Control Change Cause Analysis-Based Fault Diagnostic Approach
Gang-Gang Wu, Zong-Xiao Yang†, Gen-Sheng Li, and Lei Song
Institute of Systems Science and Engineering, Henan Engineering Laboratory of Wind Power Systems,
Henan University of Science and Technology
No.48 Xiyuan Road, Jianxi District, Luoyang 471003, PR China
How to identify the fault causes quickly and improve the efficiency of maintenance, which can reduce the fault disaster, has always been one of the key problems in equipments fault diagnosis. In this paper, a new qualitative fault diagnostic approach based on control change cause analysis (3CA) is proposed to identify the fault causes and fault risk index, which can be utilized to control the risk of equipment fault. We employed an existing method that was events and conditional factors analysis (ECFA+) to identify the analysis objects of 3CA, and put forward integrated methods including first principle-best practices approach, barrier failure analysis and prioritization rating code (PRC) matrix to accomplish control analysis, change analysis and significance rating of 3CA respectively, and those technical methods could be used to build the procedure diagram of identifying the content in each column of 3CA worksheet. According to the procedure of 3CA, we built a worksheet of 3CA for a vehicle engine fault, then fault causes and significance rating on behalf of the rating of fault risk index were determined. Meanwhile fault risk index had also been used to rank the fault causes, accomplishing fault diagnosis and verifying the availability or this method for fault diagnosis. The proposed approach can be able to identify fault causes of different fault modes that they have different risk index, and provide the fault causes rating that is the foundations of troubleshooting, which can mitigate and control fault disaster.
-  S. Abe, “Transport accident investigation status and issues,” J. of Disaster Research, Vol.6, No.2, pp. 185-192, 2011.
-  Y. Kawata, “Special Issue on safety science: comprehensive approach to social disasters and natural disasters,” J. of Disaster Research, Vol.6, No.2, p. 175, 2011.
-  E. Y. Chow and A. S. Willsky, “Analytical redundancy and the design of robust failure detection systems,” IEEE Trans. on Automatic Control, Vol.29, No.7, pp. 603-614, 1984.
-  J. J. Gertler,“Survey of model-based failure detection and isolation in complex plants,” IEEE Control Systems Magazine, Vol.8, No.6, pp. 3-11, 1988.
-  J. J. Gertler, M. Costin, X. W. Fang, R. Hira, Z. Kowalczuk, and Q. Luo, “Model-based on-board fault detection and diagnosis for automotive engines,” Control Engineering Practice, Vol.1, No.1, pp. 3-17, 1993.
-  M. Nyberg, “Model-based diagnosis of an automotive engine using several types of fault models,” IEEE Trans. on Control Systems Technology, Vol.10, No.5, pp. 679-689, 2002.
-  T. Escobet, D. Feroldi, S. Lira, V. Puig, J. Quevedo, J. Riera, and M. Serra, “Model-based fault diagnosis in PEM fuel cell systems,” J. of Power Sources, Vol.192, pp. 216-223, 2009.
-  R. Doraiswami, “Performance monitoring and fault prediction using a linear predictive coding algorithm,” Automatica, Vol.29, No.4, pp. 1115-1120, 1993.
-  R. Isermann, “Supervision, fault-detection and fault-diagnosis methods-an introduction,” Control Engineering Practice, Vol.5, No.5, pp. 639-652, 1997.
-  F. Taya and L. X. Shen, “Fault diagnosis based on rough set theory,” Engineering Applications of Artificial Intelligence, Vol.16, pp. 39-43, 2003.
-  B. Ayhan, M.Y. Chow, and M. H. Song, “Multiple signature processing-based fault detection schemes for broken rotor bar in induction motors,” IEEE Trans. on Energy Conversion, Vol.20, No.2, pp. 336-343, 2005.
-  D. Ceglarek, J. Shi, and S. M. Wu, “A knowledge-based diagnostic approach for the launch of the auto-body assembly process,” J. of Engineering for Industry, Vol.116, pp. 491-499, 1994.
-  R. Q. Li, J. Chen, and X. Wu, “Fault diagnosis of rotating machinery using knowledge-based fuzzy neural network,” Applied Mathematics and Mechanics, Vol.27, No.1, pp. 99-108, 2006.
-  P. V. Rodri’guez, and A. Arkkio, “Detection of stator winding fault in induction motor using fuzzy logic,” Applied Soft Computing, Vol.8, pp. 1112-1120, 2008.
-  S. S. Bathaie, Z. N. Vanini, and K. Khorasani, “Dynamic neural network-based fault diagnosis of gas turbine engines,” Neurocomputing, Vol.125, pp. 153-165, 2014.
-  Y. P. Huang, Y. S. Wang, and R. J. Zhang, “Fault troubleshooting using bayesian network and multicriteria decision analysis,” Advances in Mechanical Engineering, Vol.6, No.7, pp. 1-7, 2014.
-  Z. W. Gao, C. Cecati, and S. X. Ding, “A survey of fault diagnosis and fault-tolerant techniques – part I: fault diagnosis with model-based and signal-based approaches,” IEEE Trans. Industrial Electronics, Vol.62, No.6, pp. 3757-3767, 2015.
-  Z. W. Gao, C. Cecati, and S. X. Ding, “A survey of fault diagnosis and fault-Tolerant techniques—part II: fault diagnosis with knowledge-based and hybrid/active approaches,” IEEE Trans. Industrial Electronics, Vol.62, No.6, pp. 3768-3774, 2015.
-  P. Katsakiori, G. Sakellaropoulos, and E. Manatakis, “Towards an Evaluation of Accident Investigation Methods in Terms of Their Alignment with Accident Causation Models,” Safety Science, Vol.47, pp. 1007-1015, 2009.
-  M. Gerbec, “Supporting organizational learning by comparing activities and outcomes of the safety-management system,” J. of Loss Prevention in the Process Industries, Vol.26, No.1, pp. 1113-1127, 2013.
-  G. Nano and M. Derudi, “A critical analysis of techniques for the reconstruction of workers accidents,” Chemical Engineering Trans., Vol.31, No.1, pp. 415-420, 2013.
-  G. Nano and M. Derudi, “Evaluation of workers accidents through risk analysis,” Chemical Engineering Trans., Vol.26, No.1, pp. 495-500, 2012.
-  S. Sklet, “Comparison of some selected methods for accident investigation,” J. of Hazardous Materials, Vol.111, pp. 29-37, 2004.
-  M. Eddleston and L. Scholefield, “Developments in geotechnical best practices in evaluating embankment dams,” Dams and Reservoirs, Vol.24, No.3, pp. 111-119, 2014.
-  S. D. Spray, “Deriving and applying generally applicable safety principles,” R. SAND98 -1590C - PF.1, 1998.
-  M. A. Dvorack and T. R. Jooes, “System safety assessments combining first principles and model based safety assessment methodologies,” R .SAND98 - 0162C, 1998.
-  S. Sklet, “Methods for Accident Investigation,” trondheim, Norwegian University of Science and Technology, 2002.
-  T. C. Kuo, H. Y. Ma, S. H. Huang, A. H. Hu, and C. S. Huang, “Barrier analysis for product service system using interpretive structural model,” Int. J. of Advanced Manufacturing Technology, Vol.49, No.1, pp. 407-417, 2010.
-  S. J. Lu, Y. Y. Wang, and Y. G. Teng, “Heavy metal pollution and ecological risk assessment of the paddy soils near a zink-lead mining area in Human,” Environmental Monitoring and Assessment, Vol.187, No10, pp. 627-631, 2015.
This article is published under a Creative Commons Attribution-NoDerivatives 4.0 International License.