A Prognostics and Health Management Strategy for Complex Electronic Systems

It is important to find the potential failure of subsystems or components for complex electronic systems such as radar, aviation systems, and then repair or replace them before the whole system fails. To repair or replace potential failure subsystems or circuits before a whole system fails, a new online prognostics and health management (PHM) system has been developed based on Mahalanobis distance (MD), the multisignal model, and the least squares support vector machine (LS-SVM). MD is used as a health index for monitoring each test parameter in the system. If health indices indicate that the test parameters will fail soon, the multisignal model is triggered to locate the potential failure subsystems at the system level. Then, LS-SVM is triggered to identify the failure modes or components in the potential subsystems. This paper presents an experiment with a radar transmitter system, which shows that the new PHM strategy can find potential failure circuits or components before the whole system fails. This method is useful for condition-based maintenance (CBM) of complex electronic systems.

[1]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[2]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machines , 2002 .

[3]  Long Bing Researching on method of modeling complex electronic system for testability , 2009 .

[4]  T. Martin McGinnity,et al.  Fault diagnosis of electronic systems using intelligent techniques: a review , 2001, IEEE Trans. Syst. Man Cybern. Part C.

[5]  Bin Zhang,et al.  Machine Condition Prediction Based on Adaptive Neuro–Fuzzy and High-Order Particle Filtering , 2011, IEEE Transactions on Industrial Electronics.

[6]  Michael Dewey,et al.  Next generation test system architectures for Depot and O-level test , 2011, 2011 IEEE AUTOTESTCON.

[7]  Krishna R. Pattipati,et al.  Computationally efficient algorithms for multiple fault diagnosis in large graph-based systems , 2003, IEEE Trans. Syst. Man Cybern. Part A.

[8]  Michael G. Pecht,et al.  A prognostics and health management roadmap for information and electronics-rich systems , 2010, Microelectron. Reliab..

[9]  Bing Long,et al.  A Hierarchical Modeling and Fault Diagnosis Technique for Complex Electronic Devices , 2009, 2009 IEEE Circuits and Systems International Conference on Testing and Diagnosis.

[11]  Bin Zhang,et al.  A .NET framework for an integrated fault diagnosis and failure prognosis architecture , 2010, 2010 IEEE AUTOTESTCON.

[12]  W. Marsden I and J , 2012 .

[13]  Michael Pecht,et al.  Physics-of-failure-based prognostics for electronic products , 2009 .

[14]  Yaow-Ming Chen,et al.  Online Failure Prediction of the Electrolytic Capacitor for LC Filter of Switching-Mode Power Converters , 2008, IEEE Transactions on Industrial Electronics.

[15]  M.G. Pecht,et al.  Prognostics and health management of electronics , 2008, IEEE Transactions on Components and Packaging Technologies.

[16]  Tommy W. S. Chow,et al.  Approach to Fault Identification for Electronic Products Using Mahalanobis Distance , 2010, IEEE Transactions on Instrumentation and Measurement.

[17]  Mohammed A. Alam,et al.  Prognostics of Failures in Embedded Planar Capacitors using Model-Based and Data-Driven Approaches , 2011 .

[18]  Lei Liu,et al.  Research on performance comparison of two MFD algorithms: Research on performance comparison of two MFD algorithms , 2011 .

[19]  Qiang Miao,et al.  Research on features for diagnostics of filtered analog circuits based on LS-SVM , 2011, 2011 IEEE AUTOTESTCON.

[20]  Youren Wang,et al.  A novel approach of analog circuit fault diagnosis using support vector machines classifier , 2011 .

[21]  Shulin Tian,et al.  Least squares support vector machine based Analog-Circuit Fault Diagnosis using wavelet transform as preprocessor , 2008, 2008 International Conference on Communications, Circuits and Systems.