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.

Online NoC Switch Fault Detection and Diagnosis Using a High Level Fault Model

Abstract: This paper presents an efficient method for online testing of NoC switches. This method deals with control faults of NoC switches; i.e. the routing faults which cause NoC packets to be sent to output ports not intended to. A high level fault model has been proposed in this paper to model switch routing faults. The proposed method is evaluated by fault simulation that is based on our high-level fault model. This simulation and evaluation environment is modeled at the transaction level in VHDL.

Two-stage fault location

Abstract: A two-stage procedure for locating VLSI faults is presented. The approach utilizes dynamic fault dictionaries, test set partitioning, and reduced fault lists to achieve a reduction in size and complexity over classic static fault dictionaries. An industrial implementation is reported in which faults were injected and diagnosed in a VLSI chip and the perjiormunce of two-stage fault location was measured.

Capturing High-Discriminative Fault Features for Electronics-Rich Analog System via Deep Learning

Abstract: Fault detection and isolation (FDI) is very difficult for electronics-rich analog systems due to its sophisticated mechanism and variable operational conditions. Traditionally, FDI in such systems is done through the monitoring of deviation of output signals in voltage or current at system level, which commonly arises from the degradation of one or more critical components. Therefore, FDI can be transformed to a multiclass classification task given the extracted features of the output signals in voltage or current of the circuit. Traditional feature extraction on the circuit output is mostly based on time-domain, frequency-domain, or time-frequency signal processing, which collapse high-dimensional raw signals into a lower dimensional feature set. Such low-dimensional feature set usually suffers from information loss so as to affect the accuracy of the later fault diagnosis. In order to retain as much information as possible, deep learning is proposed which employs a hierarchical structure to capture the different levels of semantic representations of the signals. In this paper, a novel fault diagnostic application of Gaussian–Bernoulli deep belief network (GB-DBN) for electronics-rich analog systems is developed which can more effectively capture the high-order semantic features within the raw output signals. The novel fault diagnosis is validated experimentally on two typical analog filter circuits. Experimental results show the fault diagnosis based on GB-DBN is with superior diagnostic performance than the traditional feature extraction methods.

A new PMU-based fault detection/location technique for transmission lines with consideration of arcing fault discrimination-part II: performance evaluation

Abstract: The theory and algorithms of the proposed technique have been presented in Part I of this two-paper set. In Part II of this two-paper set, the proposed technique is evaluated by considerable simulation cases simulated by the Matlab/Power system Blockset simulator. For the proposed fault detector, the trip time achieved can be up to 3.25 ms and the average value of trip times is about 8 ms for both permanent and arcing faults on transmission lines. For the proposed fault locator, the accuracy can be up to 99.99% and the error does not exceed 0.45%. Moreover, the proposed arcing fault discriminator can discriminate between arcing and permanent faults within four cycles after fault inception. It has proven to be an effective tool to block reclosing on the permanent faults in the computer simulations. The simulation results also demonstrate that the presented extended discrete Fourier transform algorithm eliminates effectively the error caused by exponentially decaying dc offset on fundamental and harmonic phasor computations. Finally, a test case using the real-life measured data proves the feasibility of the proposed technique.