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.
[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).
[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).
@article{"International Journal of Information, Control and Computer Sciences:53752", author = "Hanaa E.Sayed and Hossam A. Gabbar and Shigeji Miyazaki", title = "Faults Forecasting System", 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.", volume = "3", number = "5", pages = "1305-7", }
