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.

Combined Fault Detector and Faulted Phase Selector for Transmission Lines Based on Adaptive Cumulative Sum Method

Abstract: In this paper, a novel approach is proposed for fault detection in transmission lines. This idea is based on the adaptive cumulative sum method (ACUSUM), whose structure is adaptive with the current passing through the corresponding line. The proposed ACUSUM algorithm can detect even low magnitude faults with high resistances. By using the proposed method, just a few milliseconds after fault inception, the fault detection unit permits the main protection algorithm to become activated. Moreover, ACUSUM output indices can discriminate faulted phases within only 1 ms after fault registration. This new faulted-phase selector can also be applied to double-circuit transmission lines to detect cross-faults as well as intercircuit faults. The results have shown that the proposed method has good performance in speed and accuracy as a combined fault detector and faulted-phase selector algorithm.

Conformance Testing of Temporal Role-Based Access Control Systems

Abstract: We propose an approach for conformance testing of implementations required to enforce access control policies specified using the Temporal Role-Based Access Control (TRBAC) model. The proposed approach uses Timed Input-Output Automata (TIOA) to model the behavior specified by a TRBAC policy. The TIOA model is transformed to a deterministic se-FSA model that captures any temporal constraint by using two special events Set and Exp. The modified W-method and integer-programming-based approach are used to construct a conformance test suite from the transformed model. The conformance test suite so generated provides complete fault coverage with respect to the proposed fault model for TRBAC specifications.

Hidden Markov model-based process monitoring system

Abstract: Sensor signals produced in industrial manufacturing processes contain valuable information about the condition of operations. However, extracting the appropriate feature for effective fault diagnosis is difficult. Moreover, the adaptability and flexibility of current fault diagnosis systems are often found wanting in real-world applications. Unfortunately, it is essential to rebuild most fault diagnosis systems when new fault types emerge. This paper presents an intelligent fault diagnosis system based on a hidden Markov model. Introducing the concepts of time marginal energy and frequency marginal energy, the features of which can be acquired by the wavelet packet technique satisfy the requirements for fault diagnosis. By utilizing the best tree principle, this method not only extracts the feature automatically without a priori experience but also compresses the data; both of which ensure a system that is practical for real-time application. The new diagnosis system developed here is efficient and effective, as demonstrated by the model developed and applied to a real-time sheet metal stamping process. Based on tests conducted during two experiments (one based on simple blanking, the other on progressive operations) and related comparisons, the proposed method is substantially more effective than other approaches. In addition, the new method requires that only related models be created for new fault types, which results show are ideal for shop floor applications.

A Bee Colony Optimization Algorithm for Fault Coverage Based Regression Test Suite Prioritization

Abstract: The process of verifying the modified software in the maintenance phase is called Regression Testing. The size of the regression test suite and its selection process is a complex task for regression testers because of time and budget constraints. In this research paper, the Bee Colony Optimization (BCO) algorithm for the fault coverage regression test suite prioritization has been presented. In the natural bee colony, there are of two types of worker bees; Scout bees and forager bee, who are responsible for the development and maintenance of the colony. The BCO algorithm developed for the fault coverage regression test suite is based on the behavior of these two bees. The BCO algorithm has been formulated for fault coverage to attain maximum fault coverage in minimal units of execution time of each test case, using two examples whose results are comparable to optimal solution. Average Percentage of Fault Detection (APFD) metrics and charts has been used to show the effectiveness of proposed algorithm.

Composite Fault Diagnosis in Wireless Sensor Networks Using Neural Networks

Abstract: Wireless sensor networks (WSNs) are spatially distributed devices to support various applications. The undesirable behavior of the sensor node affects the computational efficiency and quality of service. Fault detection, identification, and isolation in WSNs will increase assurance of quality, reliability, and safety. In this paper, a novel neural network based fault diagnosis algorithm is proposed for WSNs to handle the composite fault environment. Composite fault includes hard, soft, intermittent, and transient faults. The proposed fault diagnosis protocol is based on gradient descent and evolutionary approach. It detects, diagnose, and isolate the faulty nodes in the network. The proposed protocol works in four phases such as clustering phase, communication phase, fault detection and classification phase, and isolation phase. Simulation results show that the proposed protocol performs better than the existing protocols in terms of detection accuracy, false alarm rate, false positive rate, and detection latency.