Stuck-at fault

About: Stuck-at fault is a research topic. Over the lifetime, 9707 publications have been published within this topic receiving 160254 citations.

Built-in Self Testing of Embedded Memories

Abstract: The authors present a built-in self-test (BIST) method for testing embedded memories. Two algorithms are proposed for self-testing of embedded bedded RAMs, both of which can detect a large variety of stuck-at and non-stuck-at faults. The hardware implementation of the methods requires a hardware test-pattern generator, which produces address, data, and read/write inputs. The output responses of the memory can be compressed by using a parallel input signature analyzer, or they can be compared with expected responses by an output comparator. The layout of memories has been considered in the design of additional BIST circuitry. The authors conclude by evaluating the two schemes on the basis of area overhead, performance degradation, fault coverage, test application time, and testing of self-test circuitry. The BIST overhead is very low and test time is quite short. Six devices, with one of the test schemes, have been manufactured and are in the field.

Probabilistic scheduling guarantees for fault-tolerant real-time systems

Abstract: Hard real time systems are usually required to provide an absolute guarantee that all tasks will always complete by their deadlines. We address fault tolerant hard real time systems, and introduce the notion of a probabilistic guarantee. Schedulability analysis is used together with sensitivity analysis to establish the maximum fault frequency that a system can tolerate. The fault model is then used to derive a probability (likelihood) that, during the lifetime of the system, faults will not arrive faster than this maximum rate. The framework presented is a general one that can accommodate transient 'software' faults, tolerated by recovery blocks or exception handling; or transient 'hardware' faults dealt with by state restoration and re-execution.

Machine Fault Signature Analysis

Abstract: The objective of this paper is to present recent developments in the field of machine fault signature analysis with particular regard to vibration analysis. The different types of faults that can be identified from the vibration signature analysis are, for example, gear fault, rolling contact bearing fault, journal bearing fault, flexible coupling faults, and electrical machine fault. It is not the intention of the authors to attempt to provide a detailed coverage of all the faults while detailed consideration is given to the subject of the rolling element bearing fault signature analysis.

Fault diagnosis in power plant using neural networks

Abstract: Fault diagnosis and identification (FDI) have been widely developed during recent years. Model-based methods, fault tree approaches and pattern recognition techniques are among the most common methodologies used in such tasks. Neural networks have been used in FDI problems for model approximation and pattern recognition as well. However, because of difficulties to perform Neural Network training on dynamic patterns, the second approach seems more adequate. In this paper, the FDI methodology consists of two stages. In the first stage, the fault is detected on the basis of residuals generated from a bank of Kalman filters, while, in the second stage, fault identification is obtained from pattern recognition techniques implemented by Neural Networks. The proposed fault diagnosis tool has been tested on a model of a power plant and results from simulations are reported and commented in the paper.