Multivariate Fault Detection using Vector Autoregressive Moving Average and Orthogonal Transformation in Residual Space
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Abstract
We propose the use of multivariate orthogonal space transformations and Vector Autoregressive Moving-Average (VARMA) models in combination with data-driven system identification models to improve residual-based approaches to fault detection in rolling mills. Introducing VARMA models allows us to build k-step ahead multi-dimensional prediction models including the time lags that best explain the target. Multivariate orthogonal space transformations pro- vide estimates for the dynamical parameters by rewriting the equation set of the system at hand, decomposing the measured data into process and residuals spaces. Modeling in the process space then produces much more accurate models due to dimensionality (noise) reduction. Since we use an unsupervised scheme that requires a priori neither annotated samples nor fault patterns/models, both model identification and fault detection are based solely on the on-line recorded data streams. Our experimental results demonstrate that our approach yields improved Receiver Operating Characteristic (ROC) curves than methods that do not employ vector autoregressive moving-average models and multivariate orthogonal space transformations.
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residual-based fault detection, system identification, vector auto-regressive moving average model, multivariate orthogonal space transformations, on-line dynamic residual analysis
Angelov, P., Lughofer, E., & Zhou, X. (2008). Evolving fuzzy classifiers using different model architectures. Fuzzy Sets and Systems, 159(23), 3160–3182.
Bang, Y., Yoo, C., & Lee, I. (2002). Nonlinear pls modeling with fuzzy inference system. Chemometrics and Intelligent Laboratory Systems, 64, 137–155.
Bishop, C. (2006). Pattern recognition and machine learning. New York, NY, USA: Springer.
Bolt, P., Batazzi, D., Belfiore, N., Gaspard, C., Goiset, L., Laugier, M., et al. (2010). Damage resistance and roughness retention of work rolls in cold rolling mills. Revue de Me ́tallurgie, 107(6), 245–255.
Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys, 41(3).
Daubechies, I., Defrise, M., & Mol, C. D. (2004). An itera- tive thresholding algorithm for linear inverse problems with a sparsity constraint. Communications on Pure and Applied Mathematics, 57(11), 1413–1457.
Dong, M., Liu, C., & Li, G. (2010). Robust fault diagnosis based on nonlinear model of hydraulic gauge control system on rolling mill. IEEE Transactions on Control Systems Technology, 18(2), 510–515.
Frank, P., Alcorta, E., & Ko ̈ppen-Seliger, B. (2000). Modelling for fault detection and isolation versus modelling for control. Mathematics and Computers in Simulation, 53(33), 259 - 271.
Holan, S., Lund, R., & Davis, G. (2010). The arma alphabet soup: A tour of arma model variants. Statistical Surveys, 4, 232–274.
Isermann, R., & Balle ́, P. (1997). Trends in the application of model-based fault detection and diagnosis of technical processes. Control Engineering Practice, 5(5), 709– 719.
Jackson, J., & Mudholkar, G. (1979). Control procedures for residuals associated with principal component analysis. Technometrics, 21(3), 341–349.
Jolliffe, I. (2002). Principal component analysis (2nd ed.). New York, NY, USA: Springer.
Li, G., Qin, S., & Zhou, D. (2010). Geometric properties of partial least squares for process monitoring. Automatica, 204–210.
Lughofer, E., & Kindermann, S. (2010). SparseFIS: Data- driven learning of fuzzy systems with sparsity constraints. IEEE Transactions on Fuzzy Systems, 18(2), 396–411.
Miller, A. (2002). Subset selection in regression second edition. Boca Raton, FL, USA: Chapman and Hall/CRC.
Muradore, R., & Fiorini, P. (2012). A pls-based statistical approach for fault detection and isolation of robotic manipulators. Industrial Electronics, IEEE Transactions on, 59, 3167–3175.
Nelles, O. (2001). Nonlinear system identification. Berlin: Springer.
Odgaard, P., Lin, B., & Jørgensen, S. (2008). Observer and data-driven-model-based fault detection in power plant coal mills. IEEE Transactions on Energy Conversion, 23(2), 659–668.
Pichler, K., Lughofer, E., Buchegger, T., Klement, E., & Huschenbett, M. (2012). A visual method to detect broken reciprocating compressor valves under varying load conditions. In
Proceedings of the asme 2012 international mechanical engineering congress & exposition (p. to appear). Houston, TX, USA: ASME.
Scho ̈ener, H., Moser, B., & Lughofer, E. (2008). On pre- processing multi-channel sensor data for online process monitoring. In Proceedings of the international conference on computational intelligence for modelling, control and automation (cimca) (pp. 414–419). Vienna, Austria.
So ̈derstro ̈m, T., & Stoica, P. (1988). System identification. Upper Saddle River, NJ, USA: Prentice Hall.
Tamura, M., & Tsujita, S. (2007). A study on the number of principal components and sensitivity of fault detection using pca. Computers & Chemical Engineering, 31(9), 1035–1046.
Theilliol, D., Mahfouf, M., Ponsart, J., Sauter, D., & Gama, M. (2010). Design of a fault diagnosis system based on a bank of filter-observers with application to a hot rolling mill. Transactions of the Institute of Measure- ment and Control, 32(3), 265–285.
Venkatasubramanian, V., Rengaswamy, R., Kavuri, S., & Yin, K. (2003). A review of process fault detection and diagnosis: Part iii: Process history based methods. Computers &
Chemical Engineering, 27(3), 327–346. Wang, X., Kruger, U., & Lennox, B. (2003). Recursive partial least squares algorithms for monitoring complex industrial processes. Control
Engineering Practice, 6(11),613–632.
Yang, M., & Makis, V. (2010). ARX model-based gear box fault detection and localization under varying load conditions. Journal of Sound and Vibration, 329(24), 5209–5221.
Yang, T. (2006). A method of fast fault detection based on ARMA and neural network. In Proceedings of the sixth world congress on intelligent control and automation (wcica) (pp. 5438–5441). Dalian, China.
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