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

• Published in: Journal of electronic testing Vol. 37; no. 2; pp. 215 - 223
• Main Authors: Sun, Lu, Li, Yang, Du, Han, Liang, Peipei, Nian, Fushun
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.04.2021; Springer Nature B.V
• Subjects: Algorithms; Amplifiers; Analog circuits; Broadband; CAE) and Design; Circuits and Systems; Cluster analysis; Clustering; Computer-Aided Engineering (CAD; Electrical Engineering; Engineering; Fault diagnosis; Fault location; High temperature environments; Low noise; Markov chains; Radio frequency; Support vector machines; Vector quantization; HMM; PHM (Prognostic Health Management); SVM; LNA; K-means Clustering
• ISSN: 0923-8174; 1573-0727
• DOI: 10.1007/s10836-021-05938-0

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.