A novel approach for analog fault diagnosis based on stochastic signal analysis and improved GHMM. (March 2016)

• Record Type: Journal Article
• Authors: Luo, Hui; Lu, Wei; Wang, Youren; Wang, Ling; Zhao, Xianlin
• Is Part Of: Measurement. Volume 81(2016:Mar.)
• Page Start: 26
• Page End: 35
• Publication Date: 2016-03
• Subjects: Analog circuit -- Fault diagnosis -- Stochastic process -- HMM -- Incipient soft fault; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2015.11.041
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: The linear circuit responses are considered as a stochastic process. Three stochastic feature series are extracted as fault features. An improved GHMM (Gaussian mixture HMM) classifier is designed. This method can diagnose ordinary and incipient soft faults effectively. Abstract: This paper presents a novel fault diagnosis method to diagnose ordinary soft fault and incipient soft fault for linear analog circuit. Due to the presence of tolerance, the parameter variation of analog component obeys a nearly normal distribution, and the linear circuit responses are considered as a stochastic process. Therefore, three kinds of stochastic series are extracted as fault features. Then, fault feature samples are divided into train samples and test samples. The relevant statistic values of them are calculated as final test samples in the stage of fault testing. In order to diagnose stochastic series effectively, an improved GHMM classifier is designed based on the fault occurrence characteristics of analog component. The stochastic sequences are thought as the observation symbols for the proposed GHMM. Simulation circuits and actual circuits are tested to prove the effectiveness of the new method.