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Fault Diagnosis Method of Low Noise Amplifier Based on Support Vector Machine and Hidden Markov Model

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Abstract

Radio Frequency (RF) analog circuit failures often occur in broadband, high voltage and high temperature environment, so how to determine fault location and forecast the time which failure is going to occur is an important topic. Based on actual working data of RF Low Noise Amplifier (LNA), a kind of RF circuit fault diagnosis method is put forward with the combination of K-means Clustering, Support Vector Machine (SVM) and Hidden Markov Model (HMM).Simulation results show that the combined method has (3 ~ 4)% recognition accuracy higher than that of the single algorithm. The proposed prognosis method is highly efficient in RF analog circuit fault diagnosis.

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References

  1. Mukhopadhyay SC (2013) Intelligent sensing, instrumentation and measurements. Springer, Berlin Heidelberg

    Book  Google Scholar 

  2. Vasan A, Long B, Pecht M (2014) Experimental validation of LS SVM based fault identification in analog circuits using frequency features. In Proc. The 6th World Congress on Engineering Asset Management(pp.629–641) .Springer

  3. Han H, Wang H, Tian S, Zhang N (2013) A new analog circuit fault diagnosis method based on improved mahalanobis distance. J Electron Test 29:95–102. https://doi.org/10.1007/s10836-012-5342-z

    Article  Google Scholar 

  4. Tamilselvan P, Wang P (2013) Failure diagnosis using deep belief learning based health state classification. Reliability Engineering & System Safety 115:124–135. https://doi.org/10.1016/j.ress.2013.02.022

    Article  Google Scholar 

  5. Rathnapriya S, Manikandan V (2020) Remaining useful life prediction of analog circuit using improved unscented particle filter. J Electron Test 36:169–181. https://doi.org/10.1007/s10836-020-05870-9

    Article  Google Scholar 

  6. Binu D, Kariyappa BS (2019) Ridenn: a new rider optimization algorithm-based neural network for fault diagnosis in analog circuits. Instrumentation and Measurement, IEEE Transactions on Instrumentation and Measurement 68:2–26. https://doi.org/10.1109/TIM.2018.2836058

    Article  Google Scholar 

  7. Li Y, Zhang R, GuoY HP, Zhang M (2020) Nonlinear soft fault diagnosis of analog circuits based on rcca-svm. IEEE Access 8:60951–60963. https://doi.org/10.1109/ACCESS.2020.2982246

    Article  Google Scholar 

  8. Deng Y, Chai G (2016) Soft fault feature extraction in nonlinear analog circuit fault diagnosis. Circuits Systems & Signal Processing 35:4220–4248. https://doi.org/10.1007/s00034-016-0265-z

    Article  MathSciNet  MATH  Google Scholar 

  9. Cui J, Wang Y (2011) Analog circuit fault classification using improved one-against-one support vector machines. Metrology & Measurement Systems 18:569–582. https://doi.org/10.2478/v10178-011-0055-7

    Article  Google Scholar 

  10. Okoh C, Roy R, Mehnen J, Redding L (2014) Overview of remaining useful life prediction techniques in through-life engineering services. In Proc. The 6th Conference on Industrial Product Service Systems (pp.158–163)

  11. Abidine M B, Fergani B, Menhour I (2019) Activity Recognition from Smartphones Using Hybrid Classifier PCA-SVM-HMM. In Proc. 2019 International Conference on Wireless Networks and Mobile Communications (pp. 1–5)

  12. Jing Z, Yuzhu H, Weijia C, Polytechnic C (2018) Analog circuit fault diagnosis based on SVM optimized by SCA. Navigation and Control 33: 57–64. https://doi.org/10.13382/j.jemi.B1801840

  13. Yingrong Z, Wenbo S, Changfeng W, Yao W (2018) Based on improvement of HMM analog circuit fault prognostics model. Fire Control & Command Control 43:91–101

    Google Scholar 

  14. Cortes C, Vapnik V (1995) Support vector network. Machine Learning 20: 273–297. https://doi.org/10.1007/BF00994018

  15. Balwant A, Doye SS (2012) Hidden markov model for speech recognition using modified forward-backward re-estimation algorithm. International Journal of Computer Science Issues 9:242–247

    Google Scholar 

  16. Yu SZ (2010) Hidden semi-Markov models. Artif Intell 174:215–243

    Article  MathSciNet  Google Scholar 

  17. Nose K, Mizuno (2008) A 0.016 mm, 2.4 GHz RF signal quality measurement macro for RF test and diagnosis. IEEE Journal of Solid-State Circuits 43(4):1038–1046. https://doi.org/10.1109/jssc.2008.917566

  18. Ashraf M, Chetty G, Tran D, Sharma D (2012) A New Approach for Constructing Missing Features Values. International Journal of Intelligent Information Processing 3:110–118. https://doi.org/10.4156/ijiip.vol3.issue1.11

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Mechanical and Electrical Engineering, Xidian University, Xi’an, 710071, Shaanxi, China

    Lu Sun, Yang Li, Han Du & Peipei Liang

  2. The 41st research institute of China electronics science and technology group, 370200, Qingdao, China

    Fushun Nian

Authors
  1. Lu Sun
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  2. Yang Li
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  3. Han Du
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  4. Peipei Liang
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  5. Fushun Nian
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Correspondence to Yang Li.

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Responsible Editor: B. C. Kim.

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Sun, L., Li, Y., Du, H. et al. Fault Diagnosis Method of Low Noise Amplifier Based on Support Vector Machine and Hidden Markov Model. J Electron Test 37, 215–223 (2021). https://doi.org/10.1007/s10836-021-05938-0

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  • DOI: https://doi.org/10.1007/s10836-021-05938-0

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