Date of download: 10/12/2017 Copyright © ASME. All rights reserved. From: Application of Possibilistic C-Means for Fault Detection in Nuclear Power Plant Data J. Eng. Gas Turbines Power. 2015;137(6):062901-062901-7. doi:10.1115/1.4028809 Figure Legend: Representation of the results provided by the PCA-based Algorithm 3, accounting for the fact that for this algorithm the memberships can be either zero or one. Top left panel: classification of the same data as in Fig. 1 (from A through I: nine signals classified as normal; L: one signal classified as outlier). Top right panel: classification of the same data as in Fig. 2 (from A through G: seven signals classified as normal; L, I, H: first, second, and third outlier). Bottom panel: classification of the same data as in Fig. 3 (from A through E: six signals classified as normal; L: the actual outlier; I, H, G: misclassified data).

Date of download: 10/12/2017 Copyright © ASME. All rights reserved. From: Application of Possibilistic C-Means for Fault Detection in Nuclear Power Plant Data J. Eng. Gas Turbines Power. 2015;137(6):062901-062901-7. doi:10.1115/1.4028809 Figure Legend: Analysis of the power channels in the reactor system. Top left panel: initial data made of 95 time series where each signal has been normalized and rescaled to arbitrary unit (each curve corresponds to one time series). Top right panel: classification of Algorithm 2 after two iterations. Bottom panel: memberships for nine signals classified as normal (from A through H) and for the outlier provided by Algorithm 2 (L).

Date of download: 10/12/2017 Copyright © ASME. All rights reserved. From: Application of Possibilistic C-Means for Fault Detection in Nuclear Power Plant Data J. Eng. Gas Turbines Power. 2015;137(6):062901-062901-7. doi:10.1115/1.4028809 Figure Legend: Analysis of temperature time series in the SCWS. Top left panel: initial data made of 90 time series where each signal has been normalized and rescaled to arbitrary unit (each curve corresponds to one time series). Top right panel: centroids associated to one of the final third iteration for one of the 75 runs of Algorithm 2. Bottom left panel: memberships for one of the 75 four-class outcomes (from A through G: seven signals classified as normal; L, I, H: first, second, and third outlier). Bottom right panel: memberships for one of the 25 three-class outcome of Algorithm 2 (from A through H: eight signals classified as normal; L, I: first and second outlier).

Date of download: 10/12/2017 Copyright © ASME. All rights reserved. From: Application of Possibilistic C-Means for Fault Detection in Nuclear Power Plant Data J. Eng. Gas Turbines Power. 2015;137(6):062901-062901-7. doi:10.1115/1.4028809 Figure Legend: Analysis of the flux mapping routine in the RRS. Top left panel: initial data set made of 55 time series where each signal has been normalized and rescaled to arbitrary unit (each curve corresponds to one time series). Top right panel: the two classes produced by Algorithm 2 after the first iteration. Bottom left panel: membership values for one of the 95 runs where the second iteration does not provide any further classification (from A through I: nine signals classified as normal; L: one signal classified as outlier). Bottom right panel: membership values for one of the five runs where the second iteration provides a third class (from A through H: eight signals classified as normal; L: first signal classified as outlier; I: second signal classified as outlier.