Combination of analytical and statistical models for dynamic systems fault diagnosis
Complex industrial and aerospatial systems require efﬁcient monitoring and fault detection schemes to ease prognosis and health monitoring tasks. In this work we rely upon the model-based approach to perform robust fault detection and isolation using analytical and statistical models. We have combined Principal Component Analysis (PCA) together with Possible Conﬂicts (PCs), to improve the overall diagnosis process for complex system. Our proposal uses residuals computed using PCs as the input for the PCA tool. The PCA tool is able to accurately determine signiﬁcant deviations in the residuals, that will be identiﬁed as faults. The integration of both techniques provides more robust results for fault detection, while avoiding false alarms in PCAs due to changes in operation modes. Moreover, it provides the PCA approach with the necessary mechanisms to perform fault isolation. This approach has been tested on a laboratory plant with real data, obtaining promising results.
How to Cite
fault diagnosis, principal component analysis, model decomposition
Blanke, M., Kinnaert, M., Lunze, J., & Staroswiecki,M. (2006). Diagnosis and Fault-Tolerant Control. Springer.Bregon, A., Pulido, B., Biswas, G., & Koutsoukos,X. (2009). Generating Possible Conﬂicts from Bond Graphs Using Temporal Causal Graphs. In Proceedings of the 23rd European Conference on Modelling and Simulation, ECMS09 (p. 675-682). Madrid, Spain.
Bro, R., Kjeldahl, K., & Kiers, H. (2008). Crossvalidation of component models: A critical look at current methods. Analytical and Bioanalytical Chemistry, 390, 1241-1251.
Chiang, L., Russell, E., & Braatz, R. (2000). Fault Detection and Diagnosis in Industrial Systems. Nueva York: Springer.
Dressler, O., & Struss, P. (1996). The Consistency-based approach to automated diagnosis of devices. InIndustrial Systems. Nueva York: Springer.
G. Brewka (Ed.), Principles of Knowledge Representation (p. 269-314). CSLI Publications, Standford.
Eastment, H., & Krzanowski, W. (1982). Cross validatory choice of the number of components from a principal component analysis. Technometrics, 24, 73-77.
Fuente, M., Garcia, G., & Sainz, G. (2008). Fault diagnosis in a plant using Fisher discriminant analysis. Proceding of the 16th Mediterranean Conference on Control and Automation Congress Centre, Ajaccio, France, 53-58.
Gertler, J. (1998). Fault Detection and Diagnosis in Engineering Systems. Marcel Dekker, Inc. Gertler, J., Li, W., Huang, Y., & McAvoy, T. (1999).
Isolation enhanced principal component analysis. American Institute of Chemical Engineers (AIChE) Journal, 45, 323-334.
Huang, Y., Gertler, J., & McAvoy, T. (2000). Sensor and actuator fault isolation by structured partial PCA with nonlinear extensions. Journal of Process Control, 10(5), 459-469.
Hwang, D., & Han, C. (1999). Real-time monitoring for a process with multiple operating modes. Control Engineering Practice, 7, 891-902.
Jackson, J. (1991). A user’s guide to principal components. Wiley.
Jackson, J., & Mudholkar, G. (1979). Control procedures for residuals associated with principal component analysis. Technometrics, 21, 341-349.
Kourti, T., & MacGregor, J. (1996). Multivariate SPC Methods for Process and Product Monitoring. Journal of Quality Technology, 28, 409-428.
Ku, W., Storer, R., & Georgakis, C. (1995, November). Disturbance detection and isolation by dynamic principal component analysis. Chemometrics and intelligent laboratory systems, 30, 179-196.
Lane, S., Martin, E., Morris, A., & Gower, P. (2003). Application of exponentially weighted principal component analysis for the monitoring of a polymer ﬁlm manufacturing process. Transactions of the Institute of Measurement and Control, 25, 17-35.
Li, W., Yue, H., Valle-Cervantes, S., & Qin, S. (2000).Recursive PCA for adaptative process monitoring. Journal of Process Control, 10, 471-486.
Misra, M., Yue, H., Qin, S., & Ling, C. (2002). Multivariable process monitoring and fault diagnosis by multi-scale PCA. Computers & Chemical Engineering, 26, 1281-1293.
Pulido, B., Alonso, C., & Acebes, F. (2001). Lessons learned from diagnosing dynamic systems using possible conﬂicts and quantitative models. In Engineering of Intelligent Systems. XIV Conf. IEA/AIE-2001 (Vols. LNAI, Current Topics in AI, Springer, p. 135-144). Budapest, Hungary.
Pulido, B., & Alonso-González, C. (2004, Octubre).Possible Conﬂicts: a compilation technique for consistency-based diagnosis. IEEE Trans. on Systems, Man, and Cybernetics. Part B: Cybernetics, 34(5), 2192-2206.
Reiter, R. (1987). A Theory of Diagnosis from First Principles. Artiﬁcial Intelligence, 32, 57-95.
Tien, D., Lim, K., & Jun, L. (2004, November 2-6).Compartive study of PCA approaches in process monitoring and fault detection. The 30th annual conference of the IEEE industrial electronics society, 2594-2599.
Venkatasubramanian, V., Rengaswamy, R., Kavuri, S.,& Yin, K. (2003). A review of process fault detection and diagnosis. Part I, II and III: Quantitative model-based methods. Computers & Chemical Engineering, 27, 291-346.
Weighell, M., Martin, E., & Morris, A. (2001). The statistical monitoring of a complex manufacturing process. Journal of Applied Statistics, 28, 409425.
Wold, S. (1987). Principal Component Analysis. Chemometrics and intelligent laboratory systems, 2, 37-52.
Zumoffen, D., & Basualdo, M. (2007). From large chemical plant data to fault diagnosis integrated to decentralized fault tolerant control: pulp mill process application. Industrial & Engineering Chemistry Research, 47, 1201-1220.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.