A key metric and its calculation models for a continuous diagnosis capability based on a dependency matrix

• Autorzy: Shi J.-Y. , Lin X.-G. , Shi M.
• Języki publikacji: PL

Abstrakty

EN

This paper is devoted to measuring the continuous diagnosis capability of a system. A key metric and its calculation models are proposed enabling us to measure the continuous diagnosis capability of a system directly without establishing and searching the sequential fault tree (SFT) of the system. At first a description of a D matrix is given and its metric is defined to determine the weakness of a continuous diagnosis. Then based on the definition of a sequential fault combination, a sequential fault tree (SFT) is defined with its establishment process summarized. A key SFT metric is established to measure the continuous diagnosis capability of a system. Two basic types of dependency graphical models (DGMs) and one combination type of DGM are selected for characteristics analysis and establishment of metric calculation models. Finally, both the SFT searching method and direct calculation method are applied to two designs of one type of an auxiliary navigation equipment, which shows the high efficiency of the direct calculation method.

Słowa kluczowe

EN

fault diagnosis; dependency matrix; measurement

• Wydawca: Komitet Metrologii i Aparatury Naukowej PAN
• Czasopismo: Metrology and Measurement Systems
• Rocznik: 2012
• Tom: Vol. 19, nr 3
• Strony: 509--520
• Opis fizyczny: Bibliogr. 24 poz., rys., tab.

Twórcy

autor

Shi J.-Y.

autor

Lin X.-G.

autor

Shi M.

• School of Reliability and System Engineering, Beihang University, Xueyuan Road 37, 100191 Beijing, China,

Bibliografia

• [1] Emment, G. (2010). Method for improving design testability through modelling. In Proc. IEEE AUTOTESTCON, 1-4.
• [2] Ma, F., Song, D. (2010). A new testability model of electronic system and its application. In Proc. 2010 International Conference on Computer Application and System Modelling (ICCASM), 10, 516-520.
• [3] Shi, J.Y., Gong, J.J. (2010). Improvement method for testability modelling with multiple faults. Journal of Beijing University of Aeronautics and Astronautics, 36(30), 270-273.(in Chinese)
• [4] Temple, G., Jize, N. (2005). Testability modelling and analysis of a rocket engine test stand. In Proc. IEEE Aerospace Conference, 3874-3895.
• [5] Rao, N.S.V. (1996). On parallel algorithms for single-fault diagnosis in fault propagation graph systems. IEEE Trans. Parallel and Distributed Systems, 7(12), 1217-1223.
• [6] Filippetti, F., Artioli, M. (2000). Single and multiple fault diagnosis based on symbolic analysis and reduced set of observable points for linear analogue circuits. In Proc. 7th IEEE International Conference on Electronics, Circuits and Systems (ICECS), 433-436.
• [7] Anita, J.P., Vaathi, P.T. (2010). Multiple fault diagnosis with improved diagnosis resolution for VLSI circuits. In Proc. 2010 International Conference on Computer Communication and Networking Technologies (ICCCNT), 1-6.
• [8] Kundu, S., Chattopadhyay, S. (2011). Multiple fault diagnosis based on multiple fault simulation using particle swarm optimization. In Proc. 24th International Conference on VLSI Design, 364-369.
• [9] Kleer, J.D., Williams, B.C. (1987). Diagnosing multiple faults. Artif. In-tell.
• [10] Simpson, W.R., Sheppard, J.W. (1994). Multiple failure diagnosis. In Proc. of the IEEE AUTOTESTCON, 381-389.
• [11] Shakeri, M., Pattipati, K.R. (1998). Optimal and near-optimal algorithms for multiple fault diagnosis with unreliable tests. IEEE Trans. SMC, 28(3), 431-443.
• [12] Fang, T., Pattipati, K.R. (2003). Computationally efficient algorithms for multiple fault diagnosis in large graph-based systems. IEEE Trans. SMC, 33(1), 73-82.
• [13] Tung, L., Hadjicostis, C.N. (2008). Max-product algorithms for the generalized multiple-fault diagnosis problem. IEEE Trans. Systems, Man, and Cybernetics, Part B, Cybernetics, 3(3), 1607-1621.
• [14] Tadeusiewicz, M., Hałgas, S. (2005). Multiple fault diagnosis in analogue circuits.In Proc. of the 2005 European Conference on Circuit Theory and Design, 205-208.
• [15] Wang, Z., Marek-Sadowska, M. (2003). Multiple fault diagnosis using n-detection tests. In Proc. of 21st International Conference on Computer Design, 198-201.
• [16] Anita, J.P., Vaathi, P.T. (2010). Multiple fault diagnosis with improved diagnosis resolution for VLSI circuits. In Proc. 2010 International Conference on Computing Communication and Networking Technologies (ICCCNT), 1-6.
• [17] Grzechca, D., Rutkowski, J. (2009). Fault diagnosis in analogue electronic circuits - the SVM approach. Metrol. Meas. Syst., 16(4), 583-597.
• [18] Lu, Y., Gan, Z.H. (2010). Research and implementation of a multi-fault diagnosis method based on case-based reasoning. In Proc. 8th World Congress on Intelligent Control and Automation (WCICA), 5737-5741.
• [19] Luo, Z.G., Chen, Q. (2010). Multi-fault diagnosis of gear based on sequential fuzzy inference. In Proc. 2010 International Conference on Mechanic Automation and Control Engineering (MACE), 2492-2496.
• [20] Grzechca, D. (2011). Soft fault clustering in analogue electronic circuits with the use of self organizing neural network. Metrol. Meas. Syst., 18(4), 555-568.
• [21] Lin, Y.C., Lu, F. (2007). Multiple-fault diagnosis based on adaptive diagnostic test pattern generation. IEEE Trans. Computer-Aided Design of Integrated Circuits and Systems, 26(5), 932-942.
• [22] Tadeusiewicz, M., Hałgas, S. (2011). Multiple soft fault diagnosis of nonlinear DC circuits considering component tolerances. Metrol. Meas. Syst., 18(3), 349-360.
• [23] Shi, J.Y., Lin, X.G., Lv, K.Y. (2012). A method for searching and evaluating diagnosable sequence fault sets of a dependency matrix. In Proc. IEEE Conference on Prognostics & System Health Management, 1-7.
• [24] Singh, S., Holland, S.W., Bandyopadhyay, P. (2010). Trends in the development of system-level fault dependency matrices. In Proc. IEEE Aerospace Conference, 1-9.