Quantifying the reliability of fault classifiers - CentraleSupélec
Journal Articles Information Sciences Year : 2014

Quantifying the reliability of fault classifiers

Abstract

Fault diagnostics problems can be formulated as classification tasks. Due to limited data and to uncertainty, classification algorithms are not perfectly accurate in practical applications. Maintenance decisions based on erroneous fault classifications result in inefficient resource allocations and/or operational disturbances. Thus, knowing the accuracy of classifiers is important to give confidence in the maintenance decisions. The average accuracy of a classifier on a test set of data patterns is often used as a measure of confidence in the performance of a specific classifier. However, the performance of a classifier can vary in different regions of the input data space. Several techniques have been proposed to quantify the reliability of a classifier at the level of individual classifications. Many of the proposed techniques are only applicable to specific classifiers, such as ensemble techniques and support vector machines. In this paper, we propose a meta approach based on the typicalness framework (Kolmogorov's concept of randomness), which is independent of the applied classifier. We apply the approach to a case of fault diagnosis in railway turnout systems and compare the results obtained with both extreme learning machines and echo state networks.
Fichier principal
Vignette du fichier
FINK_ZIO_WEIDMANN_INFORMATION_SCIENCES_2013.pdf (164.45 Ko) Télécharger le fichier
Origin Files produced by the author(s)
Loading...

Dates and versions

hal-00930985 , version 1 (14-01-2014)

Identifiers

Cite

Olga Fink, Enrico Zio, Ulrich Weidmann. Quantifying the reliability of fault classifiers. Information Sciences, 2014, 266, pp.65-74. ⟨10.1016/j.ins.2013.12.008⟩. ⟨hal-00930985⟩
208 View
565 Download

Altmetric

Share

More