Fault coverage

About: Fault coverage is a research topic. Over the lifetime, 10153 publications have been published within this topic receiving 161933 citations. The topic is also known as: test coverage.

Fault diagnosis in designs with convolutional compactors

Abstract: The paper introduces a new non-adaptive fault diagnosis technique for scan-based designs. The proposed scheme guarantees accurate and time-efficient identification of failing scan cells based on results of a convolutional test response compaction.

A Study of Implication Based Pseudo Functional Testing

Abstract: In this paper, we present a study of the implication based functional constraint extraction techniques to generate pseudo-functional scan tests. Novel algorithms to extract pair-wise and multi-node constraints as Boolean expressions on arbitrary gates in the design are presented. We analyze its impact on reducing the overkill in testing, and report the trade-offs in coverage and scan-loads for a number of fault models. In the case of path-delay fault model, we show that the longest paths contribute most to the over-testing problem, raising the question about scan testing of the longest paths. Finally, our evaluation of the functional constraints on large industrial circuits show that the proposed constraint generation algorithm generate a powerful set of constraints most of which are not captured in the constraints extracted by designers for design-verification purposes.

Method for the automated generation of a fault tree structure

Abstract: A method for automated generation of an extended fault tree structure adapted to a production installation or a specific installation is used within a system determining the effectiveness and analyzing causes of faults. Generation takes place using a data processor and stored programs for carrying out functions of a hypothesis verifier, a fault data classifier, and a hypothesis configurer, and also based upon a prescribed general hierarchical fault tree structure produced by accessing data of a data server with the verifier, from which it derives fault events according to execution requirements of the verification script and stores these fault events, possibly together with previously entered fault events, in a fault database. At prescribable time intervals, the classifier carries out classification of the fault events by accessing the database, maps them as weighted causes of faults onto the tree structure, and displays or outputs the tree structure so extended.

System fault diagnostics using fault tree analysis

Abstract: Over the last 50 years, advances in technology have led to an increase in the complexity and sophistication of systems. More complex systems can be harder to maintain and the root cause of a fault more difficult to isolate. Downtime resultin from a system failure can be dangerous or expensive, depending on the type of system. In aircraft systems the ability to diagnose quickly the causes of a fault can have a significant impact on the time taken to rectify the problem and to return the aircraft to service. In chemical prcess plants the need to diagnose causes of a safety-critical failure in a system can be vital and a diagnosis may be required within minutes. Speed of fault isolation can save time, reduce costs, and increase company productivity and therefore profits. System fault diagnosis is the process of identifying the cause of a malfunction by observing its effect at various test points. Fault tree analysis (FTA) is a method that describes all possible causes of a specified system state in terms of the state of the components within the system. A system model is used to identify the states that the system should be in at any point in time. This paper presents a method for diagnosing faults in systems using FTA to explain the deviations from normal operation observed in sensor outputs. The causes of a system’s failure modes will be described in terms of the component states. This will be achieved with the use of coherent and non-coherent fault trees. A coherent fault tree is constructed from AND and OR logic and therefore considers only component-failed states. The non-coherent method expands this, allowing the use of NOT logic, which implies that the existence of component-failed states and component-working states are both taken into account. This paper illustrates the concepts of this method by applying the technique to a simplified water tank level control system.

Fault detection and diagnosis using multivariate statistical techniques : Process operations and control

Abstract: This paper describes a technique for on-line process fault detection and diagnosis based upon the statistical analysis of process data. A principal component model is developed from nominal operating data and is used to reduce the dimensionality of the measured process information. Current process performance is monitored through plots of the squared prediction error and the principal component scores. The fault signatures for various process malfunctions are located through multivariate statistical analysis of on-line monitored data. In order to identify fault directions in measurement space, a principal component analysis is carried out for each data set corresponding to different process malfunctions. Fault diagnosis is then achieved by comparing the direction of the current on-line measurements with those of a database of known trajectories of previously identified faults. The fault whose direction is most closely aligned with the current data direction is identified to be the most likely fault to have occurred and is taken as the diagnosis result. The technique is straightforward to implement and can be used to complement current fault diagnosis approaches. Application of the proposed technique to the on-line diagnosis of equipment malfunctions in a continuous stirred tank reactor (CSTR) system illustrates the power of the approach.