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Real-Time Fault Detection Using Recursive Density Estimation

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Abstract

Applications of fault detection techniques in industrial environments are increasing in order to improve the operational safety, as well as to reduce the costs related to unscheduled stoppages. Although there are numerous proposals in the literature about fault detection techniques, most of the approaches demand extensive computational effort or even require too many thresholds or problem-specific parameters to be predefined in advance, impairing their use in real-time applications. Aiming to overcome these problems, we propose in this paper an approach for real-time fault detection of industrial plants based on the analysis of the control and error signals, using recursive density estimation. Our proposed approach is based on the concept of the density in the data space, which is not the same as probability density function, but is a very useful measure for abnormality/outliers detection. The density can be calculated recursively, which makes it suitable for real-time environments. We define a criterion for density drop integral/sum, which is used as a problem- and user-insensitive (automatic) threshold to identify the faults/anomalies. In order to validate our proposal, we present experimental results from a level control laboratory process, where control and error signals are used as features for the fault detection, but the approach is generic and the number of features can be significant due to the computationally lean methodology, since covariance or more complex calculations are not required. The obtained results are encouraging when compared with the traditional statistical approach.

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References

  • Angelov, P. (2012a). Anomalous system state identification, patent GB1208542.9, priority date: 15 May 2012.

  • Angelov, P. (2012b). Autonomous learning systems: from data to knowledge in real time. New York: Willey.

    Book  Google Scholar 

  • Angelov, P., & Buswell, R. (2001). Evolving rule-based models: A tool for intelligent adaptation. In IFSA world congress and 20th NAFIPS international conference, 2001. Joint 9th (vol. 2, pp. 1062–1067).

  • Angelov, P., & Zhou, X. (2008). Evolving fuzzy-rule-based classifiers from data streams. IEEE Transactions on Fuzzy Systems, 16(6), 1462–1475.

    Article  Google Scholar 

  • Angelov, P., Ramezani, R., & Zhou, X. (2008). Autonomous novelty detection and object tracking in video streams using evolving clustering and takagi-sugeno type neuro-fuzzy system. In IEEE international joint conference on neural networks, 2008. IJCNN 2008. (IEEE world congress on computational intelligence) (pp. 1456–1463).

  • Anwar, S., & Chen, L. (2007). An analytical redundancy-based fault detection and isolation algorithm for a road-wheel control subsystem in a steer-by-wire system. IEEE Transactions on Vehicular Technology, 56(5), 2859–2869.

    Article  Google Scholar 

  • Anzanello, M. J. (2010). Feature extraction and feature selection: A survey of methods in industrial applications. Hoboken: Wiley.

    Google Scholar 

  • Bernieri, A., Betta, G., & Liguori, C. (1996). On-line fault detection and diagnosis obtained by implementing neural algorithms on a digital signal processor. IEEE Transactions on Instrumentation and Measurement, 45(5), 894–899.

    Article  Google Scholar 

  • Breunig, M., Kriegel, H. P., Ng, R. T., & Sander, J. (2000). LOF: Identifying density-based local outliers. In Proceedings of the 2000 ACM SIGMOD international conference on management of data (pp. 93–104). ACM.

  • Chen, W., & Saif, M. (2007). Observer-based strategies for actuator fault detection, isolation and estimation for certain class of uncertain nonlinear systems. IET Control Theory Applications, 1(6), 1672–1680.

    Article  Google Scholar 

  • Cook, G., Maxwell, J., Barnett, R., & Strauss, A. (1997). Statistical process control application to weld process. IEEE Transactions on Industry Applications, 33(2), 454–463.

    Article  Google Scholar 

  • Costa, B., Bezerra, C. G., & Guedes, L. A. (2010). Java fuzzy logic tool-box for industrial process control. In Proceedings of the 2010 Brazilian conference on automatics (CBA), Brazilian Society for Automatics (SBA), Bonito-MS, Brazil.

  • Costa, B., Bezerra, C., & Guedes, L. (2012). A multistage fuzzy controller: Toolbox for industrial applications. In IEEE international conference on industrial technology (ICIT) (pp. 1142–1147).

  • Costa, B., Skrjanc, I., Blazic, S., & Angelov, P. (2013). A practical implementation of self-evolving cloud-based control of a pilot plant. In 2013 IEEE international conference on cybernetics, Lausanne, Switzerland.

  • Dash, S., Rengaswamy, R., & Venkatasubramanian, V. (2003). Fuzzy-logic based trend classification for fault diagnosis of chemical processes. Computers & Chemical Engineering, 27(3), 347–362.

    Article  Google Scholar 

  • DeLorenzo, (2009). DL2314br—Didactic process control pilot plant. DeLorenzo Italy: Catalog.

    Google Scholar 

  • El-Shal, S., & Morris, A. (2000). A fuzzy expert system for fault detection in statistical process control of industrial processes. Systems, Man, and Cybernetics C, 30(2), 281–289.

