees. Chem. Eng. Sci. 57, 2971–2985 (2002)
W.H. Chen, Online fault diagnosis for power transmission networks using fuzzy digraph models. IEEE Trans. Power Deliv. 27, 688–698 (2012)
W.H. Chen, C.S. Yu, Fault diagnosis for distribution substations using fuzzy sagittal mapping analysis. J. Chin. Inst. Eng. 35, 129–140 (2012)
M. Dong, Y. Zhang, Y. Li, M.D. Judd, Fault diagnosis model for power transformers based on information fusion. Meas. Sci. Technol. 16, 1517–1524 (2005)
W.G. Fenton, T.M. McGinnity, L.P. Maguire, Fault diagnosis of electronic systems using intelligent techniques: a review. IEEE Trans. Syst. Man Cybern. Part C: Appl. Rev. 31, 269–281 (2001)
D. Grzechca, T. Golonek, J. Rutkowski, Analog fault AC dictionary creation—the fuzzy set approach, in IEEE International Symposium on Circuits and Systems (2006), pp. 1–4
D. Grzechca, J. Rutkowski, Use of neuro-fuzzy system to time domain electronic circuits fault diagnosis, in ICSC Congress on Computational Intelligence Methods and Applications, (Istanbul, 2005), pp. 1–4
H. Habbi, M. Kidouche, M. Kinnaert, M. Zelmat, Fuzzy model-based fault detection and diagnosis for a pilot heat exchanger. Int. J. Syst. Sci. 42, 587–599 (2011)
H. Han, H.J. Wang, S.L. Tian, N. Zhang, A new analog circuit fault diagnosis method based on improved mahalanobis distance. J. Electron. Test. 29, 95–102 (2013)
S.P. He, F. Liu, Filtering-based robust fault detection of fuzzy jump systems. Fuzzy Sets Syst. 185, 95–110 (2011)
S. Holst, H.J. Wunderlich, Adaptive debug and diagnosis without fault dictionaries. J. Electron. Test. 25, 259–268 (2009)
Z. Lendeka, J. Lauber, T.M. Guerra, R. Babuška, B. De Schutter, Adaptive observers for TS fuzzy systems with unknown polynomial inputs. Fuzzy Sets Syst. 161, 2043–2065 (2010)
W. Li, F. Jiang, Z.C. Zhu, G.B. Zhou, G.A. Chen, Fault diagnosis of bearings based on a sensitive feature decoupling technique. Meas. Sci. Technol. 24, 1–9 (2013)
J.H. Luo, H.Y. Tu, K. Pattipati, L. Qiao, S. Chigusa, Diagnosis knowledge representation and inference. IEEE Instrum. Meas. Mag. 9, 45–52 (2006)
Z.Y. Luo, M. Xiang, H.F. Xie, P. Wang, A method for fault diagnosis of analog circuits based on optimized fuzzy inference, in 2010 Second Pacific-Asia Conference on Circuits Communication System (PACCS), vol. 2 (2010), pp. 8–11
L. Ma, X.B. Zhang, K. Wang, Y.H. Luo, BIT design of hierarchical structure of avionics system. Int. J. Digit. Content Technol. Appl. 6, 58–66 (2012)
L.F. Mendonca, J.M.C. Sousa, J.M.G.S. da Costa, An architecture for fault detection and isolation based on fuzzy methods. Expert Syst. Appl. 36, 1092–1104 (2009)
V. Pasca, L. Anghel, M. Benabdenbi, Kth-aggressor fault (KAF)-based thru-silicon-via interconnect built-in self-test and diagnosis. J. Electron. Test. 28, 817–829 (2012)
A.P. Rotshtein, H.B. Rakytyanska, Diagnosis problem solving using fuzzy relations. IEEE Trans. Fuzzy Syst. 16, 664–675 (2008)
J.W. Sheppard, S.G.W. Butcher, A formal analysis of fault diagnosis with D-matrices. J. Electron. Test. 23, 309–322 (2007)
J.Y. Shi, X.G. Lin, M. Shi, A key metric and its calculation models for a continuous diagnosis capability base dependency matrix. Metrol. Meas. Syst. 19, 509–520 (2012)
M.A. Shukoor, V.D. Agrawal, Diagnostic test set minimization and full-response fault dictionary. J. Electron. Test. 28, 177–187 (2012)
S. Singh, S.W. Holland, P. Bandyopadhyay, Trends in the development of system-level fault dependency matrices, in IEEE Aerospace Conference Proceedings (Big Sky, MT, 2010), pp. 1–9
D. Song, Q. Hu, CQ. Wang, A method for optimum test point selection and fault diagnosis strategy for BIT of avionic system, in IEEE Circuits System International Conference on Testing and Diagnosis (Chengdu, China, 2009), pp. 1–5
D. Wang, P.W. Tse, W. Guo, Q. Miao, Support vector data description for fusion of multiple health indicators for enhancing gearbox fault diagnosis and prognosis. Meas. Sci. Technol. 22, 1–13 (2011)
F.W. Wang, J.Y Shi, L. Wang, Method of diagnostic tree design for system-level faults based on dependency matrix and fault tree, in Proceedings of IEEE International Conference on Industrial Engineering and Engineering Management, part 2 (Changchun, 2011), pp. 1113–1117
L. Wang, J.Y. Shi, X.G. Lin, An extend dependency matrix generation method using structure information, in Proceedings of IEEE Prognostics System Health Management Conference, (Beijing, 2012), pp. 1–6
Q. Wu, Fuzzy fault diagnosis based on fuzzy robust v-support vector classifier and modified genetic algorithm. Expert Syst. Appl. 38, 4882–4888 (2011)
P. Zhao, X.D. Mu, Z.R. Yin, Z.X. Yi, Fault diagnosis of parts of electronic embedded system based on fuzzy fusion approach, in Proceedings of International Workshop on Education Technology and Computer Science (Wuhan, Hubei, 2009), pp. 876–879
X.F. Zhao, Y.B. Liu, X. He, Fault diagnosis of gas turbine based on fuzzy matrix and the principle of maximum membership degree, in International Conference on Future Energy, Environment, and Materials. Energy procedia, vol. 16 (Hong Kong, 2012) pp. 1448–1454
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This research was supported in part by the Major State Basic Research Development Program (61316705), Technology Foundation Program (Z132014B002), and Advanced Research Program (51319040301).
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Shi, JY., Chen, L. & Cui, WW. A New Hybrid Fault Diagnostic Method for Combining Dependency Matrix Diagnosis and Fuzzy Diagnosis Based on an Enhanced Inference Operator. Circuits Syst Signal Process 35, 1–28 (2016). https://doi.org/10.1007/s00034-015-0047-z
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DOI: https://doi.org/10.1007/s00034-015-0047-z