scholarly journals Machine Learning Approach to the Prediction of Surface Roughness Using Statistical Features of Vibration Signal Acquired in Turning

2015 ◽  
Vol 50 ◽  
pp. 282-288 ◽  
Author(s):  
M. Elangovan ◽  
N.R. Sakthivel ◽  
S. Saravanamurugan ◽  
Binoy.B. Nair ◽  
V. Sugumaran
Keyword(s):  
Machine Learning ◽  
Surface Roughness ◽  
Vibration Signal ◽  
Learning Approach ◽  
Statistical Features ◽  
Machine Learning Approach

scholarly journals A Comparative Study for Condition Monitoring on Wind Turbine Blade using Vibration Signals through Statistical Features: a Lazy Learning Approach

2018 ◽  
Vol 7 (4.10) ◽  
pp. 190 ◽  
Author(s):  
A. Joshuva ◽  
V. Sugumaran
Keyword(s):  
Machine Learning ◽  
Wind Turbine ◽  
Phase 1 ◽  
Turbine Blades ◽  
Computational Time ◽  
Learning Approach ◽  
Statistical Features ◽  
Wind Turbine Blades ◽  
Vibration Signals ◽  
Machine Learning Approach

This study is to identify whether the wind turbine blades are in good or faulty conditions. If faulty, then the objective to find which fault condition are the blades subjected to. The problem identification is carried out by machine learning approach using vibration signals through statistical features. In this study, a three bladed wind turbine was chosen and faults like blade cracks, hub-blade loose connection, blade bend, pitch angle twist and blade erosion were considered. Here, the study is carried out in three phases namely, feature extraction, feature selection and feature classification. In phase 1, the required statistical features are extracted from the vibration signals which obtained from the wind turbine through accelerometer. In phase 2, the most dominating or the relevant feature is selected from the extracted features using J48 decision tree algorithm. In phase 3, the selected features are classified using machine learning classifiers namely, K-star (KS), locally weighted learning (LWL), nearest neighbour (NN), k-nearest neighbours (kNN), instance based K-nearest using log and Gaussian weight kernels (IBKLG) and lazy Bayesian rules classifier (LBRC). The results were compared with respect to the classification accuracy and the computational time of the classifier.  

1552-P: Machine Learning Approach to Decision-Making for Initial Insulin Use in Japanese Patients with Type 2 Diabetes

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 1552-P
Author(s):  
KAZUYA FUJIHARA ◽  
MAYUKO H. YAMADA ◽  
YASUHIRO MATSUBAYASHI ◽  
MASAHIKO YAMAMOTO ◽  
TOSHIHIRO IIZUKA ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Decision Making ◽  
Japanese Patients ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Insulin Use
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