Wind energy is one of the important renewable energy resources available in nature. It is one of the major resources for production of energy because of its dependability due to the development of the technology and relatively low cost. Wind energy is converted into electrical energy using rotating blades. Due to environmental conditions and large structure, the blades are subjected to various vibration forces that may cause damage to the blades. This leads to a liability in energy production and turbine shutdown. The downtime can be reduced when the blades are diagnosed continuously using structural health condition monitoring. These are considered as a pattern recognition problem which consists of three phases namely, feature extraction, feature selection, and feature classification. In this study, statistical features were extracted from vibration signals, feature selection was carried out using a J48 decision tree algorithm and feature classification was performed using best-first tree algorithm and functional trees algorithm. The better algorithm is suggested for fault diagnosis of wind turbine blade. © 2017 ISA

Sugumaran V

Mechanical

School of Mechanical Engineering

Chennai Campus

Joshuva. A

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

ISA Transactions

The main objective of the proposed research study is to discriminate different blade fault conditions which affect the wind turbine blades under operating condition through machine learning approach. A three bladed wind turbine was chosen and the faults like blade bend, blade cracks, blade erosion, hub-blade loose connection and pitch angle twist were considered in the study. This problem is formulated as a machine learning problem which consists of three phases, namely feature extraction, feature selection and feature classification. Histogram features were extracted from vibration signals and feature selection was carried out using J48 decision tree algorithm. Feature classification was performed using lazy classifiers like nearest neighbour, k-nearest neighbours, locally weighted learning and K-star classifier. The results of these classifiers were compared with respect to their correctly classified instances (accuracy percentage) and found that, locally weighted learning yielded a maximum accuracy of 93.83% with a computational time of 0.07 s. © 2019 Elsevier Ltd

Measurement

A lazy learning approach for condition monitoring of wind turbine blade using vibration signals and histogram features

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.

Fulltext

International Journal of Engineering & Technology

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

In wind turbine, blades are the major component for capturing the wind, however, due to environmental conditions it prompts to get a fault. To overcome this problem, a machine learning based condition monitoring technique is incorporated into the wind turbine to identify the fault classification which occurred in the blade. In this study, a three-bladed horizontal axis wind turbine was chosen and the faults like blade bend, blade cracks, hub-blade loose connection, blade erosion and pitch angle twist were considered as these are the faults mostly affect the turbine blade. In this study, the autoregressive-moving-average (ARMA) features have been extracted from the raw signal and the dominating feature was selected through J48 decision tree algorithm followed by the fault classification using machine learning classifiers. The results were compared with respect to the classification accuracy and their computational time of the classifier. Copyright © 2018 Inderscience Enterprises Ltd.

Progress in Industrial Ecology, An International Journal

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

Indian Journal of Science and Technology

Wind Turbine Blade Fault Diagnosis Using Vibration Signals through Decision Tree Algorithm

Hydraulic brakes in automobiles are important components for the safety of passengers; therefore, the brakes are a good subject for condition monitoring. The condition of the brake components can be monitored by using the vibration characteristics. On-line condition monitoring by using machine learning approach is proposed in this paper as a possible solution to such problems. The vibration signals for both good as well as faulty conditions of brakes were acquired from a hydraulic brake test setup with the help of a piezoelectric transducer and a data acquisition system. Descriptive statistical features were extracted from the acquired vibration signals and the feature selection was carried out using the C4.5 decision tree algorithm. There is no specific method to find the right number of features required for classification for a given problem. Hence an extensive study is needed to find the optimum number of features. The effect of the number of features was also studied, by using the decision tree as well as Support Vector Machines (SVM). The selected features were classified using the C-SVM and Nu-SVM with different kernel functions. The results are discussed and the conclusion of the study is presented. © 2014 Elsevier Ltd. All rights reserved.

Mechanical Systems and Signal Processing

Fault diagnosis of automobile hydraulic brake system using statistical features and support vector machines

Hydraulic brakes are the most important components in automobile. It requires advanced supervision and fault diagnosis to improve the safety of passenger, reliability and economy. Condition monitoring is one of the major division through which the reliability of such components could be monitored. The condition of the brake components can be monitored by using the vibration characteristics which will reveal the condition of the brake systems. In this paper machine learning algorithm using vibration monitoring is proposed as a possible solution to this problem. From the hydraulic brake test set up, the vibration signals were acquired by using a piezoelectric transducer and data acquisition system. C4.5 decision tree algorithm was used to extract statistical features from vibration signals. Feature selection was also carried out. Since no much of methodologies are available to find the effective number of features for a given problem, a detailed study is needed to find the best possible number of features. Hence the effect of number of features was studied by using decision tree. The selected features were classified using C4.5 decision tree algorithm and Best first decision tree algorithm with pre pruning and post pruning techniques. The results are discussed and conclusions of the study are presented. © 2013 Elsevier Ltd. All rights reserved.

Comparative study of decision tree classifier and best first tree classifier for fault diagnosis of automobile hydraulic brake system using statistical features

Bearing is one of vital transmission elements, finding numerous applications in small, medium and large machineries. Sound and vibration signals of a rotating machine contain the dynamic information about its operating condition. There are many articles in the literature reporting suitability of vibration signals for fault diagnosis applications; however, the transducer used for vibration signals (accelerometers) is costly. This prevents small scale industries and low cost equipments from using diagnostic tools on affordability ground. On the other hand, transducers used for acquiring sound signals are relatively low cost or/and affordable. Hence, there is a need for studying the use of sound signal for fault diagnosis applications. This paper uses acoustic signals (sound) acquired from near field area of bearings in good and simulated faulty conditions for the purpose of fault diagnosis through machine learning approach. The descriptive statistical features were extracted from sound signals and the important ones were selected using decision tree (dimensionality reduction). The selected features were then used for classification using C4.5 decision tree algorithm. The paper also discusses the effect of features, effect of various classifier parameters on classification accuracy. © 2012 Elsevier Ltd. All rights reserved.

Exploiting sound signals for fault diagnosis of bearings using decision tree

Renewable Energy

Design and kinetic analysis of wind turbine blade-hub-tower coupled system

A data driven approach for condition monitoring of wind turbine blade using vibration signals through best-first tree algorithm and functional trees algorithm: A comparative study

Journal	Data powered by TypesetISA Transactions
Publisher	Data powered by TypesetElsevier BV
ISSN	0019-0578
Open Access	No