    Article  Google Scholar 

  • Hautamaki, V., Karkkainen, I., & Franti, P. (2004). Outlier detection using k-nearest neighbour graph. In Proceedings of the 17th international conference on pattern recog-nition, 2004, ICPR 2004 (vol. 3, pp. 430–433).

  • Hossain, A., Choudhury, Z., & Suyut, S. (1996). Statistical process control of an industrial process in real time. IEEE Transactions on Industry Applications, 32(2), 243–249.

    Article  Google Scholar 

  • Hwang, I., Kim, S., Kim, Y., & Seah, C. (2010). A survey of fault detection, isolation, and reconfiguration methods. IEEE Transactions on Control Systems Technology, 18(3), 636–653.

    Article  Google Scholar 

  • Kano, M., Sakata, T., & Hasebe, S. (2010). Just-in-time statistical process control for exible fault management. In Proceedings of SICE annual conference 2010 (pp. 1482–1485).

  • Kembhavi, A., Harwood, D., & Davis, L. (2011). Vehicle detection using partial least squares. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(6), 1250–1265.

    Article  Google Scholar 

  • Kolev, D., Angelov, P., Markarian, G., Suvorov, M., & Lysanov, S. (2013). ARFA: Automated real time flight data analysis using evolving clustering, classifiers and recursive density estimation. In Proceedings of the IEEE symposium series on computational intelligence SSCI-2013, Singapore (pp. 91–97).

  • Laukonen, E., Passino, K., Krishnaswami, V., Luh, G. C., & Rizzoni, G. (1995). Fault detection and isolation for an experimental internal combustion engine via fuzzy identification. IEEE Transactions on Control Systems Technology, 3(3), 347–355.

    Article  Google Scholar 

  • Laurentys, C., Palhares, R., & Caminhas, W. (2010a). Design of an artificial immune system based on danger model for fault detection. Expert Systems with Applications, 37(7), 5145–5152.

    Google Scholar 

  • Laurentys, C., Ronacher, G., Palhares, R., & Caminhas, W. (2010b). Design of an artificial immune system for fault detection: A negative selection approach. Expert Systems with Applications, 37(7), 5507–5513.

    Google Scholar 

  • Leite, D. F., Hell, M. B., Jr, P. C., & Gomide, F. (2009). Real-time fault diagnosis of nonlinear systems. Nonlinear Analysis: Theory, Methods and Applications, 71(12), e2665–e2673.

    Article  MATH  Google Scholar 

  • Levine, M. (1969). Feature extraction: A survey. Proceedings of the IEEE, 57(8), 1391–1407.

    Article  Google Scholar 

  • Li, X. J., & Yang, G. H. (2012). Dynamic observer-based robust control and fault detection for linear systems. IET Control Theory Applications, 6(17), 2657–2666.

    Article  MathSciNet  Google Scholar 

  • Liu, H., Chen, G., Jiang, S., & Song, G. (2008). A survey of feature extraction approaches in analog circuit fault diagnosis. In Pacific-Asia workshop on computational intelligence and industrial application, 2008. PACIIA ’08 (vol. 2, pp. 676–680).

  • Liu, J., Lim, K. W., Ho, W. K., Tan, K. C., Tay, A., & Srinivasan, R. (2005). Using the opc standard for real-time process monitoring and control. IEEE Software, 22(6), 54–59.

    Article  Google Scholar 

  • Liukkonen, T., & Tuominen, A. (2004). A case study of spc in circuit board assembly: Statistical mounting process control. In 24th international conference on microelectronics, 2004 (vol. 2, pp 445–448).

  • Maiying, Z., Chenghui, Z., Steven, D., & James, L. (2004). Observer-based fault detection scheme for a class of discrete time-delay systems. Journal of Systems Engineering and Electronics, 15(3), 288–294.

    Google Scholar 

  • Malhi, A., & Gao, R. (2004). PCA-based feature selection scheme for machine defect classification. IEEE Transactions on Instrumentation and Measurement, 53(6), 1517–1525.

    Article  Google Scholar 

  • Marins, A. (2009). Continuous process workbench. DeLorenzo Brazil: Technical manual.

    Google Scholar 

  • Martin, E., Morris, A. J., & Zhang, J. (1996). Process performance monitoring using multivariate statistical process control. IEE Proceedings Control Theory and Applications, 143(2), 132–144.

    Article  MATH  Google Scholar 

  • Miljkovic, D. (2011). Fault detection methods: A literature survey. In MIPRO, 2011 proceedings of the 34th international convention (pp. 750–755).

  • Oblak, S., Skrjanc, I., & Blazic, S. (2007). Fault detection for non-linear systems with uncertain parameters based on the interval fuzzy model. Engineering Applications of Artificial Intelligence, 20(4), 503–510.

    Article  Google Scholar 

  • Pande, S. S., & Prabhu, B. S. (1990). An expert system for automatic extraction of machining features and tooling selection for automats. Computer-Aided Engineering Journal, 7(4), 99–103.

