0
Research Papers

A New DROS-Extreme Learning Machine With Differential Vector-KPCA Approach for Real-Time Fault Recognition of Nonlinear Processes

[+] Author and Article Information
Yuan Xu, Liang-Liang Ye

College of Information Science and Technology,
Beijing University of Chemical Technology,
Beijing 100029, China

Qun-Xiong Zhu

College of Information Science and Technology,
Beijing University of Chemical Technology,
Beijing 100029, China
e-mail: buct_ielab@163.com

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received June 16, 2014; final manuscript received September 25, 2014; published online December 10, 2014. Assoc. Editor: Hashem Ashrafiuon.

J. Dyn. Sys., Meas., Control 137(5), 051011 (May 01, 2015) (10 pages) Paper No: DS-14-1257; doi: 10.1115/1.4028716 History: Received June 16, 2014; Revised September 25, 2014; Online December 10, 2014

In this paper, a new dynamic recurrent online sequential-extreme learning machine (DROS-ELM) OS-ELM with differential vector-kernel based principal component analysis (DV-KPCA) fault recognition approach is proposed to reconstruct the process feature and detect the process faults for real-time nonlinear system. Toward this end, the differential vector plus KPCA is first proposed to reduce the dimension of process data and enlarge the feature difference. In DV-KPCA, the differential vector is the difference between the input sample and the common sample, which is obtained from the historical data and represents the common invariant properties of the class. The optimal feature vectors of input sample and the common sample are obtained by KPCA procedure for the difference vectors. Through the differential operation between the input vectors and the common vectors, the reconstructed feature is derived by calculating the two-norm distance for the result of differential operation. The reconstructed features are then utilized to detect the process faults that may occur. In order to enhance the accuracy of fault recognition, a new DROS-ELM is developed by adding a self-feedback unit from the output of hidden layer to the input of hidden layer to record the sequential information. In the DROS-ELM, the output weight of feedback layer is updated dynamically by the change rate of output of the hidden layer. The DV-KPCA for feature reconstruction is exemplified using UCI handwriting (UCI handwriting recognition data: Database, using “Pen-Based Recognition of Handwritten Digits” produced in the Department of Computer Engineering Bogazici University, Istanbul 80815, Turkey, 1998), which the classification accuracy is obviously enhanced. Meanwhile, the DROS-ELM for process prediction is tested by the sunspot data from 1700 to 1987, which also shows better prediction accuracy than common methods. Finally, the new joint DROS-ELM with DV-KPCA method is exemplified in the complicated Tennessee Eastman (TE) benchmark process to illustrate the efficiencies. The results show that the DROS-ELM with DV-KPCA shows superiority not only in detection sensitivity and stability but also in timely fault recognition.

FIGURES IN THIS ARTICLE
<>
Copyright © 2015 by ASME
Your Session has timed out. Please sign back in to continue.

