Logistic Model Tree Classifier for Condition Monitoring of Wind Turbine Blades

Abstract

Wind energy is one of the essential renewable energy resources because of its consistency due to the development of the technology and relative cost affordability. The wind energy is converted into electrical energy using rotating blades which are connected to the generator. Due to environmental conditions and large construction, the blades are subjected to various faults and cause the lack of productivity. The downtime can be reduced when they are diagnosed periodically using condition monitoring technique. These are considered as a machine learning problem which consists of three phases, namely feature extraction, feature selection and fault classification. In this study, statistical features are extracted from vibration signals, feature selection are carried out using J48 algorithm and the fault classification was carried out using logistic model tree algorithm.

Effect of relative humidity, storage days, and packaging on pecan kernel texture: Analyses and modeling

“…A three‐parameter logistic (3PL) model, a type of sigmoid model, was used to predict the changes in pecan textural attributes over time. The 3PL model is a type of logistic model prominently used in immunoassays research (Herman, Scherer, & Shan, 2008 ) such as ELISA, microbial growth prediction (Fujikawa, 2010 ), dose–response relationships (Andrade‐Mogrovejo et al, 2022 ; Carøe, Ebbehøj, Bonde, Flachs, & Agner, 2018 ; ElHarouni et al, 2022 ), and geological phenomena (Chen et al, 2019 ; Joshuva, Deenadayalan, Sivakumar, Sathishkumar, & Vishnuvardhan, 2019 ). The parameters give unique information such as maximum value to response achieved (asymptote), slope, and the value of a predictor variable for median response (inflection point) (Figure 1 ).…”