Xuan Xie

Soft Fault Diagnosis of Analog Circuits via Frequency Response Function Measurements

This paper provides a novel method for single and multiple soft fault diagnosis of analog circuits. The method is able to locate the faulty elements and evaluate their parameters. It employs the information contained in the frequency response function (FRF) measurements and focuses on finding models of the circuit under test (CUT) as exact as possible. Consequently, the method is capable of getting different sets of the parameters which are consistent with the diagnostic test, rather than only one specific set. To fulfil this purpose, the local plolynomial approach is applied and the associated normalized FRF is developed.The proposed method is especially suitable at the pre-production stage, where corrections of the technological design are important and the diagnostic time is not crucial. Two experimental examples are presented to clarify the proposed method and prove its efficiency.

Analog Circuits Soft Fault Diagnosis Using Rényi's Entropy

An analog circuit soft fault diagnosis method using Rényi’s entropy is proposed. This method focuses on extracting the entropy information contained in the probability density function (PDF) of the output of the circuit under test (CUT), which is sensitive to the parameters of circuits. Firstly, using the Lagrange multiplier method with Rényi’s entropy deduces PDF. Then the parameter α of Rényi’s entropy is estimated adaptively by employing the output of CUT through the maximum likelihood estimation method. Finally, the value of Rényi’s entropy can be calculated using the PDF and α parameter. The divergence between the Rényi’s entropy corresponding to the fault and fault free circuits is adopted to detect the fault. The method can detect soft fautls, including the single fault and multiple faults, without complicated models and mass of data, and also without interrupting the inherent connections. We conduct experiments respectively on two circuits that are implemented on an actual circuit board. The effectiveness of the proposed method is demonstrated by the result of the experiment.

Methodology and Equipments for Analog Circuit Parametric Faults Diagnosis Based on Matrix Eigenvalues

A method and corresponding equipments for analog circuit parametric faults diagnosis based on matrix eigenvalues are presented in this paper. The proposed method organizes the discrete samples of response signals of the circuits under test (CUT) into a square matrix and calculates out the maximal and minimal eigenvalues of the square matrix. According to the one-to-one correspondence relationship between the matrix elements and fault cases, fault detection and fault location are achieved by using the maximal and minimal eigenvalues as fault signatures. Two experimental results show that the proposed method has better fault coverage, higher computational efficiency and needs fewer test points than the other state-of-the-art methods. Without the necessary of node-voltage equation or internal structure analysis, solely, depending on the analysis of the output response of CUT to achieve the fault diagnosis, the presented method is particularly suitable for the analog integrated circuit fault diagnosis, and can be extended to solve the fault diagnosis for superconductor digital circuits with finite accessible nodes.

A Splitting Bregman-Based Compressed Sensing Approach for Radial UTE MRI

A splitting Bregman-based compressed-sensing (CS) approach (CS-SplitBerg), using the nonuniform fast Fourier transform, is proposed to reconstruct radial magnetic resonance (MR) images from undersampled k- space measurements. Using the splitting Bregman framework, the proposed CS-SplitBerg approach takes fully into account the measurement noise and exploits the combined sparsity of MR images, i.e., ℓ1- norm and total variation regularization. With convergence guaranteed, the CS-SplitBerg approach uses the Bregman update process and some auxiliary variables to relax the constrained optimization problem to a sequence of easily solved unconstrained minimization problems. Experimental results using both a phantom example and a mouse cardiac example demonstrate that, under different undersampling rates, the CS-SplitBerg approach performs better than the CS-CG approach, which was introduced in our previous work. With an affordable computational cost by considering the influences of noise, the CS-SplitBerg approach can further reduce the necessary number of MR imaging (MRI) measurements for the recovery and better differentiate true MRI images from noisy data.

