Robust Fault Diagnosis for DK-2 Brake Based on Both Data-driven and Model

In this paper, according to the dynamic features of strong coupling, non-linearity and uncertainty for the DK-2 brake, a fault diagnosis strategy based on data-driven and model is proposed for online monitoring and fault isolation. Firstly, based on the analysis of structure and operating principle of the DK-2 brake, the model is established by using bond graph. Secondly, based on the large amount of historical data in normal operating condition, the empirical regression model which can be used to estimate the output by the observations is set up using the auto-associative kernel regression (AAKR). Thirdly, based on prediction intervals (PIs), the uncertainty of the system is described quantitatively in the regression model, so the robust on-line monitoring of the system is realized. Combining the two methods, the online monitoring and fault isolation of the DK-2 brake can be achieved. Finally, the strategy proposed in this paper is verified by simulation and the results show that it can realize online monitoring and fault isolation accurately and quickly.

[1]  Steven X. Ding,et al.  A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part II: Fault Diagnosis With Knowledge-Based and Hybrid/Active Approaches , 2015, IEEE Transactions on Industrial Electronics.

[2]  Zhang Jia-jie.  Fault Diagnosis of Faiveley Braking System Based on Analytical Redundancy Relations , 2013 .

[3]  Steven X. Ding,et al.  A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches , 2015, IEEE Transactions on Industrial Electronics.

[4]  Yan Shu-rong.  A Kind of Study Program of Fault Diagnosis Expert System for Brake of High-speed Train , 2010 .

[5]  Liu Yu-xiang.  Present status and development of locomotive brake in China , 2005 .

[6]  Giovanni Moroni,et al.  The Use of Adaptive PCA-Based Condition Monitoring Methods in Machining Processes , 2012 .

[7]  Dayu Yang,et al.  Anomaly-Based Intrusion Detection for SCADA Systems , 2006 .

[8]  Enrico Zio,et al.  Quantification of Signal Reconstruction Uncertainty in Fault Detection Systems , 2014 .

[9]  Wolfgang Borutzky,et al.  Bond Graph Methodology: Development and Analysis of Multidisciplinary Dynamic System Models , 2009 .

[10]  Enrico Zio,et al.  Genetic algorithm-based wrapper approach for grouping condition monitoring signals of nuclear power plant components , 2011, Integr. Comput. Aided Eng..

[11]  V. Sugumaran,et al.  Fault diagnosis of automobile hydraulic brake system using statistical features and support vector machines , 2015 .

[12]  Zhiwei Gao,et al.  From Model, Signal to Knowledge: A Data-Driven Perspective of Fault Detection and Diagnosis , 2013, IEEE Transactions on Industrial Informatics.

[13]  Zhang Yuan-min.  Fault Diagnosis of Electro-pneumatic Brake System Based on Neural Network Expert System , 2008 .

[14]  Nan Bai,et al.  Wind Turbine Gearbox Condition Monitoring with AAKR and Moving Window Statistic Methods , 2011 .

[15]  Michael Pecht,et al.  A probabilistic description scheme for rotating machinery health evaluation , 2010 .