A machine learning approach for condition monitoring of wind turbine blade using autoregressive moving average (ARMA) features through vibration signals: a comparative study

2018 ◽  
Vol 12 (1/2) ◽  
pp. 14 ◽  
Author(s):  
A. Joshuva ◽  
V. Sugumaran
Keyword(s):  
Machine Learning ◽  
Comparative Study ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Condition Monitoring ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Learning Approach ◽  
Vibration Signals ◽  
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.  

scholarly journals Implementation of Rule based Classifiers for Wind Turbine Blade Fault Diagnosis using Vibration Signals

2019 ◽  
Vol 8 (2S11) ◽  
pp. 320-331
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Feature Selection ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Learning Approach ◽  
Feature Classification ◽  
Rule Based ◽  
Machine Learning Approach

The fast developing wind industry has revealed a requirement for more multifaceted fault diagnosis system in the segments of a wind turbine. “Present wind turbine researches concentrate on enhancing their dependability quality and decreasing the cost of energy production, especially when wind turbines are worked in off-shore places. Wind turbine blades are ought to be an important component among the other basic segments in the wind turbine framework since they transform dynamic energy of wind into useable power and due to environmental conditions, it get damage often and cause lack in productivity. The main objective of this study is to carry out a fault identification model for wind turbine blade using a machine learning approach through vibration data to classify the blade condition. Here five faults namely, blade bend, hub-blade loose connection, blade cracks, blade erosion and pitch angle twist have been considered. Machine learning approach has three steps namely feature extraction, feature selection and feature classification. Feature extraction was carried out by statistical analysis followed by feature selection using J48 decision tree algorithm. Feature classification was done using twelve rule based classifiers using WEKA. The results were compared with respect to the classification accuracy and the computational time of the classifier.”

scholarly journals Application of FURIA for Finding the Faults in a Hydraulic Brake System Using a Vibration Analysis through a Machine Learning Approach

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Alamelu Manghai T. M ◽  
Jegadeeshwaran R
Keyword(s):  
Machine Learning ◽  
Fault Diagnosis ◽  
Decision Tree ◽  
Continuous Monitoring ◽  
Selection Process ◽  
Brake System ◽  
Learning Approach ◽  
Vibration Signals ◽  
Hydraulic Brake ◽  
Machine Learning Approach

Vibration-based continuous monitoring system for fault diagnosis of automobile hydraulic brake system is presented in this study. This study uses a machine learning approach for the fault diagnosis study. A hydraulic brake system test rig was fabricated. The vibration signals were acquired from the brake system under different simulated fault conditions using a piezoelectric transducer. The histogram features were extracted from the acquired vibration signals. The feature selection process was carried out using a decision tree. The selected features were classified using fuzzy unordered rule induction algorithm ( FURIA ) and Repeated Incremental Pruning to Produce Error Reduction ( RIPPER ) algorithm. The classification results of both algorithms for fault diagnosis of a hydraulic brake system were presented. Compared to RIPPER and J48 decision tree, the FURIA performs better and produced 98.73 % as the classification accuracy.

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