On-line process monitoring during transient operations using weighted distance Auto Associative Bilateral Kernel Regression.

In this paper, a new data-driven auto associative bilateral kernel regression (AABKR) method based on weighted distance is proposed for the on-line monitoring of transient process operations. A bilateral approach to the kernel regression formulates a representative model that uses both the spatial and temporal information in the data, and a new weighted-distance algorithm captures temporal information. Moreover, an adaptive approach is proposed to dynamically compensate for faulty process inputs in the bilateral kernel evaluations, providing a robust model with little spillover. The proposed weighted-distance AABKR is first implemented using numerical process examples and then applied to the transient start-up operation of a nuclear power plant. Monte Carlo simulation results are provided by randomly assigning fault sensors and fault magnitudes. The results demonstrate the feasibility and efficiency of the proposed method.

[1]  Fan Li,et al.  Model-based monitoring and fault diagnosis of fossil power plant process units using Group Method of Data Handling. , 2009, ISA transactions.

[2]  S. Demir,et al.  ON THE ADAPTIVE NADARAYA-WATSON KERNEL REGRESSION ESTIMATORS , 2010 .

[3]  S. Qin.  Recursive PLS algorithms for adaptive data modeling , 1998 .

[4]  Ming Zhang,et al.  Multiresolution Bilateral Filtering for Image Denoising , 2008, IEEE Transactions on Image Processing.

[5]  Chunhui Zhao,et al.  Fault-relevant Principal Component Analysis (FPCA) method for multivariate statistical modeling and process monitoring , 2014 .

[6]  Gyunyoung Heo,et al.  Internal leakage detection for feedwater heaters in power plants using neural networks , 2012, Expert Syst. Appl..

[7]  Jie Chen,et al.  Observer-based fault detection and isolation: robustness and applications , 1997 .

[8]  Mohamed Benouaret,et al.  Sensor fault detection, isolation and reconstruction using nonlinear principal component analysis , 2007, Int. J. Autom. Comput..

[9]  Junhong Li,et al.  Improved kernel principal component analysis for fault detection , 2008, Expert Syst. Appl..

[10]  J. Wesley Hines,et al.  Development and Application of Fault Detectability Performance Metrics for Instrument Calibration Verification and Anomaly Detection , 2006 .

[11]  Xiaoguang Chen,et al.  A self-adaptive alarm method for tool condition monitoring based on Parzen window estimation , 2013 .

[12]  G. Irwin,et al.  Process monitoring approach using fast moving window PCA , 2005 .

[13]  E. Zio,et al.  Robust signal reconstruction for condition monitoring of industrial components via a modified Auto Associative Kernel Regression method , 2015 .

[14]  Enrico Zio,et al.  Fuzzy logic for signal prediction in nuclear systems , 2003 .

[15]  Jie Chen,et al.  Robust residual generation for model-based fault diagnosis of dynamic systems. , 1995 .

[16]  Jin Jiang,et al.  Applications of fault detection and diagnosis methods in nuclear power plants: A review , 2011 .

[17]  Thomas F. Edgar,et al.  Identification of faulty sensors using principal component analysis , 1996 .

[18]  Enrico Zio,et al.  A randomized model ensemble approach for reconstructing signals from faulty sensors , 2011, Expert Syst. Appl..

[19]  S. Mohanty.  Artificial neural network based system identification and model predictive control of a flotation column , 2009 .

[20]  Steven X. Ding,et al.  Model-based Fault Diagnosis Techniques: Design Schemes, Algorithms, and Tools , 2008 .

[21]  Soon Heung Chang,et al.  Detection of process anomalies using an improved statistical learning framework , 2011, Expert Syst. Appl..

[22]  Frédo Durand,et al.  Bilateral Filtering: Theory and Applications , 2009, Found. Trends Comput. Graph. Vis..

[23]  Gyunyoung Heo,et al.  Development of nuclear forensic models using kernel regression , 2017 .

[24]  Andrew W. Moore,et al.  Locally Weighted Learning , 1997, Artificial Intelligence Review.

[25]  Riccardo Muradore,et al.  A PLS-Based Statistical Approach for Fault Detection and Isolation of Robotic Manipulators , 2012, IEEE Transactions on Industrial Electronics.

[26]  Haixian Wang,et al.  Image Denoising Using Trivariate Shrinkage Filter in the Wavelet Domain and Joint Bilateral Filter in the Spatial Domain , 2009, IEEE Transactions on Image Processing.

