Prognostics of Analog Filters Based on Particle Filters Using Frequency Features

Analog circuits play an important role in ensuring the availability of electronic systems. Unexpected circuit failures can lead to severe economic implications, and hence, prevention of circuit failures is imperative and highly desired. However, few methods have been suggested for predicting the remaining time till circuit failure. To address this challenge, a novel method for predicting the remaining useful performance (RUP) of analog filters is proposed. By analyzing the circuit’s response to a sweep signal, frequency features such as the center frequency, the lower pass-band limit, the upper pass-band limit, and the maximum frequency response, are extracted. Based on the extracted features, a fault indicator (FI) monitoring the degradation trend of the analog filters is developed for failure prognosis. Moreover, a model is developed based on the degradation trend exhibited by the FI. Particle filters approach is applied to model adaption and RUP prediction. Case studies demonstrating this approach are presented. The studies’ results show that (1) the proposed FI based on the frequency features can well characterize the degradation trend of the analog filters; and (2) the proposed prognostic approach can predict the RUP of analog filters with small error.

[1]  Bing Long,et al.  Diagnostics and Prognostics Method for Analog Electronic Circuits , 2013, IEEE Transactions on Industrial Electronics.

[2]  P. Mahalanobis.  On the generalized distance in statistics , 1936 .

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

[4]  Fredrik Gustafsson,et al.  Particle filters for positioning, navigation, and tracking , 2002, IEEE Trans. Signal Process..

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

[6]  M. Pecht,et al.  A Wireless Sensor System for Prognostics and Health Management , 2010, IEEE Sensors Journal.

[7]  Shigeyuki Sakane,et al.  Sensor Planning for Mobile Robot Localization - A Hierarchical Approach Using a Bayesian Network and a Particle Filter , 2008, IEEE Trans. Robotics.

[8]  Salvador Mir,et al.  Fault diagnosis of analog circuits based on machine learning , 2010, 2010 Design, Automation & Test in Europe Conference & Exhibition (DATE 2010).

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

[10]  Abhijit Chatterjee,et al.  Concurrent Device/Specification Cause–Effect Monitoring for Yield Diagnosis Using Alternate Diagnostic Signatures , 2012, IEEE Design & Test of Computers.

[11]  Shulin Tian,et al.  Diagnostics of Filtered Analog Circuits with Tolerance Based on LS-SVM Using Frequency Features , 2012, J. Electron. Test..

[12]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1972 .

[13]  Bin Zhang,et al.  Prediction of Machine Health Condition Using Neuro-Fuzzy and Bayesian Algorithms , 2012, IEEE Transactions on Instrumentation and Measurement.

[14]  Simon J. Godsill,et al.  Particle methods for Bayesian modeling and enhancement of speech signals , 2002, IEEE Trans. Speech Audio Process..

[15]  V. Makis,et al.  Condition monitoring and classification of rotating machinery using wavelets and hidden Markov models , 2007 .

[16]  Farzan Aminian,et al.  Analog fault diagnosis of actual circuits using neural networks , 2002, IEEE Trans. Instrum. Meas..

[17]  Kai Goebel,et al.  Comparison of prognostic algorithms for estimating remaining useful life of batteries , 2009 .

[18]  Camelia Hora,et al.  Diagnosis of Local Spot Defects in Analog Circuits , 2012, IEEE Transactions on Instrumentation and Measurement.

[19]  M. Pecht,et al.  Prognostics of Interconnect Degradation using RF Impedance Monitoring and Sequential Probability Ratio Test , 2010 .

[20]  Zhang Yi,et al.  One-class classifier based on SBT for analog circuit fault diagnosis , 2008 .

[21]  N. Gordon,et al.  Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .

[22]  Shulin Tian,et al.  Feature Vector Selection Method Using Mahalanobis Distance for Diagnostics of Analog Circuits Based on LS-SVM , 2012, J. Electron. Test..

[23]  Michael Isard,et al.  CONDENSATION—Conditional Density Propagation for Visual Tracking , 1998, International Journal of Computer Vision.

[24]  Aijun Zhang,et al.  Study on Combined Forecasting of Stabilized Platform Motion Attitude , 2012, 2012 International Conference on Computer Science and Electronics Engineering.

[25]  Farzan Aminian,et al.  A Modular Fault-Diagnostic System for Analog Electronic Circuits Using Neural Networks With Wavelet Transform as a Preprocessor , 2007, IEEE Transactions on Instrumentation and Measurement.

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

[27]  Deng Li.  Fault prognostic model based on grey relevance vector machine , 2012 .

[28]  T. W. Williams,et al.  Design for testability of analog/digital networks , 1989 .

[29]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1971 .

[30]  Jie Gu,et al.  Prognostics implementation of electronics under vibration loading , 2007, Microelectron. Reliab..

[31]  Dong Wang,et al.  Robust health evaluation of gearbox subject to tooth failure with wavelet decomposition , 2009 .

[32]  Michael Pecht,et al.  Prognostics-based health management for free air cooling of data centers , 2010, 2010 Prognostics and System Health Management Conference.

[33]  Fred J. Taylor,et al.  Electronic Filter Design Handbook, Fourth Edition (McGraw-Hill Handbooks) , 2006 .

[34]  M. Pecht,et al.  Precursor Parameter Identification for Insulated Gate Bipolar Transistor (IGBT) Prognostics , 2009, IEEE Transactions on Reliability.

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