A multivariate statistical combination forecasting method for product quality evaluation

In this paper, a multivariate statistical combination forecasting method is proposed for key performance evaluation in process industry. This method is developed based on some of the most popular multivariate statistic approaches. It merges the advantages of the principal component regression method (PCR), the partial least squares regression method (PLSR) and the modified partial least squares regression method (MPLSR). We test the proposed method with a numerical example and also an actual wine production process. The results indicate that the prediction accuracy of the optimal combination forecasting method is superior to those of individual methods.

[1]  Jianbin Qiu,et al.  Robust Model Predictive Control Under Saturations and Packet Dropouts With Application to Networked Flotation Processes , 2014, IEEE Transactions on Automation Science and Engineering.

[2]  Mark J. Kamstra,et al.  Forecast combining with neural networks , 1996 .

[3]  Yugang Niu,et al.  Robust Filtering Design for Stochastic System With Mode-Dependent Output Quantization , 2010, IEEE Transactions on Signal Processing.

[4]  M. Mahmoud,et al.  Robust Kalman filtering for continuous time-lag systems with Markovian jump parameters , 2003 .

[5]  Soung Chang Liew,et al.  Wireless MIMO Switching: Weighted Sum Mean Square Error and Sum Rate Optimization , 2013, IEEE Trans. Inf. Theory.

[6]  V. Lakshmana Gomathi Nayagam,et al.  Multi-criteria decision-making method based on interval-valued intuitionistic fuzzy sets , 2011, Expert Syst. Appl..

[7]  J. Powell,et al.  Least absolute deviations estimation for the censored regression model , 1984 .

[8]  Fang Wu,et al.  Step-wise support vector machines for classification of overlapping samples , 2015, Neurocomputing.

[9]  Ping Zhang,et al.  A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process , 2012 .

[10]  LI Hong-yan.  A Kind of Combination Forecasting Model and Its Properties Based on Ordered Weighted Averaging(OWA) Operator , 2006 .

[11]  Hamid Reza Karimi,et al.  Data-driven design of robust fault detection system for wind turbines , 2014 .

[12]  Etienne Denoel,et al.  Linear prediction of speech with a least absolute error criterion , 1985, IEEE Trans. Acoust. Speech Signal Process..

[13]  R. Koenker,et al.  Asymptotic Theory of Least Absolute Error Regression , 1978 .

[14]  James Lam,et al.  $H_{\bm \infty}$ Fuzzy Filtering of Nonlinear Systems With Intermittent Measurements , 2009, IEEE Transactions on Fuzzy Systems.

[15]  Okyay Kaynak,et al.  Improved PLS Focused on Key-Performance-Indicator-Related Fault Diagnosis , 2015, IEEE Transactions on Industrial Electronics.

[16]  O. Kaynak,et al.  Asymptotic stability and stabilisation of uncertain delta operator systems with time-varying delays , 2013 .

[17]  Jianbin Qiu,et al.  Fuzzy-Model-Based Reliable Static Output Feedback $\mathscr{H}_{\infty }$ Control of Nonlinear Hyperbolic PDE Systems , 2016, IEEE Transactions on Fuzzy Systems.

[18]  Shen Yin,et al.  Velocity-Free Fault-Tolerant and Uncertainty Attenuation Control for a Class of Nonlinear Systems , 2016, IEEE Transactions on Industrial Electronics.

[19]  Guang Wang,et al.  Quality-Related Fault Detection Approach Based on Orthogonal Signal Correction and Modified PLS , 2015, IEEE Transactions on Industrial Informatics.

[20]  Daniel W. C. Ho,et al.  Fuzzy Filter Design for ItÔ Stochastic Systems With Application to Sensor Fault Detection , 2009, IEEE Transactions on Fuzzy Systems.

[21]  Wei Sun,et al.  An Advanced PLS Approach for Key Performance Indicator-Related Prediction and Diagnosis in Case of Outliers , 2016, IEEE Transactions on Industrial Electronics.

[22]  Tae Hyup Roh.  Forecasting the volatility of stock price index , 2007, Expert Syst. Appl..

[23]  Daniel E. Quevedo,et al.  Predictive Optimal Switching Sequence Direct Power Control for Grid-Connected Power Converters , 2015, IEEE Transactions on Industrial Electronics.

[24]  A. R. Aladro-Gonzalvo,et al.  Pilates-based exercise for persistent, non-specific low back pain and associated functional disability: a meta-analysis with meta-regression. , 2013, Journal of bodywork and movement therapies.

[25]  Okyay Kaynak,et al.  An LWPR-Based Data-Driven Fault Detection Approach for Nonlinear Process Monitoring , 2014, IEEE Transactions on Industrial Informatics.

[26]  Hamid Reza Karimi,et al.  Quality Evaluation Based on Multivariate Statistical Methods , 2013 .

[27]  Du Ji-gui.  Application of Combined Method in Power Load Forecasting , 2011 .

[28]  Torsten Jeinsch,et al.  Quality-Related Fault Detection in Industrial Multimode Dynamic Processes , 2014, IEEE Transactions on Industrial Electronics.

[29]  Huijun Gao,et al.  Fault-tolerant control of Markovian jump stochastic systems via the augmented sliding mode observer approach , 2014, Autom..

[30]  P. Shi.  Filtering on sampled-data systems with parametric uncertainty , 1998, IEEE Trans. Autom. Control..