Search

In this paper a new method to find the parametric faults in weakly nonlinear analog circuit is proposed. Frequency domain volterra kernels are derived to calculate the test parameters i.e., nonlinear distortions and the cross spectral density. Volterra kernels are sensitive to device parameter and the components used to build the circuit. Device parameter and each component value are varied within the tolerance limit using Monte Carlo simulation. For the fault free circuit maximum and minimum values of the 3rd order harmonic, intermodulation distortion and the cross spectral density are measured. During testing, single and multiple faults are injected and the test parameters are measured. If any one or more test parameters are outside the predetermined bounds the circuit is declared to be faulty. This proposed method offers high fault coverage capability. To validate this method CMOS differential amplifier circuit is taken under consideration. [ABSTRACT FROM AUTHOR]

Oscillation-based test algorithm has been proposed and verified experimentally as an alternative to the specification--based test of analog circuits. Active filters are transformed to oscillators using nonlinear feedback, realized with a Schmitt trigger. Faults are detected based on the deviation of the oscillation frequency outside the tolerance band due to the variation of different circuit components under test. This proposed technique ensures high test precision due to the processing of the oscillation frequency with the help of a purely digital circuit. Undetectable ranges of parametric faults of circuit components have been identified by simulation with Cadence virtuoso using the 0.18 µm CMOS technology. Then, practical circuits of second order Butterworth Low Pass Filter and Sallen-Key Band Pass Filter have been tested experimentally. Experimental results ensure high fault coverage of the proposed test strategy. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Input test signal plays important role in testing of analog circuits. Single type of input stimulus cannot maximally reveal the state of the circuit. To combat this shortcoming, this work proposes to integrate information from the output responses corresponding to different input stimuli and to use the combined information to improve the accuracy of fault diagnosis in analog circuits. The circuit under test is excited with different input signals and wavelet features are extracted from the output responses of the circuit. Ultimate fault features have been defined by applying data fusion algorithm to the sets of wavelet features obtained from individual output. Data fusion has been performed in two steps, data whitening and Principal component analysis (PCA). Fused features are used to train SVM (Support Vector Machine) classifier for fault diagnosis. The proposed approach is validated with three types of filter circuits, i.e. Sallen-Key band pass filter, four OPAMP high pass filter and elliptic low pass filter. The average accuracy of the proposed fault classification method has been found greater than 99.8% for all the test circuits. The proposed technique offers improved classification accuracy, lower computational burden and lesser implementation complexity. • This work proposes to integrate information from the output responses corresponding to different input stimuli to improve the accuracy of fault diagnosis. • Wavelet energy vectors have been used as pre-processor. • Fault features have been constructed by applying multi-source data fusion algorithm. • Data fusion has been performed in two steps, data whitening and Principal component analysis (PCA). • The fused features are used to train SVM (Support Vector Machine) classifier for fault diagnosis. • The proposed technique offers improved classification accuracy, lower computational burden and lesser implementation complexity. [ABSTRACT FROM AUTHOR]

This paper analyzes the simultaneous estimation of states and parametric faults of a chaotic Rössler system in the presence of process and measurement noise. The parametric fault causes a bifurcation in the Rössler system. The problem of state and parametric fault estimation for nonlinear systems can be solved through the extended Kitanidis Kalman filter (EKKF). In fact, the EKKF makes a perfect estimate merely for states in the presence of unknown parameters for nonlinear systems. In this paper, an EKKF is developed to estimate states. Since the EKKF-generated innovation is a colored Gaussian stochastic process, its covariance kernel is also computed through a recursive approach. However, the parametric fault is estimated through the maximum likelihood (ML) framework based on the moving window in the EKKF-generated innovation. In the simulation section, the parametric fault occurs and causes the system to show chaotic behavior, which is described through the bifurcation diagram. According to the simulation results, the proposed approach succeeded in estimating states and parametric faults. Moreover, a low parametric fault can also cause a bifurcation in the system. [ABSTRACT FROM AUTHOR]

This work presents a testing technique based on 'Kolmogorov–Smirnov' (K–S) test for detection of parametric faults in analog circuits. The proposed method is a time-domain signal processing technique that compares the statistical similarity in terms of 'Empirical Cumulative Distribution Function' (ECDF) of the outputs of the circuit when the input of the circuit is a random analog signal. 'Multivariate Adaptive Regression Splines' (MARS) technique is used to map the tolerances of functional metrics to the components of the circuit under test (CUT). Two benchmark circuits, i.e., second-order Sallen–Key band-pass filter and weakly nonlinear cascade amplifier are tested to validate the proposed fault detection technique. The proposed statistical approach with the use of random analog signal as input excitation results reduction of complexity for designing input test signal and increases fault coverage in the analog circuit testing. The proposed method is testified experimentally for Sallen–Key band-pass filter. The experimental results are in good agreement with the simulated results. [ABSTRACT FROM AUTHOR]