    Article  Google Scholar 

  • Papadimitriou, S., Kitagawa, H., Gibbons, P., & Faloutsos, C. (2003). Loci: fast outlier detection using the local correlation integral. In Proceedings. 19th international conference on data engineering, 2003 (pp. 315–326).

  • Ramezani, R., Angelov, P., & Zhou, X. (2008). A fast approach to novelty detection in video streams using recursive density estimation. In Intelligent systems, 2008. IS ’08. 4th international IEEE conference (vol. 2, pp 142–147).

  • Samantaray, A. K., & Bouamama, B. O. (2008). Model-based process supervision: A bond graph approach (1st ed.). London: Springer.

    Google Scholar 

  • Schwarz, M. H., & Boercsoek, J. (2007). A survey on ole for process control (OPC). Proceedings of the 7th conference on 7th WSEAS international conference on applied computer science, World Scientific and Engineering Academy and Society (WSEAS) (pp. 186–191). Wisconsin: Stevens Point.

  • Simani, S., & Patton, R. J. (2008). Fault diagnosis of an industrial gas turbine prototype using a system identification approach. Control Engineering Practice, 16(7), 769–786.

    Article  Google Scholar 

  • Sneider, H., & Frank, P. (1996). Observer-based supervision and fault detection in robots using nonlinear and fuzzy logic residual evaluation. IEEE Transactions on Control Systems Technology, 4(3), 274–282.

    Article  Google Scholar 

  • Song, F., Mei, D., & Li, H. (2010). Feature selection based on linear discriminant analysis. In International Conference on intelligent system design and engineering application (ISDEA), 2010 (vol. 1, pp. 746–749).

  • Tang, J., Chen, Z., Fu, A. W. C., & Cheung, D. W. L. (2002). Enhancing effectiveness of outlier detections for low density patterns. In Proceedings of the 6th Pacific-Asia conference on advances in knowledge discovery and data mining, PAKDD ’02 (pp. 535–548). Springer: London

  • Thornhill, N. F., & Horch, A. (2007). Advances and new directions in plant-wide disturbance detection and diagnosis. Control Engineering Practice, 15(10), 1196–1206.

    Article  Google Scholar 

  • Vemuri, A., Polycarpou, M., & Diakourtis, S. (1998). Neural network based fault detection in robotic manipulators. IEEE Transactions on Robotics and Automation, 14(2), 342–348.

    Article  Google Scholar 

  • Venkatasubramanian, V., Rengaswamy, R., & Kavuri, S. N. (2003a). A review of process fault detection and diagnosis: Part II: Qualitative models and search strategies. Computers & Chemical Engineering, 27(3), 313–326.

    Article  Google Scholar 

  • Venkatasubramanian, V., Rengaswamy, R., Kavuri, S. N., & Yin, K. (2003b). A review of process fault detection and diagno-sis: Part III: Process history based methods. Computers & Chemical Engineering, 27(3), 327–346.

    Article  Google Scholar 

  • Venkatasubramanian, V., Rengaswamy, R., Yin, K., & Kavuri, S. N. (2003c). A review of process fault detection and diagnosis: Part I: Quantitative model-based methods. Computers & Chemical Engineering, 27(3), 293–311.

    Article  Google Scholar 

  • Wang, P., & Guo, C. (2013). Based on the coal mine’s essential safety management system of safety accident cause analysis. American Journal of Environment, Energy and Power Research, 1(3), 62–68.

    Google Scholar 

  • Xu, L., & Tseng, H. (2007). Robust model-based fault detection for a roll stability control system. IEEE Transactions on Control Systems Technology, 15(3), 519–528.

    Article  Google Scholar 

  • Yang, H., Xia, Y., & Liu, B. (2011). Fault detection for t-s fuzzy discrete systems in finite-frequency domain. Systems, Man, and Cybernetics B, 41(4), 911–920.

    Article  Google Scholar 

  • Zio, E. (2009). Reliability engineering: Old problems and new challenges. Reliability Engineering & System Safety, 94(2), 125–141.

    Article  Google Scholar 

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Acknowledgments

The first author would like to acknowledge the support of CAPES Foundation, Ministry of Education of Brazil, Braslia- DF 70040-020, Brazil.

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Authors and Affiliations

  1. Campus Natal - Zona Norte, Federal Institute of Rio Grande do Norte - IFRN, Natal, Brazil

    Bruno Sielly Jales Costa

  2. School of Computing and Communications, Lancaster University, Lancaster, UK

    Plamen Parvanov Angelov

  3. Department of Computing Engineering and Automation, Federal University of Rio Grande do Norte - UFRN, Natal, Brazil

    Luiz Affonso Guedes

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  1. Bruno Sielly Jales Costa
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  2. Plamen Parvanov Angelov
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  3. Luiz Affonso Guedes
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Correspondence to Bruno Sielly Jales Costa.

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Costa, B.S.J., Angelov, P.P. & Guedes, L.A. Real-Time Fault Detection Using Recursive Density Estimation. J Control Autom Electr Syst 25, 428–437 (2014). https://doi.org/10.1007/s40313-014-0128-4

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