Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30184
Faults Forecasting System

Authors: Hanaa E.Sayed, Hossam A. Gabbar, Shigeji Miyazaki

Abstract:

This paper presents Faults Forecasting System (FFS) that utilizes statistical forecasting techniques in analyzing process variables data in order to forecast faults occurrences. FFS is proposing new idea in detecting faults. Current techniques used in faults detection are based on analyzing the current status of the system variables in order to check if the current status is fault or not. FFS is using forecasting techniques to predict future timing for faults before it happens. Proposed model is applying subset modeling strategy and Bayesian approach in order to decrease dimensionality of the process variables and improve faults forecasting accuracy. A practical experiment, designed and implemented in Okayama University, Japan, is implemented, and the comparison shows that our proposed model is showing high forecasting accuracy and BEFORE-TIME.

Keywords: Bayesian Techniques, Faults Detection, Forecasting techniques, Multivariate Analysis.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1059577

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1179

References:


[1] A. Jager. A note on the informational efficiency of Austrian economic forecasts, Empirica 12, no. 2, 247-260. (1985).
[2] An-Sing Chena, Mark T. Leung, A Bayesian vector error correction model for forecasting exchange rates, Computers & Operations Research (30) 887-900. (2003).
[3] C.A. Sims. Macroeconomics and reality, Econometrica 48, l-48. (1980).
[4] C.W.J. Granger. Investing causal relations by econometric models and cross-spectral methods, Econometrica 37, 424-438. (1969).
[5] Iain Pardoea, and Robert R.Weidner, Sentencing convicted felons in the United States: a Bayesian analysis using multilevel covariates, Journal of Statistical Planning and Inference (136) 1433 - 1455. (2006).
[6] H. Theil. Applied economic forecasting (North-Holland, Amsterdam). (1966).
[7] J.L. Kling, and D.A. Bessler. A comparison of multivariate procedures for economic time series, International Journal of Forecasting 1, 5-24. (1985).
[8] Jacob Chi-Man Yiu, Shengwei Wang, Multiple ARMAX modeling scheme for forecasting air conditioning system performance, Energy Conversion and Management (48) 2276-2285. (2007).
[9] Leo H. Chiang, and Richard D. Braatz, Process monitoring using causal map and multivariate statistics: Fault detection and identification , Chemometrics and Intelligent Laboratory Systems 65 (2003) 159- 178
[10] M. Sinan Gön├╝l, Dilek ├ûnkal, Michael Lawrence, The effects of structural characteristics of explanations on use of a DSS. Decision Support Systems (42)1481-1493. (2006).
[11] Massimiliano Marcellinoa, James H. Stockb, Mark W. Watson, A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series, Journal of Econometrics (135) 499-526. (2006)
[12] Mattias Villani, Bayesian prediction with cointegrated vector autoregressions, International Journal of Forecasting (17) 585-605. (2001).
[13] Po-Hsuan Hsua, Chi-Hsiu Wangb, Joseph Z. Shyua, and Hsiao-Cheng Yu, A Litterman BVAR approach for production forecasting of technology industries, Technological Forecasting & Social Change (70) 67-82. (2002)
[14] Raghunathan Rengaswamy, Venkat Venkatasubramanian, A fast training neural network and its updation for incipient fault detection and diagnosis, Computers and Chemical Engineering (24) 43 l-437. (2000).
[15] Ronald Bewley, Forecast accuracy, coefficient bias and Bayesian vector autoregressions, Mathematics and Computers in Simulation (59) 163- 169. (2002).
[16] Sang Wook Choia, Changkyu Leeb, Jong-Min Leeb, Jin Hyun Parkc, and In-Beum Lee, Fault detection and identification of nonlinear processes based on kernel PCA, Chemometrics and Intelligent Laboratory Systems (75) 55- 67. (2005).
[17] Sarah Gelper, Christophe Croux, Multivariate out-of-sample tests for Granger causality, Computational Statistics & Data Analysis (51) 3319 - 3329. (2007).
[18] Soura Dasgupta, and Brian D.O. Anderson. A Parametrization for the Closed-loop Identification of Nonlinear Time-varying Systems. Auromarrro. Vol. 32, No. 10, pp. 1349-1360. (1996).
[19] Spyros G. Makridakis, Steven C. Wheelwright, and Rob J. Hyndman, Forecasting: Methods and Applications, 3rd edition, John wiley & sons, (1998).
[20] Tao Chen, Julian Morris, and Elaine Martin, Gaussian process regression for multivariate spectroscopic calibration. Chemometrics and Intelligent Laboratory Systems (87) 59- 71. (2007).
[21] V. Haggan and O.B. Oyetunji, On the selection of subset autoregressive time series models, Journal of Time Series Analysis 5, no. 2, 103-113. (1984).
[22] V. Rao Vemuri, and Robert D. Rogers. Artificial Neural Networks, Forecasting Time Series. IEEE Computer Society Press. (1994).
[23] Volkan S. Edigera, Sertac Akarb, Berkin Ugurlu, Forecasting production of fossil fuel sources in Turkey using a comparative regression and ARIMA model,
[24] Energy Policy (34) 3836-3846. (2006). Yifeng Zhou, Juergen Hahn, and M. Sam Mannan, Fault detection and classification in chemical processes based on neural networks with feature extraction, ISA Transactions 42 ~2003! 651-664. (2003).