This paper presents a method for the detection of parametric faults in linear filters with the help of impulse response which is studied on the basis of cross-correlation, a statistical metric. Impulse input is generated with delay flip flops and R–C circuit with minimum circuit complexity. Cross-correlation of impulse responses of the faulty and non-faulty circuits is fitted with Gaussian function. Component tolerances are mapped to statistical metric spaces in terms of Gaussian fitting parameters by Monte Carlo simulation. The proposed method is validated with simulated (using UMC-180 nm technology in CADENCE Virtuoso platform) as well as experimental results. Two benchmark analog filter circuits, second-order Sallen–Key band-pass filter and fourth-order Chebyshev low-pass filter, are considered as test circuits. The present method requires minimum circuit complexity and computational effort. The proposed fault detection technique is applicable for any linear analog circuit. [ABSTRACT FROM AUTHOR]

Degradation of electronic components is typically accompanied by a deviation in their electrical parameters from their initial values, which can ultimately lead to parametric faults in electronic circuits. Existing approaches to predict parametric faults emphasize identifying monotonically deviating parameters and modeling their progression over time. However, in practical applications, where the components are integrated into a complex electronic circuit assembly, product, or system, it is generally not feasible to monitor component-level parameters. To address this problem, a circuit health estimation method was developed using a kernel-based machine-learning technique. This method exploits features that are extracted from responses of circuit-comprising components exhibiting parametric faults, instead of the component-level parameters. The method was evaluated using data from simulation experiments on a benchmark Sallen–Key filter circuit and a dc–dc converter system. [ABSTRACT FROM PUBLISHER]

This paper presents a fault detection algorithm to detect parametric fault in linear and weakly non-linear analog circuits by Bhattacharyya measure, a statistical metric. Linear feedback shift register (LFSR) generated pseudo-random bit sequences are fed to digital-to-analog converter (DAC) to obtain random analog input stimuli for the circuit under test (CUT). Bhattacharyya coefficient is measured from the probability density function (PDF) of the output. The non-Gaussian auto-regressive model is used to estimate the PDF. Component tolerance is mapped into statistical space by Monte Carlo simulation. The proposed methodology is validated through three benchmark circuits: continuous-time low pass state variable filter circuit, fourth order low pass Chebyshev filter circuit and cascade amplifier. All the circuits are simulated with CADENCE Virtuoso using UMC-180nm technology. Defect screening is also measured with linear regression analysis. Detectability of the proposed method for parametric fault is reasonably large in comparison to functional test method. [ABSTRACT FROM AUTHOR]

Due to the wide range of applications of electronic circuits in the recent years, the fault diagnosis in electronic circuits is a foremost problem. The main purpose of the fault diagnosis technique is isolating the faults present in the electronic circuits and also, detecting the fault which affects the safety and performance of the system. For various real-time applications of fault diagnosis, literature presents several techniques for detecting the faults in electronic circuits. In this paper, reviews on the research based on the fault diagnosis techniques which are all gained much attention are comprehended. Accordingly, 114 research papers related to the fault diagnosis are reviewed and analyzed based on the various objectives. In this review, we present the taxonomy of the fault detection in analog circuits and discuss the state of the art algorithms with various advantages and major drawbacks. The comprehensive analysis is carried out on finding the coverage of the publishers, faults, circuits, methods, simulation tools, and metrics. This critical review finally discusses the research challenges that are still available in the existing techniques and the way to extend the current research is also examined. [ABSTRACT FROM AUTHOR]

This work addresses the problem of detecting parametric faults in nonlinear dynamic systems by extending an eigenstructure based technique to a nonlinear context. Two local state-space models are updated online based on a recursive subspace system identification technique. One of the models relies on input–output real-time data collected from the plant, while the other is updated using data generated by a neural network predictor, describing the nonlinear plant behaviour in fault-free conditions. Parametric faults symptoms are generated based on eigenvalues residuals associated with two linear state-space model approximators. The feasibility and effectiveness of the proposed framework are demonstrated through two case studies. [ABSTRACT FROM AUTHOR]

Copyright of Automatika: Journal for Control, Measurement, Electronics, Computing & Communications is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Problems related to the design of observer-based parametric fault detection (PFD) systems are studied. The core of our study is to first describe the faults occurring in system actuators, sensors and components in the form of additive parameter deviations,then to transform the PFD problems into a similar additive fault setup, based on which an optimal observer-based optimization fault detection approach is proposed. A constructive solution optimal in the sense of minimizing a certain performance index is developed. The main results consist of defining parametric fault detectability, formulating a PFD optimization problem and its solution. A numerical example to demonstrate the effectiveness of the proposed approach is provided. [ABSTRACT FROM AUTHOR]