A Novel Method for Analog Fault Diagnosis Based on Neural Networks and Genetic Algorithms

Autor:	Yigang He, Yanghong Tan, Chun Cui, Guanyuan Qiu
Rok vydání:	2008
Předmět:	Nonlinear system Basis (linear algebra) Artificial neural network Computer science Feature (computer vision) Feature vector Genetic algorithm Electronic engineering Electrical and Electronic Engineering Fault (power engineering) Instrumentation Algorithm Linear circuit
Zdroj:	IEEE Transactions on Instrumentation and Measurement. 57:2631-2639
ISSN:	1557-9662 0018-9456
Popis:	A systematic method based on a neural network that utilizes a genetic algorithm (GNN) and the deviation space to diagnose faulty behavior in analog circuits under test (CUTs) is presented in the paper. To reduce the computational requirement of network simulations, we derive a unified fault feature, which can be extracted from measurable voltage deviation in the deviation space. The extracted unified feature vectors for single, double, and triple faults are characterized on the basis of measurable voltage deviation in the deviation space. Then, the faults can be classified by applying a neural network (NN) whose inputs are extracted from independent measurements - the transfer impedances at accessible nodes or the corresponding feature of various faults. It is applicable to linear circuits as well as nonlinear ones. The method presented minimizes the online measurements and offline computation. Illustrative examples verify the effectiveness of the proposed method.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::879ab1b255bf16442ed877ffd382d980 https://doi.org/10.1109/tim.2008.925009 Zobrazit plný text záznamu