Fault Diagnosis Of Nonlinear Circuits Research Articles

Soft fault diagnosis of non‐linear circuits having multiple DC solutions

This study deals with a single soft fault diagnosis of non-linear circuits having multiple DC solutions. The key problem of the diagnosis is arranging a measurement test. Creation of the test for the above-defined class of circuits is discussed in detail. It is assumed that only input and output terminals are accessible in the circuit. The test is created based on some single-valued input or transfer characteristics traced in a modified circuit. The modification does not disturb the integrity of the circuit and is realised by connecting some components to the accessible terminals. A very fast method for tracing the characteristics is developed. Having appropriate characteristics several values of the exciting voltage are chosen and the corresponding values of voltage or current defined by the characteristic are measured in the course of testing. The method for arranging the measurement test is illustrated using four non-linear circuits having multiple DC solutions and three of them were built in bipolar and complementary metal-oxide-semiconductor (CMOS) technology. One of the circuits is comprehensibly diagnosed using a method for detecting, locating, and estimating values of different faulty parameters exploiting the proposed test.

A method for fault diagnosis of nonlinear circuits

PurposeThe purpose of this paper is to develop a method for multiple soft fault diagnosis of nonlinear circuits including fault detection, identification of faulty elements and estimation of their values in real circumstances.Design/methodology/approachThe method for fault diagnosis proposed here uses a measurement test leading to a system of nonlinear equations expressing the measured quantities in terms of the circuit parameters. Nonlinear functions, which appear in these equations are not given in explicit analytical form. The equations are solved using a homotopy concept. A key problem of the solvability of the equations is considered locally while tracing the solution path. Actual faults are selected on the basis of the observation that the probability of faults in fewer number of elements is greater than in a larger number of elements.FindingsThe results indicate that the method is an effective tool for testing nonlinear circuits including bipolar junction transistors and junction field effect transistors.Originality/valueThe homotopy method is generalized and associated with a restart procedure and a numerical algorithm for solving differential equations. Testable sets of elements are found using the singular value decomposition. The procedure for selecting faulty elements, based on the minimal fault number rule, is developed. The method comprises both theoretical and practical aspects of fault diagnosis.

Nonlinear Analog Circuit Fault Diagnosis Based on MFDFA Method

This paper presents a fault diagnosis method for nonlinear analog circuit based on multifractal detrended fluctuation analysis (MFDFA) method. The MFDFA method is applied to analysis fault signal and extracts the multifractal features from the raw signal. The selected features are given to SVM classifier for further classification. The data required to develop the classifier are generated by simulating various faults using Pspice software. The simulation results show that the proposed method provides a robust and accurate method for nonlinear circuit 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.

Particle swarm optimization based RVM classifier for non-linear circuit fault diagnosis

A relevance vector machine (RVM) based fault diagnosis method was presented for non-linear circuits. In order to simplify RVM classifier, parameters selection based on particle swarm optimization (PSO) and preprocessing technique based on the kurtosis and entropy of signals were used. Firstly, sinusoidal inputs with different frequencies were applied to the circuit under test (CUT). Then, the resulting frequency responses were sampled to generate features. The frequency response was sampled to compute its kurtosis and entropy, which can show the information capacity of signal. By analyzing the output signals, the proposed method can detect and identify faulty components in circuits. The results indicate that the fault classes can be classified correctly for at least 99% of the test data in example circuit. And the proposed method can diagnose hard and soft faults.

An algorithm for multiple fault diagnosis in analogue circuits

The paper deals with multiple fault diagnosis of analogue AC or DC circuits with limited accessible terminals for excitation and measurement and brings an algorithm for identificating faulty elements and evaluating their parameters. The main achievement is a method enabling us to efficiently identify faulty elements. For this purpose some testing equations are derived playing a key role in identification of possibly faulty elements which are next verified using a test of acceptance. The proposed approach is described in detail for double fault diagnosis. Also extension to triple fault diagnosis is given. Although the method pertains to linear circuits, some aspects of multiple fault diagnosis of non-linear circuits can be also performed using the small signal approach. Two numerical examples illustrate the proposed method and show its efficiency. Copyright © 2006 John Wiley & Sons, Ltd.

Neural network-based L1-norm optimisation approach for fault diagnosis of nonlinear circuits with tolerance

The paper deals with fault isolation in nonlinear analogue circuits with tolerance under an insufficient number of independent voltage measurements. The L1-norm optimisation problem for different scenarios of nonlinear fault diagnosis is formulated with a new fast method being presented. How to solve the L1-norm optimisation problem is discussed and a new neural network-based approach for solving the nonlinear constrained L1-norm optimisation problem is proposed and utilised in locating the most likely faulty elements in nonlinear circuits. The validity of the proposed method is verified and simulation examples are presented.

Symbolic simulators for the fault diagnosis of nonlinear analog circuits

A new symbolic technique for the realization of simulators for nonlinear analog circuits is presented. The generated simulators work with input/output in numerical form, but they are very efficient due to the use of the symbolic approach. For nonlinear components a PWL (PieceWise Linearization) method is used. The proposed approach permits to obtain libraries of simulators which can be very useful in many application fields. In particular we present a possible application to an expert system devoted to nonlinear circuit fault diagnosis. The program package, realized for this application, is described from both algorithmic and functional points of view. Some simple examples are presented in order to illustrate the main features of the program.

NONLINEAR FAULT DIAGNOSIS——A NEW THEORY AND IMPLEMENTATION

A new theory of the fault diagnosis of nonlinear circuits and systems, the Volterra func-tional series theory has been proposed, and the recursive approach of fault equations of thenth order and the decision algorithm of large-scale nonlinear systems have been developed.They are SAT approaches in the frequency domain, applicable to any time-invariant nonli-near system with dynamics of any order, and to both linear and nonlinear systems with mul-tiple hard and/or soft faults. They can also be used to isolate failures up to arbitrary speci-fied subsystem. The theory is not only completely analytical, but also symbolic. Hence,it is quite general.