Sentence examples for classification of faults from inspiring English sources

Detection and classification of faults are achieved with the help of approximate coefficients based alienation coefficients of current signals, measured from all the buses of system.

Several methods for the location and classification of faults in power transmission lines using computational intelligence and digital signal processing techniques have been described in literature.

Machine learning methods involving feature extraction, feature selection, and classification of faults offer a systematic approach to fault diagnosis and can be used in automated or unmanned environments.

This paper represents a comprehensive effort to review and highlight recent advances, ongoing research and future prospects, as reported in the literature, on the classification of faults in c-Si PV modules and advanced diagnosis in the field, by means of the increasingly popular method of infrared thermal (IRT) imaging.

In this paper, two new methods are presented for detection and classification of faults.

In this method, the detection and classification of faults are not affected by the fault type and location, pre-fault power, fault resistance or fault inception time.

In this paper, a novel method is introduced to realize classification of fault signal without extracting feature vector preliminarily.

The obtained errors are lower than 1% and accuracy is 100% for the classification of fault signals with noise.

Four different ANN networks are designed for detection of the fault, fault section identification, classification of fault and location of fault in time domain.

The results clearly indicate more than 97.61% correct classification of fault classes in the example circuits of various sizes in the presence of similar faults.

Accuracy of the proposed method is 100% for fault detection and 99.99% for classification of fault from all the tested fault cases.