Data-driven fault detection with process topology for fault identification

Abstract In this paper a fault diagnosis framework based on detection with feature extraction methods and identification based on data-driven process topology methods was investigated. A simulation of a simple system consisting of two tanks with heat exchangers was used to generate data for normal operating conditions and a number of faults. Fault detection methods included principal component analysis and kernel principal component analysis feature extraction with Shewhart, cumulative sum and exponentially weighted moving average monitoring charts. Process topology information was extracted with linear cross-correlation, partial cross-correlation and transfer entropy. Connectivity maps were constructed to identify possible fault propagation paths to aid root cause analysis and changes in connectivity structure due to faults were exploited for fault identification. Kernel principal component analysis with a CUSUM chart gave the best detection performance, while connectivity graphs based on partial correlation gave an accurate representation of the system and assisted fault identification.

[1]  Theodora Kourti,et al.  Process analysis, monitoring and diagnosis, using multivariate projection methods , 1995 .

[2]  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.

[3]  Aiguo Wang,et al.  A partial correlation-based Bayesian network structure learning algorithm under linear SEM , 2011, Knowl. Based Syst..

[4]  Nina F. Thornhill,et al.  A practical method for identifying the propagation path of plant-wide disturbances , 2008 .

[5]  George C. Runger,et al.  Designing a Multivariate EWMA Control Chart , 1997 .

[6]  Nina F. Thornhill,et al.  Specifying the directionality of fault propagation paths using transfer entropy , 2004 .

[7]  Raghunathan Rengaswamy,et al.  A review of process fault detection and diagnosis: Part III: Process history based methods , 2003, Comput. Chem. Eng..

[8]  Manabu Kano,et al.  Comparison of multivariate statistical process monitoring methods with applications to the Eastman challenge problem , 2002 .

[9]  C. Yoo,et al.  Nonlinear process monitoring using kernel principal component analysis , 2004 .

[10]  Leo H. Chiang,et al.  Process monitoring using causal map and multivariate statistics: fault detection and identification , 2003 .

[11]  Hector Budman,et al.  Fault detection, identification and diagnosis using CUSUM based PCA , 2011 .

[12]  Q. Mcnemar.  Note on the sampling error of the difference between correlated proportions or percentages , 1947, Psychometrika.