Diagnosis of multiple and unknown faults using the causal map and multivariate statistics

Abstract Feature extraction is crucial for fault diagnosis and the use of complementary features allows for improved diagnostic performance. Most of the existing fault diagnosis methods only utilize data-driven and causal connectivity-based features of faults, whereas the important complementary feature of the propagation paths of faults is not incorporated. The propagation path-based feature is important to represent the intrinsic properties of faults and plays a significant role in fault diagnosis, particularly for the diagnosis of multiple and unknown faults. In this article, a three-step framework based on the modified distance (DI) and modified causal dependency (CD) is proposed to integrate the data-driven and causal connectivity-based features with the propagation path-based feature for diagnosing known, unknown, and multiple faults. The effectiveness of the proposed approach is demonstrated on the Tennessee Eastman process.

[1]  Roberto Brunelli,et al.  Face Recognition: Features Versus Templates , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Leo H. Chiang,et al.  Fault Detection and Diagnosis for Large -Scale Systems , 2001 .

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

[4]  Nina F. Thornhill,et al.  Advances and new directions in plant-wide disturbance detection and diagnosis , 2007 .

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

[6]  Rolf Isermann,et al.  Supervision, fault-detection and fault-diagnosis methods — An introduction , 1997 .

[7]  George D. C. Cavalcanti,et al.  Feature representation selection based on Classifier Projection Space and Oracle analysis , 2013, Expert Syst. Appl..

[8]  En Sup Yoon,et al.  Multiple-Fault Diagnosis Based on System Decomposition and Dynamic PLS , 2003 .

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

[10]  Leo H. Chiang,et al.  Fault diagnosis in chemical processes using Fisher discriminant analysis, discriminant partial least squares, and principal component analysis , 2000 .

[11]  Nina F. Thornhill,et al.  Cause-and-effect analysis in chemical processes utilizing XML, plant connectivity and quantitative process history , 2009, Comput. Chem. Eng..

[12]  Ali Cinar,et al.  Statistical process monitoring and disturbance diagnosis in multivariable continuous processes , 1996 .

[13]  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.

[14]  Ali Cinar,et al.  Multivariate statistical methods for monitoring continuous processes: assessment of discrimination power of disturbance models and diagnosis of multiple disturbances , 1995 .

[15]  Seongkyu Yoon,et al.  Statistical and causal model‐based approaches to fault detection and isolation , 2000 .

[16]  E. F. Vogel,et al.  A plant-wide industrial process control problem , 1993 .

[17]  Nina F. Thornhill,et al.  Diagnosis of plant-wide oscillation through data-driven analysis and process understanding , 2003 .