An online data driven fault detection method in dynamic process based on sparse representation

With the development of science and industrial technologies, the intermittent fault has become the main fault of actual system, and the fault diagnosis on intermittent fault has progressed. However, with the increase in the complexity and uncertainty of modern engineering system, it is not feasible to establish accurate mathematical models. Thus, data-driven method is required for fault detection. Based on the sparsity of intermittent faults in some domains, an intermittent fault detection method based on sparse representation is proposed, with the online update of over-complete dictionary and fault detection threshold. With the simulation verification, the proposed method is suitable for intermittent fault detection in dynamic system.

[1]  Jing Qiu,et al.  A novel fault diagnosis approach based on environmental stress level evaluation , 2013 .

[2]  Yanyan Hu,et al.  Intermittent Fault Detection for Uncertain Networked Systems , 2013 .

[3]  F. Morant,et al.  Intermittent Failure Dynamics Characterization , 2012, IEEE Transactions on Reliability.

[4]  David L Donoho,et al.  Compressed sensing , 2006, IEEE Transactions on Information Theory.

[5]  Zhou Dong,et al.  Review of Intermittent Fault Diagnosis Techniques for Dynamic Systems , 2014 .

[6]  Dong Shen,et al.  Reliable H∞ control for uncertain nonlinear discrete-time systems subject to multiple intermittent faults in sensors and/or actuators , 2015, J. Frankl. Inst..

[7]  Dong Shen,et al.  Reliable H∞ control of discrete-time systems against random intermittent faults , 2016, Int. J. Syst. Sci..

[8]  Shuilong He,et al.  Compressed sparse time–frequency feature representation via compressive sensing and its applications in fault diagnosis , 2015 .

[9]  Zhou Dong.  Fault Diagnosis Techniques for Dynamic Systems , 2009 .

[10]  Donghua Zhou,et al.  On Designing $H_{\infty}$ Fault Detection Filter for Linear Discrete Time-Varying Systems , 2010, IEEE Transactions on Automatic Control.

[11]  Amitava Chatterjee,et al.  A sparse representation based approach for recognition of power system transients , 2014, Eng. Appl. Artif. Intell..

[12]  R. K. Mehra,et al.  Correspondence item: An innovations approach to fault detection and diagnosis in dynamic systems , 1971 .

[13]  E. Candès,et al.  Stable signal recovery from incomplete and inaccurate measurements , 2005, math/0503066.

[14]  Dong-Hua Zhou,et al.  Fault Diagnosis Techniques for Dynamic Systems: Fault Diagnosis Techniques for Dynamic Systems , 2009 .