A Framework to Debug Diagnostic Matrices



Published Oct 14, 2013
Anuradha Kodali Peter Robinson Ann Patterson-Hine


Diagnostics is an important concept in system health and monitoring of space operations. Many of the existing diagnostic algorithms utilize system knowledge in the form of diagnostic matrix (D-matrix, also popularly known as diagnostic dictionary, fault signature matrix or reachability matrix). The D-matrix maps tests on observed conditions to failures. This matrix is mostly gleaned from physical models during system development. But, sometimes, this may not be enough to obtain high diagnostic performance during operation due to system modifications and lag and noise in sensor measurements. In such a case, it is important to modify this D-matrix based on knowledge obtained from sources such as time-series data stream (simulated or maintenance data) within a framework that includes the diagnostic/inference algorithm. A systematic and sequential update procedure, diagnostic modeling evaluator (DME) is proposed to modify D-matrix and wrapper/test logic considering the least expensive update first. The user sets the diagnostic performance criteria. This iterative procedure includes conditions ranging from modifying 0’s and 1’s in the matrix, adding/removing the rows (failure sources)/columns (tests), or modifying test/wrapper logic used to determine test results. We will experiment this framework on ADAPT datasets from DX challenge 2009.

How to Cite

Kodali, A. ., Robinson, P., & Patterson-Hine, A. . (2013). A Framework to Debug Diagnostic Matrices. Annual Conference of the PHM Society, 5(1). https://doi.org/10.36001/phmconf.2013.v5i1.2278
Abstract 203 | PDF Downloads 85




Luo, J., & Pattipati, K. (2007). An integrated diagnostic development process for automotive engine control systems. IEEE Trans. Syst., Man, Cybern. C, vol. 37, no. 6, pp. 1163–1173, Nov. 2007.

Singh, S., Kodali, A., Choi, K., Pattipati, K., Namburu, S., Chigusa, S., Prokhorov, D.V., & Qiao, L. (2009). Dynamic multiple fault diagnosis: Mathematical formulations and solution techniques. IEEE Trans. Syst., Man, Cybern. A, vol. 39, no. 1, pp. 160–176.

Singh, S., Holland, S., & Bandyopadhyay, P. (2011). Trends in the development of system-level fault dependency matrices. IEEE Aerospace Conference, Big Sky, Montana.

Luo, J., Tu, H., Pattipati, K., Qiao, L., & Chigusa, S. (2006). Graphical models for diagnostic knowledge representation and inference. IEEE Instrum. Meas. Mag., vol. 9, no. 4, pp. 45–52.

Qualtech Systems Inc., www.teamqsi.com.
Simpson, W., & Sheppard, J. (1992). System Testability Assessment for Integrated Diagnostics. IEEE Des. Test. Comput., vol. 9, no. 1, pp. 40-54.

Kodali, A., Singh, S., & Pattipati, K. (2013). Dynamic set covering for real-time multiple fault diagnosis with delayed test outcomes. IEEE Trans. Syst., Man, Cyben. A, vol. 43, no. 3, pp. 547-562.

Kodali, A., Pattipati, K., & Singh, S. (2013). Coupled factorial hidden Markov models (CFHMM) for diagnosing multiple and coupled faults. IEEE Trans. Syst., Man, Cyben. A, vol. 43, no. 3, pp. 522-534.

Narasimhan, S., Balaban, E., Daigle, M., Roychoudhury, I., Sweet, A., Celaya, J., & Goebel, K. (2012) Autonomous decision making for planetary rovers using diagnostic and prognostic information. Proceedings of the 8th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes (SAFEPROCESS 2012), Mexico City, Mexico.

Poll, S., Patterson-Hine, A., Camisa, J., Garcia, D., Hall, D., Lee, C., Mengshoel, O., Neukom, C., Nishikawa, D., Ossenfort, J., Sweet, A., Yentus, S., Roychoudhury, I., Daigle, M., Biswas,

G., & Koutsoukos, X. (2007). Advanced diagnostics and prognostics testbed. In Proc. DX’07, pp. 178–185.

Kurtoglu, T., Narasimhan, S., Poll, S., A., Garcia, D., Kuhn, L., de Kleer, J., van Gemund, A., & Feldman, A. (2009). First international diagnostics competition – DXC’09. In Proc. DX’09.
Technical Research Papers

Most read articles by the same author(s)