This paper deals with the application of artificial neural networks (ANNs) to fault detection and location in extra high voltage (EHV) transmission lines for high speed protection using terminal line data. The proposed neural fault detector and locator were trained using various sets of data available from a selected power network model and simulating different fault scenarios (fault types, fau...

In this paper, the fault detection problem is investigated for a class of networked multi-rate systems (NMSs) with network-induced fading channels and randomly occurring faults. The stochastic characteristics of the fading measurements are governed by mutually independent random channel coefficients over the known interval [0, 1]. By applying the lifting technique, the system model for the obse...

This paper aims at the blade root moment sensor fault detection and isolation issue. The underlying problem is crucial to the successful application of the individual pitch control system which plays a key role for reducing the blade loads of large offshore wind turbines. In this paper, a wind turbine model is built based on the closed loop identification technique, where the wind dynamics is i...

Monitoring the quality of service in a multi-domain network allows providers to ensure the control of multi-domain service performance. A multi-domain service is a service that crosses multiple domains. In this paper, we propose several mechanisms for fault detection and fault localization. A fault is detected when an end-to-end contract is not respected. Faulty domains are domains that do not ...

Most conventional software-based approaches for fault tolerant Ethernet (FTE) adopt a heartbeat mechanism for fault detection. However, due to the tradeoff between the number of nodes and the fault detection time, these conventional approaches do not provide scalability for large-scale networks. To solve this scalability issue, we previously proposed the SAFE scheme, which utilizes multiple sub...

In robotics, exogenous fault detection is the process through which one robot detects faults that occur in other, physically separate robots. In this paper, we study exogenous fault detection in a collective leader-follower task for autonomous robots. We record sensory inputs from the robots while they are operating normally and after simulated faults have been injected. Given that faults are s...

In the building energy efficiency field, developing automatic and accurate fault detection and diagnosis methods is necessary in order to ensure optimal operations of systems and to save energy. In this paper first, fault detection analysis based on statistical methods where anomalies are detected through a comparison with neighborhood and averaged fault-free values and through a clustering tec...

The task of diagnosis is to find an explanation for a set of observations and – in the case of prognosis – to forecast the course of events. Diagnosis can be broken down into anomaly detection and failure identification, depending on the desired granularity of information required. Prognosis is concerned with incipient failure detection, margin prediction, or overall performance prediction. The...

In this paper, we propose a software reliability model that considers not only error generation but also fault removal efficiency combined with testing coverage information based on a nonhomogeneous Poisson process (NHPP). During the past four decades, many software reliability growth models (SRGMs) based on NHPP have been proposed to estimate the software reliability measures, most of which ha...

This paper studies the robust fault detection problem using the standard H∞ filtering formulation. With this formulation, the minimization of the disturbance effect on the residual is formulated as a standard H∞ filtering problem and the design is solved using standard H∞ techniques. To facilitate the enhancement of the residual sensitivity to the fault, the difference between the residual and ...