[27]  Enrico Zio,et al.  Comparison of Data-Driven Reconstruction Methods For Fault Detection , 2015, IEEE Transactions on Reliability.

[28]  Singiresu S Rao,et al.  Applied numerical methods for engineers and scientists , 2001 .

[29]  Soon Heung Chang,et al.  Thermal power estimation by fouling phenomena compensation using wavelet and principal component analysis , 2000 .

[30]  Silvio Simani,et al.  Model-based fault diagnosis in dynamic systems using identification techniques , 2003 .

[31]  Ismail Al turk Lutfiah,et al.  Modification of the adaptive Nadaraya-Watson kernel regression estimator , 2014 .

[32]  Steven X. Ding,et al.  Optimal fault detection for linear discrete time-varying systems , 2010, Autom..

[33]  J. Wesley Hines,et al.  VALIDATION OF ON-LINE MONITORING TECHNIQUES TO NUCLEAR PLANT DATA , 2007 .

[34]  Barry Lennox,et al.  Industrial application of neural networks — an investigation , 2001 .

[35]  Tomoaki Suzudo,et al.  Real-time nuclear power plant monitoring with neural network , 1998 .

[36]  Myung Seung Yang,et al.  EVALUATION OF PROLIFERATION RESISTANCE USING THE INPRO METHODOLOGY , 2007 .

[37]  Shu-Tao Xia,et al.  Entropy-based bilateral filtering with a new range kernel , 2017, Signal Process..

[38]  Steven X. Ding,et al.  Data-driven design of monitoring and diagnosis systems for dynamic processes: A review of subspace technique based schemes and some recent results , 2014 .

[39]  Moon‐Ghu Park,et al.  KERNEL-BASED NOISE FILTERING OF NEUTRON DETECTOR SIGNALS , 2007 .

[40]  Enrico Zio,et al.  A support vector machine integrated system for the classification of operation anomalies in nuclear components and systems , 2007, Reliab. Eng. Syst. Saf..

[41]  Yang Hu,et al.  Fault detection based on signal reconstruction with Auto-Associative Extreme Learning Machines , 2017, Eng. Appl. Artif. Intell..

[42]  Man Gyun Na,et al.  ESTIMATION OF BREAK LOCATION AND SIZE FOR LOSS OF COOLANT ACCIDENTS USING NEURAL NETWORKS , 2004 .

[43]  Jian Sun,et al.  Guided Image Filtering , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[44]  G. S. Watson,et al.  Smooth regression analysis , 1964 .

[45]  Xuefeng Yan,et al.  Plant-wide process monitoring based on mutual information-multiblock principal component analysis. , 2014, ISA transactions.

[46]  Rolf Isermann,et al.  Fault-Diagnosis Applications: Model-Based Condition Monitoring: Actuators, Drives, Machinery, Plants, Sensors, and Fault-tolerant Systems , 2011 .

[47]  S. Joe Qin,et al.  Consistent dynamic PCA based on errors-in-variables subspace identification , 2001 .

[48]  Yang Song,et al.  Parity space-based fault detection for linear discrete time-varying systems with unknown input , 2015, Autom..

[49]  E. Nadaraya.  On Estimating Regression , 1964 .

[50]  Hamid Reza Karimi,et al.  Fault Detection for Linear Discrete Time-Varying Systems Subject to Random Sensor Delay: A Riccati Equation Approach , 2018, IEEE Transactions on Circuits and Systems I: Regular Papers.

[51]  Ma Yao,et al.  On-line monitoring of batch processes using generalized additive kernel principal component analysis , 2015 .

[52]  Weihua Li,et al.  Recursive PCA for adaptive process monitoring , 1999 .

[53]  Richard D. Braatz,et al.  Fault Detection and Diagnosis in Industrial Systems , 2001 .

[54]  E. Nadaraya.  On Non-Parametric Estimates of Density Functions and Regression Curves , 1965 .

[55]  Gyun Young Heo.  CONDITION MONITORING USING EMPIRICAL MODELS: TECHNICAL REVIEW AND PROSPECTS FOR NUCLEAR APPLICATIONS , 2008 .

[56]  Manoj Kumar Tiwari,et al.  Leak detection of pipeline: An integrated approach of rough set theory and artificial bee colony trained SVM , 2012, Expert Syst. Appl..

[57]  Enrico Zio,et al.  Fault Detection in Nuclear Power Plants Components by a Combination of Statistical Methods , 2013, IEEE Transactions on Reliability.

[58]  Si-Zhao Joe Qin,et al.  Reconstruction-based contribution for process monitoring , 2009, Autom..