Signed Directed Graph and Qualitative Trend Analysis Based Fault Diagnosis in Chemical Industry

Abstract In the past 30 years, signed directed graph (SDG), one of the qualitative simulation technologies, has been widely applied for chemical fault diagnosis. However, SDG based fault diagnosis, as any other qualitative method, has poor diagnostic resolution. In this paper, a new method that combines SDG with qualitative trend analysis (QTA) is presented to improve the resolution. In the method, a bidirectional inference algorithm based on assumption and verification is used to find all the possible fault causes and their corresponding consistent paths in the SDG model. Then an improved QTA algorithm is used to extract and analyze the trends of nodes on the consistent paths found in the previous step. New consistency rules based on qualitative trends are used to find the real causes from the candidate causes. The resolution can be improved. This method combines the completeness feature of SDG with the good diagnostic resolution feature of QTA. The implementation of SDG-QTA based fault diagnosis is done using the integrated SDG modeling, inference and post-processing software platform. Its application is illustrated on an atmospheric distillation tower unit of a simulation platform. The result shows its good applicability and efficiency.

[1]  Nicolás J. Scenna,et al.  Fault diagnosis for a MSF using a SDG and fuzzy logic , 2003 .

[2]  A qualitative shape analysis formalism for monitoring control loop performance , 2001 .

[3]  Venkat Venkatasubramanian,et al.  Signed Digraph based Multiple Fault Diagnosis , 1997 .

[4]  Venkat Venkatasubramanian,et al.  PCA-SDG based process monitoring and fault diagnosis , 1999 .

[5]  Raghunathan Rengaswamy,et al.  Fault Diagnosis by Qualitative Trend Analysis of the Principal Components , 2005 .

[6]  Chuei-Tin Chang,et al.  Fuzzy diagnosis method for control systems with coupled feed forward and feedback loops , 2006 .

[7]  Wu Chong-guang.  Integrated SDG Modeling, Inference and Post-Processing Software Platform , 2003 .

[8]  C. McGreavy,et al.  The application of fuzzy qualitative simulation in safety and operability assessment of process plants , 1996 .

[9]  Chonghun Han,et al.  Fault diagnosis of the multi-stage flash desalination process based on signed digraph and dynamic partial least square , 2008 .

[10]  Raghunathan Rengaswamy,et al.  A syntactic pattern-recognition approach for process monitoring and fault diagnosis , 1995 .

[11]  Liang-Sun Lee,et al.  Use of Fuzzy Cause-Effect Digraph for Resolution Fault Diagnosis for Process Plants. 1. Fuzzy Cause-Effect Digraph , 1995 .

[12]  Eiji O'Shima,et al.  A graphical approach to cause and effect analysis of chemical processing systems , 1980 .

[13]  M. Iri,et al.  An algorithm for diagnosis of system failures in the chemical process , 1979 .

[14]  Gary J. Powers,et al.  Computer-aided Synthesis of Fault-trees , 1977, IEEE Transactions on Reliability.

[15]  Raghunathan Rengaswamy,et al.  Consistent malfunction diagnosis inside control loops using signed directed graphs , 2003 .

[16]  Jun Chen,et al.  A self-validating control system based approach to plant fault detection and diagnosis , 2001 .

[17]  Raghunathan Rengaswamy,et al.  A review of process fault detection and diagnosis: Part I: Quantitative model-based methods , 2003, Comput. Chem. Eng..

[18]  Tao Xia,et al.  SDG multiple fault diagnosis by real-time inverse inference , 2005, Reliab. Eng. Syst. Saf..

[19]  Liang-Sun Lee,et al.  Use Fuzzy of Cause-Effect Digraph for Resolution Fault Diagnosis for Process Plants. 2. Diagnostic Algorithm and Applications , 1995 .

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

[21]  Qin Yan,et al.  Study on Fault Diagnosis Based on the Qualitative / Quantitative Model of SDG and Genetic Algorithm , 2006, 2006 International Conference on Machine Learning and Cybernetics.

[22]  Raghunathan Rengaswamy,et al.  Fuzzy-logic based trend classification for fault diagnosis of chemical processes , 2003, Comput. Chem. Eng..

[23]  Mark A. Kramer,et al.  A rule‐based approach to fault diagnosis using the signed directed graph , 1987 .

[24]  Sylvie Charbonnier,et al.  Trends extraction and analysis for complex system monitoring and decision support , 2005, Eng. Appl. Artif. Intell..

[25]  Chuei-Tin Chang,et al.  A fuzzy-logic based fault diagnosis strategy for process control loops , 2003 .

[26]  David M. Himmelblau,et al.  The possible cause and effect graphs (PCEG) model for fault diagnosis—I. Methodology , 1994 .

[27]  Venkat Venkatasubramanian,et al.  Automatic generation of qualitative descriptions of process trends for fault detection and diagnosis , 1991 .

[28]  Raghunathan Rengaswamy,et al.  A review of process fault detection and diagnosis: Part II: Qualitative models and search strategies , 2003, Comput. Chem. Eng..

[29]  Raghunathan Rengaswamy,et al.  A signed directed graph-based systematic framework for steady-state malfunction diagnosis inside control loops , 2006 .

[30]  Raghunathan Rengaswamy,et al.  A Signed Directed Graph and Qualitative Trend Analysis-Based Framework for Incipient Fault Diagnosis , 2007 .

[31]  Xiao De-yun.  Probabilistic SDG Model and Approach to Inference for Fault Analysis , 2006 .