In this paper, various fault location techniques in analog networks are described and compared. The emphasis is on the more recent developments in the subject. Four main approaches for fault location are addressed, examined, and illustrated using simple network examples. In particular, we consider the fault dictionary approach, the parameter identification approach, the fault verification approach, and the approximation approach. Theory and algorithms that are associated with these approaches are reviewed and problems of their practical application are identified. Associated with the fault dictionary approach we consider fault dictionary construction techniques, methods of optimum measurement selection, different fault isolation criteria, and efficient fault simulation techniques. Parameter identification techniques that either utilize linear or nonlinear systems of equations to identify all network elements are examined very thoroughly. Under fault verification techniques we discuss node-fault diagnosis, branch-fault diagnosis, subnetwork testability conditions as well as combinatorial techniques, the failure bound technique, and the network decomposition technique. For the approximation approach we consider probabilistic methods and optimization-based methods. The artificial intelligence technique and the different measures of testability are also considered. The main features of the techniques considered are summarized in a comparative table. An extensive, but not exhaustive, bibliography is provided.

Fault diagnosis of analog circuits

Traditionally, work on analog testing has focused on diagnosing faults in board designs. Recently, with increasing levels of integration, not just diagnosing faults, but distinguishing between faulty and good circuits has become a problem. Analog blocks embedded in digital systems may not easily be separately testable. Consequently, many papers have been recently written proposing techniques to reduce the burden of testing analog and mined-signal circuits. This survey attempts to outline some of this recent work, ranging from tools for simulation-based test set development and optimization to built-in self-test (BIST) circuitry.

A tutorial introduction to research on analog and mixed-signal circuit testing

Introduction to Higher Algebra. By Maxime Bôcher. Pp. xi, 321. New York: The Macmillan Company. 1907.

A DFT (design-for-test) methodology to improve the controllability/observability of internal signals in active filters is presented. The normal filter design becomes an 'analog scan' structure in the test mode. The fundamental theory underlying the proposed methodology is presented, followed by an algorithmic description and the results of several case studies. These results are considered from the perspectives of the overhead incurred and the extension of the technique to other analog structures. The DFT algorithm includes not only the modification technique but also a test generation scheme. The algorithm is suitable for silicon compiler and CAD (computer-aided-design) implementation. Experimental results confirm the validity of the methodology. >

A design-for-test methodology for active analog filters

1. A Circuit Theoretic Approach to Analog Fault Diagnosis.- Background.- An Introduction to Analog Fault Diagnosis.- Important Issues of Analog Fault Diagnosis.- The Element-Value Solvability Problem.- A Fault/Tolerance Compensation Model.- k-Fault Diagnosis: The Ideal Case.- k-Fault Diagnosis: The Tolerance Case.- Illustrative Examples.- 2. Linear Method vs. Nonlinear Method.- Setting of the Problem.- Probability.- Probability Measure.- Perturbation of Parameters.- Geometry of SF and TF.- Linear Method Is as Powerful as the Nonlinear Method.- 3. Topological Testability Conditions for Analog Fault Diagnosis.- Theory.- Applications.- 4. Fault Diagnosis of Nonlinear Electronic Circuits.- Fault Diagnosis of Linear Circuits.- Fault Diagnosis of PWL Circuits.- Examples.- 5. Analog Multifrequency Fault Diagnosis with the Assumption of Limited Failures.- The CCM and the Fault Diagnosis Equations of [4].- A Motivational Example.- Diagnosability for nf Faults.- Limited Fault Algorithm.- Limited Fault Algorithm Examples.- 6. A Searching Approach Self-Testing Algorithm for Analog Fault Diagnosis.- Automatic Test Program Generation.- Decision Algorithms.- The Heuristic Algorithm.- Parallel Processing for Analog Fault Diagnosis.- Design for Testability.- 7. An Artificial Intelligence Approach to Analog Systems Diagnosis.- Qualitative Causal Models.- The Treatment of Fault Probabilities.- Best Test Strategies.- FIS: An Implemented Diagnostic System.- Current Applications of FIS.- 8. Automatic Testing for Control Systems Conformance.- Historical Development of ATE.- Transfer Function Testing.- Return Signal Processing.- Tuning of Large Electro-Mechanical Servosystems.- The SUT Test Signature.- Transfer Function Models of Control Systems.- The "Fuzzy" Nature of Control System Behavior.- Checkout Based on Quadratic Performance Criteria.- "Closed Loop" Testing.- 9. Testing of Analog Integrated Circuits.- Testing vs. Diagnosis.- Digital vs. Analog Testing.- Specification-Based Testing.- Solution of the Test Tolerance Assignment Problem.- Consideration for Fault-Model-Based Testing.- An Approach to Fault-Model-Based Testing.- 10. A Unified Theory on Test Generation for Analog/Digital Systems.- Notation and Basic Concepts.- Testability of Interconnected Systems.- Reachability, Observability, and Testability.- Test Generation for Interconnected Systems: Case Studies.

Testing and Diagnosis of Analog Circuits and Systems

A concept of k -node-fault testability is introduced. A sufficient and almost necessary condition for testability as well as the test procedure is presented. This condition is further evolved to a necessary and almost sufficient topological condition for testability. A unique feature of this condition is that it depends only on the graph of the circuit, not on the element values. Based on this condition, a design of testability can be established.

Node-fault diagnosis and a design of testability

The necessity for a coprime fractional representation of a plant, in order to completely parametrize all stabilizing compensators by the Youla theory, is examined. It is shown that the coprimeness is necessary for systems in a factorial ring, a class inclusive of multidimensional scalar systems. Some observations on partial-state transfer functions in the feedback configuration are found to be useful in showing the necessity.

A necessary and sufficient condition for feedback stabilization in a factorial ring

A concept of k-node-fault testability is introduced. A sufficient and almost necessary condition for testability as well as the test procedure is presented. This condition requires little computation with a few test points in view of the complexity of the problem of multifault diagnosis of large-scale circuits. This condition is further evolved to a necessary and almost sufficient condition for testability. A unique feature of this condition is that it depends only on the graph of the circuit, not on the element values. Based on this condition, a design of testability is established.

The well-posedness, the complete solution, and the coprimeness of the feedback systems are presented in an algebraic setting. It is an extension of an earlier paper by Desoer et al. [1]. In particular, the condition on coprimeness and the existence of fractional representation in H are removed. In addition, it also resolved the case for non-linear operators.

On well-posed feedback systems - In an algebraic setting

This paper gives a summary on the research on the fault diagnosis of a large-scale analog circuit carried out by the authors. The key to its success is a property shared by incremental functions, called the generalized linear function.

R. Liu

Papers

Node-fault diagnosis and a design of testability

A necessary and sufficient condition for feedback stabilization in a factorial ring

Node-fault diagnosis and a design of testability

On well-posed feedback systems - In an algebraic setting

Generalized linear functions: Global sensitivity, diagnosis and nonlinear estimation