Tsallis Entropy Based q -Gaussian Density Model and Its Application in Measurement Accuracy Improvement

The central limit theorem guarantees the distribution of the measurand is Gaussian when the number of repeated measurement is infinity, but in many practical cases, the number of measurement times is limited to a given number. To overcome this contradiction, this paper firstly carries out the maximum likelihood estimation for parameter q in q-Gaussian density model developed under the maximum Tsallis entropy principle. Then the q-Gaussian probability density function is used in the particle filter to estimate and measure the nonlinear system. The estimated parameter q is related to the ratio between the measurement variance and the given variance, which indicates that the measurement accuracy cannot be improved if we only increase the repeated measurement times. Via using the proposed q-Gaussian density model, the measurement error (the average mean square error) of the estimation results can be reduced to a considerable level where the number of repeated measurement is limited. The experimental example is given to verify the proposed model and the measurement results prove the correctness and effectiveness of it.

Measurement Uncertainty Estimation for Electromagnetism Devices and Equipment Using Extreme Fisher Information

In electromagnetism devices and equipment measurement activities, it is hard to identify and compensate every effect in measurement and evaluate the incompleteness of the measurement results accurately and efficiently. In this paper, we employ the probability density functions (PDFs) derived from the extreme Fisher information (EFI) method in the view of information theory for estimating the measurement uncertainty of the measurement result. By considering the physical essence of EFI, a differential equation model is established for the measurement practice and the explicit form of the solution PDF corresponding to EFI is given, which guarantees the dynamics and the practicability of the estimation of the measurement uncertainty (MU). The obtained the PDF can describe the combined performance of the system and all the measurement effects, then, with the PDF and the confidence, MU can be estimated more dynamically and efficiently. The effectiveness of the proposed EFI method is demonstrated by the numerical results of two practical instances.

A comparative study of compressed sensing approaches with splitting Bregman framework for radial UTE MRI

This paper investigates two compressed sensing (CS) approaches that can be used to reconstruct radial Magnetic Resonance (MR) images with undersampled k-space measurements. Combining CS with gridding and non-uniform fast Fourier transform (NUFFT), yields two different approaches: Regridding-CS and NUFFT-CS. Under splitting Bregman framework, these approaches can decrease the load of data acquisition while recovering MR images through l1-norm and total variation (TV) optimization. Experiments using a phantom example have verified that the NUFFT-CS achieves better image quality than the Regridding-CS.

Estimation of measurement uncertainty for devices with extreme fisher information

In practical devices or materials measurement, such as electromagnetic devices measurement, the systematic effects cannot be corrected and quantified one by one, or the identification of systematic effects is too expensive. So, this paper proposes a method in an information view to directly estimate the measurement uncertainty interval. In the proposed method, the extreme Fisher information (EFI) is adopted based on readily provided prior information (such as the specified first-order moment) to estimate the boundary information of measurement that is the essence of the measurement uncertainty interval. Experiments are conducted. The numerical results verified the effectiveness of the proposed EFI method. However, limited by the space, only one experiment with numerical results is shown here.

Rényi's Entropy Based Method for Analog Circuits Soft Fault Detection

We propose a Rényis entropy based analog circuit soft fault detection method. This method extracts the entropy information from the probability density function (PDF) of the output of the circuit under test (CUT), which is sensitive to the parameters of circuits. In this method, firstly, the Lagrange multiplier method with Rényis entropy is used to deduce PDF of the output signal. Then through the maximum likelihood estimation method, we estimate the parameter α of Rényis entropy adaptively according to the output of CUT. Finally, the value of Rényis entropy can be calculated using the PDF and α parameter. The divergence between the Rényis entropy corresponding to the fault and fault free circuits is adopted to detect the fault. This method can 100% detect soft faults, including the single fault and multiple faults, without complicate models and mass of data, and also with no need of interrupting the inherent contentions of CUT. Experiments are conducted respectively on two circuits that are implemented on an actual circuit board. The effectiveness of the proposed method is demonstrated by the result of the experiment.