Faults In Nonlinear Analog Circuits Research Articles

Soft Fault Diagnosis of Analog Circuit Based on EEMD and Improved MF-DFA

Aiming at the problems of nonlinearity and serious confusion of fault characteristics in analog circuits, this paper proposed a fault diagnosis method for an analog circuit based on ensemble empirical pattern decomposition (EEMD) and improved multifractal detrended fluctuations analysis (MF-DFA). This method consists of three steps: preprocessing, feature extraction, and fault classification identification. First, the EEMD decomposition preprocesses (denoises) the original signal; then, the appropriate IMF components are selected by correlation analysis; then, the IMF components are processed by the improved MF-DFA, and the fault feature values are extracted by calculating the multifractal spectrum parameters, and then the feature values are input to a support vector machine (SVM) for classification, which enables the diagnosis of soft faults in analog circuits. The experimental results show that the proposed EEMD-improved MF-DFA method effectively extracts the features of soft faults in nonlinear analog circuits and obtains a high diagnosis rate.

A Novel Feature Extraction Method for Soft Faults in Nonlinear Analog Circuits Based on LMD-GFD and KPCA

A Novel Feature Extraction Method for Soft Faults in Nonlinear Analog Circuits Based on LMD-GFD and KPCA

Diagnosis of Incipient Faults in Nonlinear Analog Circuits Based on High Order Moment Fractional Transform

Considering the problem to diagnose incipient faults in nonlinear analog circuits, a novel approach of fault feature-extracting based on HMFT (high order moment fractional transform) of the component parameter change is proposed. Firstly, the equivalence circuit model is established according to the topological structure and the voltage sensitivity of the circuit under test (CUT). Then the HMFT of component parameter changes are derived, which are used as fault features. Finally, using the extracted features, incipient fault diagnosis is accomplished. The simulations illustrate the proposed method and show its effectiveness in the incipient fault recognition capability.

A Method for Fault Diagnosis Based on Multidimensional Scaling (MDS) in Analog Circuits

Considering different fault state of the circuit in the nonlinear analog circuit fault diagnosis, the output response of the circuit varies. A Method for Fault Diagnosis of Nonlinear Analog Circuits on the basis of Multidimensional Scaling (MDS) was put forward according to the variable output response. This method can transform the difference of output response under different fault states into a low-dimensional fitting composition and then distinguish the different fault types. Under the condition of single fault of the circuit, the simulation result proves that the method presented in this paper enjoys high accuracy in effectively classifying the different fault types, locating the faulty components and observing the difference among variable faults intuitively in the low-dimensional coordinate system.

Soft Fault Feature Extraction in Nonlinear Analog Circuit Fault Diagnosis

Aiming at the problem to diagnose soft faults in nonlinear analog circuits, a novel approach to extract fault features is proposed. The approach is based on the Wigner---Ville distribution (WVD) of the subband Volterra model. First, the subband Volterra kernels of the circuit under test are cleared. Then, the subband Volterra kernels are used to obtain the WVD functions. The fault features are extracted from the WVD functions and taken as input data into the hidden Markov model (HMM). Finally, with classification of features using HMMs, the soft fault diagnosis of the nonlinear analog circuit is achieved. The simulations and experiments show that the method proposed in this paper can extract the fault features effectively and improve the fault diagnosis.

An Approach to Locate Parametric Faults in Nonlinear Analog Circuits

Aiming at the problem to locate parametric faults in nonlinear analog circuits, a new approach based on the subband decomposition combined with coherence functions is proposed. First, the Volterra series of the circuit under test decomposed by wavelet packets are used to detect the parametric faults. Then, the Volterra series in subbands are used to calculate the coherence functions. By comparison with the fault signatures, different states of the parametric faulty circuits are identified, and the faults are located. Simulations show the effectiveness of the method of the fault diagnosis in nonlinear circuits.

Diagnosis of Incipient Faults in Nonlinear Analog Circuits

Diagnosis of Incipient Faults in Nonlinear Analog Circuits 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.

Nonlinear Analog Circuit Fault Diagnosis Based on MF-DFA Method

Nonlinear Analog Circuit Fault Diagnosis Based on MF-DFA Method

Non-linear analog circuit fault diagnosis with large change sensitivity

Large change sensitivity has been proved efficient at, but restricted to, generating a linear circuit fault dictionary. This paper discusses the extension of large change sensitivity to non-linear analog circuit fault diagnosis. The fault dictionary is divided into d.c. and a.c. sections. In the d.c. domain, non-linear components are approximated with piecewise linear models. By relating the operating region of each piecewise linear model to the magnitude of a single fault in a procedure termed preconditioning, it is shown that large change sensitivity can efficiently compute the response of a faulty non-linear circuit. Results presented of an analysis of computational complexity show a significant reduction in the cost of simulating single linear resistor faults in a non-linear circuit using this method. In addition, after establishing that the resistive portion of the circuit is fault free, a fault dictionary is constructed for dynamic components using large change sensitivity in the small signal a.c. domain. Included with a discussion on the issues of large change sensitivity based simulation-before-test, a small non-linear circuit is used to demonstrate the effectiveness of the proposed fault diagnosis algorithm. Copyright © 2000 John Wiley & Sons, Ltd.