When a fault occurs in a system, the main problem to be addressed is to raise an alarm, ideally diagnose what fault has occurred, and then decide how to deal with it. The problem of detecting a fault, finding the source/location and then taking appropriate action is the basis of fault tolerant control. In this chapter, an introduction to fault tolerant control (FTC) and fault detection and isol...

The premise of model-based diagnosis is that the system for diagnosing is diagnosable. Previous methods to diagnose fault are mainly based on the assumption of abrupt change, and under this kind of assumption, unpredictable fault can be caused by those possible slowly changing fault or that the system cannot be diagnosed. Diagnosability of slowly changing fault is discussed in this essay, and t...

As the fault information of power transformers has characteristics such as complementarities, redundancy and uncertainty, the diagnosis task can’t be finished by the simple fault characteristic vector and the diagnosis method. The basic ideas of information fusion are introduced, and DGA (the Dissolved Gas Analysis) is combined tightly with other information such as the results of conventional ...

In virtue of the sharp increasing scale of distribution network and remarkable improvement of network complexity of power system, it is more challenging to monitor the actual running situation of the power network and to diagnose fault. To add insult to injury, man-made operation mistakes and inadequate situational awareness push the power system into a tight corner. This research presents a ne...

Developing of fault tolerant control systems (FTCS’s) for permanent magnet Brushless DC (BLDC) motor drives to diagnose and handle various faults have been of the research concentrations in the last decade. In this paper a novel fault diagnosis algorithm for position sensors (Hall Effect sensors) failure of the BLDC motor is presented. Fault detection and identification of Hall Effect sensors a...

Fault detection and isolation (FDI) in dynamic data from an automotive engine air path using artificial neural networks is investigated. A generic SI mean value engine model is used for experimentation. Several faults are considered, including leakage, EGR valve and sensor faults, with different fault intensities. RBF neural networks are trained to detect and diagnose the faults, and also to in...

With the increasing complexity of VLSI devices, more complex faults have appeared. Most of previous fault diagnosis methods considered a single defect assumption. However, for present technologies and chip sizes, defects have tendency to be clustered. So, we propose a multiple fault diagnosis method using a fault selection table. The proposed method can diagnose multiple defects based on a sing...

Traditional network diagnosis methods of Client-Terminal Device (CTD) problems tend to be laborintensive, time consuming, and contribute to increased customer dissatisfaction. In this paper, we propose an automated solution for rapidly diagnose the root causes of network performance issues in CTD. Based on a new intelligent inference technique, we create the Intelligent Automated Client Diagnos...

The method of improved wavelet transform neural network based on hybrid GA(genetic algorithm) is presented to diagnose rolling bearings faults in this paper. Genetic Artificial Neural Networks(GA-ANN) overcomes BP neural network’s fault of slow convergence, long hours of training, and falling into the local minimum point. And First, the signal is processed through the wavelet deoising, Then, th...

Keywords: Self-checking adder Carry-select adder Fault localization Self-repairing adder Multiple faults a b s t r a c t In this paper we propose an area-efficient self-repairing adder that can repair multiple faults and identify the particular faulty full adder. Fault detection and recovery has been carried out using self-checking full adders that can diagnose the fault based on internal funct...