Fuzzy PCA-Guided Robust $k$-Means Clustering

Honda, Katsuhiro; Notsu, Akira; Ichihashi, Hidetomo

doi:10.1109/tfuzz.2009.2036603

Cited by 82 publications

(31 citation statements)

References 20 publications

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“…Essentially, K-means clustering automatically divides a data set into K groups [19]. It proceeds by selecting k initial cluster centres and then iteratively refining them as follows.…”

Section: K-means Clustering Methodsmentioning

confidence: 99%

“…As one of the most important influencing meteorological parameters, the wind speed is still retained, but it is not used for PARAFAC. Thus there are 52 sensor signals left for PARAFAC analysis, which are associated with the parameters defining the performance of the turbine operations, such as the nacelle position (sensor 1); blades positions (sensors 2-8); mains currents (sensors 9-11); apparent power and active power (sensors 12-13); reactive power (sensor 14); pitch motor currents (sensors [15][16][17]; oil pressures (sensors [18][19]; oscillation signals (sensors 20-25), speeds of the generator and the rotor (sensors 26-32), temperatures of the generator windings, the gearbox bearings, the nacelle, the gearbox oil sump, the hydraulic fluid and the cooling water (sensors 33-52).…”

Section: Data Selectionmentioning

confidence: 99%

See 1 more Smart Citation

Simultaneous Fault Detection and Sensor Selection for Condition Monitoring of Wind Turbines

Zhang

2016

Energies

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Data collected from the supervisory control and data acquisition (SCADA) system are used widely in wind farms to obtain operation and performance information about wind turbines. The paper presents a three-way model by means of parallel factor analysis (PARAFAC) for wind turbine fault detection and sensor selection, and evaluates the method with SCADA data obtained from an operational farm. The main characteristic of this new approach is that it can be used to simultaneously explore measurement sample profiles and sensors profiles to avoid discarding potentially relevant information for feature extraction. With K-means clustering method, the measurement data indicating normal, fault and alarm conditions of the wind turbines can be identified, and the sensor array can be optimised for effective condition monitoring.

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“…Essentially, K-means clustering automatically divides a data set into K groups [19]. It proceeds by selecting k initial cluster centres and then iteratively refining them as follows.…”

Section: K-means Clustering Methodsmentioning

confidence: 99%

Section: Data Selectionmentioning

confidence: 99%

Simultaneous Fault Detection and Sensor Selection for Condition Monitoring of Wind Turbines

Zhang

2016

Energies

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“…The data set used in [21] was constructed from a Japanese novel "Kokoro" written by Soseki Natsume, which can be downloaded from Aozora Bunko (http://www.aozora.gr.jp). The novel is composed of 3 chapters, each of which include 36, 18, 56 sections, respectively.…”

Section: B Document Clusteringmentioning

confidence: 99%

A cluster validity index for FCM-type co-clustering

Muranishi

Honda

Notsu

et al. 2013

2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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Cluster validation is an important issue in FCMtype clustering applications and many validity indices have been proposed for selecting the optimal fuzzy partition. In most of FCM-type cluster validity indices, the number of clusters is evaluated by considering the partition quality and geometrical features. The quality of memberships was evaluated by the degree of overlapping or the boundary uncertainty, while the geometrical quality was measured by cluster compactness or cluster separation. In this paper, a new approach for FCM-type cluster validation in fuzzy co-clustering is proposed. Because fuzzy co-clustering extracts object-item dual clusters without using prototypes, a new concept of cluster separation is constructed without using the distances between cluster prototypes. The applicability of the new validity index is demonstrated in several numerical experiments including a document clustering application.

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“…This latter has a robust capability against noise and is quite successful in improving the robustness of a variety of fuzzy clustering algorithms (Dave and Krishnapuram, 1997;Tran and Wagner, 1999;Ichihashi et al, 2005;Soltani et al, 2010b;Honda et al, 2010). Moreover, a new error measure is introduced into the objective function to get a good result.…”

Section: Soltani Et Almentioning

confidence: 99%

A novel fuzzy c-regression model algorithm using a new error measure and particle swarm optimization

Soltani

Châari

Hmida

2012

International Journal of Applied Mathematics and Computer Science

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This paper presents a new algorithm for fuzzy c-regression model clustering. The proposed methodology is based on adding a second regularization term in the objective function of a Fuzzy C-Regression Model (FCRM) clustering algorithm in order to take into account noisy data. In addition, a new error measure is used in the objective function of the FCRM algorithm, replacing the one used in this type of algorithm. Then, particle swarm optimization is employed to finally tune parameters of the obtained fuzzy model. The orthogonal least squares method is used to identify the unknown parameters of the local linear model. Finally, validation results of two examples are given to demonstrate the effectiveness and practicality of the proposed algorithm.

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Fuzzy PCA-Guided Robust $k$-Means Clustering

Cited by 82 publications

References 20 publications

Simultaneous Fault Detection and Sensor Selection for Condition Monitoring of Wind Turbines

Simultaneous Fault Detection and Sensor Selection for Condition Monitoring of Wind Turbines

A cluster validity index for FCM-type co-clustering

A novel fuzzy c-regression model algorithm using a new error measure and particle swarm optimization

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