Topic
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.
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TL;DR: In this article, the authors proposed an adaptive adaptive adaptive control for reconfigurable manipulator actuator based on local joint information, where the unknown input state observer is exploited for fault identification and a compensation term is added to the proposed control algorithm for switching to realize active fault tolerant control when subsystem in fault.
Abstract: This paper is concerned with the active fault tolerant control problem for reconfigurable manipulator actuator based on local joint information. It is considered that the entire reconfigurable manipulator system consists of a couple of independent joint modules as subsystems, which are controlled using unified radial basis function neural network adaptive algorithm using local joint information when actuators are fault free. For the subsystem in actuator fault situation, fault detection is achieved through comparing the user defined threshold to the residual between actual velocity value and nonlinear velocity observation value. The unknown input state observer is exploited for fault identification. Based on the information aforementioned, a compensation term is added to the proposed control algorithm for switching to realize active fault tolerant control when subsystem in fault. The advantages of the presented scheme are that unlike the complex control structure in centralized control, this scheme possesses simple control structure, as well as could isolate and tolerant the fault in subsystem. Furthermore, it can be easily applied to different configurations without any parameters modification. It means that the local fault could not affect the joint in normal situation. In order to demonstrate the effectiveness of the proposed method, two different 2-DOF reconfigurable manipulators are employed for simulation.
49 citations
TL;DR: In this article, a stochastic functional model based method (FMBM) is proposed to detect and localize a scale aircraft skeleton structure with a single-excitation single-response (SISO) signal.
49 citations
TL;DR: In this article, an analytic model for power system fault diagnosis is developed to utilize redundancy and temporal information of alarms. But, the model is not suitable for the case where redundant alarms with synchronized timestamps would be received during a fault.
Abstract: In modern transmission systems, duplicated protections are widely employed to enhance reliability, and global positioning system (GPS) substation clocks are set up to deliver a higher degree of synchronization. Thus, redundant alarms tagged with synchronized timestamps would be received during a fault. Both redundancy and timestamps of alarms consist of rich and useful information. Given this background, an effort is made to develop a novel analytic model for power system fault diagnosis so as to well utilize redundancy and temporal information of alarms. An alarm preprocessing procedure is proposed to preserve such properties without impairing diagnosis accuracy and efficiency. A new fault hypothesis is established to systematically take account of malfunctions of protective devices, missing and distorted alarm messages, and timestamp errors in the proposed model. An objective function is developed for fault diagnosis, and could accommodate various protection schemes and bus configurations. Based on the presented fault diagnosis method, a software package is developed. Several fault scenarios occurred in an actual power system in China are served for demonstrating the correctness and efficiency of the developed software package.
49 citations
18 Nov 1991
TL;DR: The author describes a method by which fault models can be developed for neural networks visualized at the abstract level, thus allowing their inherent fault tolerance to be probed, and increases the possibility of their being generic in nature due to the independence of implementation.
Abstract: The author describes a method by which fault models can be developed for neural networks visualized at the abstract level, thus allowing their inherent fault tolerance to be probed. The derivation of such fault models has two stages: the location of where faults can occur, and the definition of the faults' characteristics. As an example, a fault model for the multilayer perceptron neural network model is developed for each stage. The abstract nature of such fault models increases the possibility of their being generic in nature due to the independence of implementation. Also, they will allow the inherent fault tolerance of a neural network to be constructively and realistically investigated. >
49 citations
26 Oct 2004
TL;DR: A new transition fault model called as late as possible transition fault (ALAPTF) model, which is capable of detecting smaller gate delays and produces better results in case of process variations is presented.
Abstract: The paper presents a new transition fault model called As late As Possible Transition Fault (ALAPTF) Model. The model aims at detecting smaller delays, which will be missed by both the traditional transition fault model and the path delay model. The model makes sure that each transition is launched as late as possible at the fault site, accumulating the small delay defects along its way. Because some transition faults may require multiple paths to be launched, the simple path-delay model will miss such faults. Results on ISCAS'85 and ISCAS'89 benchmark circuits shows that for all the cases, the new model is capable of detecting smaller gate delays and produces better results in case of process variations. For all circuits, on an average, 30% of the time the transition reaches later than traditional models. The algorithm proposed also detects robust and non-robust paths along with the transition faults and the execution time is linear to the circuit size.
49 citations