References

Arinton, E., Caraman, S., and Korbicz, J., 2012, “Neural Networks for Modelling and Fault Detection of the Inter-Stand Strip Tension of a Cold Tandem Mill,” Control Eng. Pract., 20(7), pp. 684–694. [CrossRef]
Jack, L. B., and Nandi, A. K., 2002, “Fault Detection Using Support Vector Machines and Artificial Neural Networks, Augmented by Genetic Algorithms,” Mech. Syst. Signal Process., 16(2), pp. 373–390. [CrossRef]
Li, Z., Yan, X., Yuan, C., Zhao, J., and Peng, Z., 2011, “Fault Detection and Diagnosis of a Gearbox in Marine Propulsion Systems Using Bispectrum Analysis and Artificial Neural Networks,” J. Mar. Sci. Appl., 10(1), pp. 17–24. [CrossRef]
Perera, N., and Rajapakse, A. D., 2011, “Recognition of Fault Transients Using a Probabilistic Neural-Network Classifier,” IEEE Trans. Power Delivery, 26(1), pp. 410–419. [CrossRef]
Bueno-Crespo, A., García-Laencina, P. J., and Sancho-Gómez, J. L., 2013, “Neural Architecture Design Based on Extreme Learning Machine,” Neural Networks, 48, pp. 19–24. [CrossRef] [PubMed]
Liang, N. Y., Huang, G. B., Saratchandran, P., and Sundararajan, N., 2006, “A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks,” IEEE Trans. Neural Networks, 17(6), pp. 1411–1423. [CrossRef]
Wong, P. K., Yang, Z., Vong, C. M., and Zhong, J., 2014, “Real-Time Fault Diagnosis for Gas Turbine Generator Systems Using Extreme Learning Machine,” Neurocomputing, 128, pp. 249–257. [CrossRef]
Zhang, Y., and Zhang, P., 2011, “Optimization of Nonlinear Process Based on Sequential Extreme Learning Machine,” Chem. Eng. Sci., 66(20), pp. 4702–4710. [CrossRef]
Pan, Y., Er, M. J., Li, X., Yu, H., and Gouriveau, R., 2014, “Machine Health Condition Prediction Via Online Dynamic Fuzzy Neural Networks,” Eng. Appl. Artif. Intell., 35, pp. 105–113. [CrossRef]
Pan, F., and Zhao, H., 2013, “Online Sequential Extreme Learning Machine Based Multilayer Perception With Output Self-Feedback for Time Series Prediction,” J. Shanghai Jiaotong Univ. (Sci.), 18(3), pp. 366–375. [CrossRef]
Rong, H. J., Huang, G. B., Sundararajan, N., and Saratchandran, P., 2009, “Online Sequential Fuzzy Extreme Learning Machine for Function Approximation and Classification Problems,” IEEE Trans. Syst., Man, Cybern., Part B, 39(4), pp. 1067–1072. [CrossRef]
Liu, Q., Guo, Z., and Wang, J., 2012, “A One-Layer Recurrent Neural Network for Constrained Pseudoconvex Optimization and Its Application for Dynamic Portfolio Optimization,” Neural Networks, 26, pp. 99–109. [CrossRef] [PubMed]
Alanis, A. Y., Sanchez, E. N., Loukianov, A. G., and Perez, M. A., 2011, “Real-Time Recurrent Neural State Estimation,” IEEE Trans. Neural Networks, 22(3), pp. 497–505. [CrossRef]
Wen, Y., He, L., and Shi, P., 2012, “Face Recognition Using Difference Vector Plus KPCA,” Digital Signal Process., 22(1), pp. 140–146. [CrossRef]
Jia, M., Xu, H., Liu, X., and Wang, N., 2012, “The Optimization of the Kind and Parameters of Kernel Function in KPCA for Process Monitoring,” Comput. Chem. Eng., 46, pp. 94–104. [CrossRef]
Zhao, L. J., Yuan, D. C., Chai, T. Y., and Tang, J., 2011, “KPCA and ELM Ensemble Modeling of Wastewater Effluent Quality Indices,” Procedia Eng., 15, pp. 5558–5562. [CrossRef]
Zhou, J., Guo, A., Celler, B., and Su, S., 2014, “Fault Detection and Identification Spanning Multiple Processes by Integrating PCA With Neural Network,” Appl. Soft Comput., 14, pp. 4–11. [CrossRef]
Yilmaz, S., and Oysal, Y., 2010, “Fuzzy Wavelet Neural Network Models for Prediction and Identification of Dynamical Systems,” IEEE Trans. Neural Networks, 21(10), pp. 1599–1609. [CrossRef]
Lim, J. S., 2013, “Partitioned Online Sequential Extreme Learning Machine for Large Ordered System Modeling,” Neurocomputing, 102(15), pp. 59–64. [CrossRef]
Guo, L., Hao, J., and Liu, M., 2014, “An Incremental Extreme Learning Machine for Online Sequential Learning Problems,” Neurocomputing, 128, pp. 50–58. [CrossRef]
Subhashini, P. P. S., and Prasad, V., 2013, “Recognition of Handwritten Digits Using RBF Neural Network,” Int. J. Res. Eng. Technol., 2(3), pp. 393–397.
Chacko, B. P., Krishnan, V. R. V., Raju, G., and Anto, P. B., 2012, “Handwritten Character Recognition Using Wavelet Energy and Extreme Learning Machine,” Int. J. Mach. Learn. Cybern., 3(2), pp. 149–161. [CrossRef]
Ge, Z., Yang, C., and Song, Z., 2009, “Improved Kernel PCA-Based Monitoring Approach for Nonlinear Processes,” Chem. Eng. Sci., 64(9), pp. 2245–2255. [CrossRef]
Yina, S., Ding, S. X., Haghani, A., Hao, H., and Zhang, P., 2012, “A Comparison Study of Basic Data-Driven Fault Diagnosis and Process Monitoring Methods on the Benchmark Tennessee Eastman Process,” J. Process Control, 22(9), pp. 1567–1581. [CrossRef]
Eslamloueyan, R., 2011, “Designing a Hierarchical Neural Network Based on Fuzzy Clustering for Fault Diagnosis of the Tennessee–Eastman Process,” Appl. Soft Comput., 11(1), pp. 1407–1415. [CrossRef]
Lau, C. K., Ghosh, K., Hussain, M. A., and Hassan, C. R. C., 2013, “Fault Diagnosis of Tennessee Eastman Process With Multi-Scale PCA and ANFIS,” Chemom. Intell. Lab. Syst., 120, pp. 1–14. [CrossRef]
Rato, T. J., and Reis, M. S., 2013, “Fault detection in the Tennessee Eastman Benchmark Process Using Dynamic Principal Components Analysis Based on Decorrelated Residuals,” Chemom. Intell. Lab. Syst., 125, pp. 101–108. [CrossRef]

Figures

Grahic Jump Location
Fig. 1

Classification results

Grahic Jump Location
Fig. 2

Structure of DROS-ELM network

Grahic Jump Location
Fig. 3

Prediction results of DROS-ELM and OS-ELM

Grahic Jump Location
Fig. 4

Flow diagram of TE process

Grahic Jump Location
Fig. 5

Variable trend under fault IDV(4) and IDV(5) before feature reconstruction

Grahic Jump Location
Fig. 6

Extracted variable trend under fault IDV(4) and IDV(5) after feature reconstruction

Grahic Jump Location
Fig. 7

Comparison results of detection time for IDV(1)

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In