Data-driven root cause diagnosis of faults in process industries

Abstract Data driven fault detection and diagnosis methods become more and more attractive in modern industries especially process industries. They can not only guarantee safe operation but also greatly improve product quality. For example, dynamic principal component analysis models and reconstruction based contribution are widely applicable in many occasions. However, there is one issue which does not receive enough attention, namely locating the root cause of a fault when it occurs. In this paper, a framework of root cause location is proposed to address this issue, including both stationary faults and nonstationary faults. A case study on Tennessee Eastman process is used to demonstrate the usage and effectiveness of these approaches. Results show the proposed framework is valid.

[1]  Si-Zhao Joe Qin,et al.  Survey on data-driven industrial process monitoring and diagnosis , 2012, Annu. Rev. Control..

[2]  Sirish L. Shah,et al.  Root cause diagnosis of plant-wide oscillations using the concept of adjacency matrix , 2009 .

[3]  S. Joe Qin,et al.  Root cause diagnosis of plant-wide oscillations using Granger causality , 2014 .

[4]  A. Negiz,et al.  Statistical monitoring of multivariable dynamic processes with state-space models , 1997 .

[5]  Ping Zhang,et al.  Subspace method aided data-driven design of fault detection and isolation systems , 2009 .

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

[7]  Tianyou Chai,et al.  Dynamic time warping based causality analysis for root-cause diagnosis of nonstationary fault processes , 2015 .

[8]  Michael Baldea,et al.  Root Cause Diagnosis of Plant-Wide Oscillations Based on Information Transfer in the Frequency Domain , 2016 .

[9]  Qiang Liu,et al.  Comprehensive monitoring of nonlinear processes based on concurrent kernel projection to latent structures , 2016, IEEE Transactions on Automation Science and Engineering.

[10]  Seongkyu Yoon,et al.  Fault diagnosis with multivariate statistical models part I: using steady state fault signatures , 2001 .

[11]  Donghua Zhou,et al.  A New Method of Dynamic Latent-Variable Modeling for Process Monitoring , 2014, IEEE Transactions on Industrial Electronics.

[12]  Jie Yu,et al.  A novel dynamic bayesian network‐based networked process monitoring approach for fault detection, propagation identification, and root cause diagnosis , 2013 .

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

[14]  Jianfeng Feng,et al.  Granger causality vs. dynamic Bayesian network inference: a comparative study , 2009, BMC Bioinformatics.

[15]  Tao Yuan,et al.  Nonstationarity and cointegration tests for fault detection of dynamic processes , 2014 .

[16]  Nina F. Thornhill,et al.  Finding the Direction of Disturbance Propagation in a Chemical Process Using Transfer Entropy , 2007, IEEE Transactions on Control Systems Technology.

[17]  D.M. Mount,et al.  An Efficient k-Means Clustering Algorithm: Analysis and Implementation , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Sirish L. Shah,et al.  Detection and Diagnosis of Plant-wide Oscillations From Industrial Data using the Spectral Envelope Method ? , 2007 .

[19]  Donald J. Berndt,et al.  Using Dynamic Time Warping to Find Patterns in Time Series , 1994, KDD Workshop.

[20]  Donghua Zhou,et al.  Generalized Reconstruction-Based Contributions for Output-Relevant Fault Diagnosis With Application to the Tennessee Eastman Process , 2011, IEEE Transactions on Control Systems Technology.

[21]  Tianyou Chai,et al.  Multi-directional reconstruction based contributions for root-cause diagnosis of dynamic processes , 2014, 2014 American Control Conference.

[22]  John F. MacGregor,et al.  Process monitoring and diagnosis by multiblock PLS methods , 1994 .

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

[24]  H. White,et al.  Granger Causality, Exogeneity, Cointegration, and Economic Policy Analysis , 2011 .

[25]  Christos Georgakis,et al.  Disturbance detection and isolation by dynamic principal component analysis , 1995 .

[26]  Tongwen Chen,et al.  Methods for root cause diagnosis of plant‐wide oscillations , 2014 .

[27]  Tianyou Chai,et al.  Data-Driven Soft-Sensor Modeling for Product Quality Estimation Using Case-Based Reasoning and Fuzzy-Similarity Rough Sets , 2014, IEEE Transactions on Automation Science and Engineering.

[28]  Feng Qian,et al.  Monitoring for Nonlinear Multiple Modes Process Based on LL-SVDD-MRDA , 2014, IEEE Transactions on Automation Science and Engineering.