10 research outputs found
A fuzzy k-modes algorithm for clustering categorical data
This correspondence describes extensions to the fuzzy k-means algorithm for clustering categorical data. By using a simple matching dissimilarity measure for categorical objects and modes instead of means for clusters, a new approach is developed, which allows the use of the k-means paradigm to efficiently cluster large categorical data sets. A fuzzy k-modes algorithm is presented and the effectiveness of the algorithm is demonstrated with experimental results.published_or_final_versio
Harmony Search-Based Cluster Initialization For Fuzzy C-Means Segmentation Of MR Images.
We propose a new approach to tackle the well known fuzzy c-means (FCM) initialization problem
Thermal performance evaluation of an induced draft evaporative cooling system through Adaptive Neuro-Fuzzy Interference System (ANFIS) model and mathematical model
The shift from fossil fuel to more renewable electricity generation will require the broader implementation of Demand Side Response (DSR) into the grid. Utility processes in industry are suited for this, having a large thermal time constant or buffer, and large electricity consumption. A widespread utility system in industry is an induced draft evaporative cooling tower. Considering the safety aspect, such a process needs to maintain cooling water temperature within predefined safe boundaries. Therefore, in this paper, two modelling methods for the prediction of the basin temperature of an induced draft evaporative cooling tower are proposed. Both a white box and a black box methodology are presented, based on the physical principles of fluid dynamics and adaptive neuro-fuzzy interference system (ANFIS) modelling, respectively. By analysing the accuracy of both models with a focus to cooling tower fan state changes, i.e., DSR purposes, it is shown that the white box model performs best. Fostering the idea of using such a system for DSR purposes, the concept of design for flexibility is also touched upon, discussing the thermal mass. Pre-cooling, where the temperature of the cooling water basin is lowered before a fan switch off period, was simulated with the white box model. It was shown that beneficial pre-cooling (to lower the temperature peak) is limited in time
Wind Turbine Fault Detection: an Unsupervised vs Semi-Supervised Approach
The need for renewable energy has been growing in recent years for the reasons we all
know, wind power is no exception. Wind turbines are complex and expensive structures
and the need for maintenance exists. Conditioning Monitoring Systems that make use of
supervised machine learning techniques have been recently studied and the results are
quite promising. Though, such systems still require the physical presence of professionals
but with the advantage of gaining insight of the operating state of the machine in use, to
decide upon maintenance interventions beforehand. The wind turbine failure is not an
abrupt process but a gradual one.
The main goal of this dissertation is: to compare semi-supervised methods to at tack the problem of automatic recognition of anomalies in wind turbines; to develop an
approach combining the Mahalanobis Taguchi System (MTS) with two popular fuzzy
partitional clustering algorithms like the fuzzy c-means and archetypal analysis, for the
purpose of anomaly detection; and finally to develop an experimental protocol to com paratively study the two types of algorithms.
In this work, the algorithms Local Outlier Factor (LOF), Connectivity-based Outlier
Factor (COF), Cluster-based Local Outlier Factor (CBLOF), Histogram-based Outlier Score
(HBOS), k-nearest-neighbours (k-NN), Subspace Outlier Detection (SOD), Fuzzy c-means
(FCM), Archetypal Analysis (AA) and Local Minimum Spanning Tree (LoMST) were
explored.
The data used consisted of SCADA data sets regarding turbine sensorial data, 8 to tal, from a wind farm in the North of Portugal. Each data set comprises between 1070
and 1096 data cases and characterized by 5 features, for the years 2011, 2012 and 2013.
The analysis of the results using 7 different validity measures show that, the CBLOF al gorithm got the best results in the semi-supervised approach while LoMST won in the
unsupervised scenario. The extension of both FCM and AA got promissing results.A necessidade de produzir energia renovável tem vindo a crescer nos últimos anos pelas
razões que todos sabemos, a energia eólica não é excepção. As turbinas eólicas são es truturas complexas e caras e a necessidade de manutenção existe. Sistemas de Condição
Monitorizada utilizando técnicas de aprendizagem supervisionada têm vindo a ser estu dados recentemente e os resultados são bastante promissores. No entanto, estes sistemas
ainda exigem a presença física de profissionais, mas com a vantagem de obter informa ções sobre o estado operacional da máquina em uso, para decidir sobre intervenções de
manutenção antemão.
O principal objetivo desta dissertação é: comparar métodos semi-supervisionados
para atacar o problema de reconhecimento automático de anomalias em turbinas eólicas;
desenvolver um método que combina o Mahalanobis Taguchi System (MTS) com dois mé todos de agrupamento difuso bem conhecidos como fuzzy c-means e archetypal analysis,
no âmbito de deteção de anomalias; e finalmente desenvolver um protocolo experimental
onde é possível o estudo comparativo entre os dois diferentes tipos de algoritmos.
Neste trabalho, os algoritmos Local Outlier Factor (LOF), Connectivity-based Outlier
Factor (COF), Cluster-based Local Outlier Factor (CBLOF), Histogram-based Outlier Score
(HBOS), k-nearest-neighbours (k-NN), Subspace Outlier Detection (SOD), Fuzzy c-means
(FCM), Archetypal Analysis (AA) and Local Minimum Spanning Tree (LoMST) foram
explorados.
Os conjuntos de dados utilizados provêm do sistema SCADA, referentes a dados sen soriais de turbinas, 8 no total, com origem num parque eólico no Norte de Portugal. Cada
um está compreendendido entre 1070 e 1096 observações e caracterizados por 5 caracte rísticas, para os anos 2011, 2012 e 2013. A ánalise dos resultados através de 7 métricas de
validação diferentes mostraram que, o algoritmo CBLOF obteve os melhores resultados
na abordagem semi-supervisionada enquanto que o LoMST ganhou na abordagem não
supervisionada. A extensão do FCM e do AA originou resultados promissores