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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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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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