Diagnosis of Incipient Faults in Nonlinear Analog Circuits

Abstract

Considering the problem to diagnose incipient faults in nonlinear analog circuits, a novel approach based on fractional correlation is proposed and the application of the subband Volterra series is used in this paper. Firstly, the subband Volterra series is calculated from the input and output sequences of the circuit under test (CUT). Then the fractional correlation functions between the fault-free case and the incipient faulty cases of the CUT are derived. Using the feature vectors extracted from the fractional correlation functions, the hidden Markov model (HMM) is trained. Finally, the well-trained HMM is used to accomplish the incipient fault diagnosis. The simulations illustrate the proposed method and show its effectiveness in the incipient fault recognition capability.

Soft Fault Feature Extraction in Nonlinear Analog Circuit Fault Diagnosis

“…Therefore, V_GS in full band is difficult to find the soft faults and cannot be used to extract the soft fault features. 13.0-13.5 (7) 11.2-11.5 (6,11) Circuits Syst Signal Process (7) 13.2-13.5 (8,2) Detection of soft faults is satisfactory using V_STFT. The results of cases 3-26 show that most SNRs F (except fault 26) are smaller than SNR f aulty-f ree (15.7 in the first order and 15.8 in the second order).…”

“…It is a logarithm amplifier circuit shown in Fig. 8 [8]. The simulation conditions are the same as simulation 1.…”

“…Furthermore, the tolerance of components in analog circuits is another factor affecting diagnosis results. Therefore, it is difficult to extract the fault features of nonlinear analog circuits and the diagnosis results are not satisfactory [8,30].…”

Diagnostics of incipient faults in analog circuits

“…Xu et al [2] has proposed to use the output voltage, autoregressive-moving average (ARMA) coefficients, a n d w a v e l e t t r a n s f o r m c o e f f i c i e n t s a s t h e combinational feature vector whose dimensions were reduced by linear discriminant analysis (LDA) to train hidden Markov model (HMM) for incipient fault diagnosis of analog circuits. Deng et al [3] has proposed to use the fault features extracted based on the fractional correlation and the sub-band Volterra series to train HMM for incipient fault diagnosis of nonlinear analog circuits. The aforementioned incipient fault diagnosis methods are all based on HMM, and the difference mainly lies in their ways of extracting the feature.…”

Design an Identification Function to Reduce the Computational Resources on the Testing Process of an Analog Electronic Circuit

“…It is intuitive and proves to be effective on many occasions. Other approaches use neural networks [4], [10]- [12], classification methods that are based on a support vector machine, etc. [13].…”