Remaining Life-Time Assessment of Gear Box using Regression Model

Joshuva, A.; Sugumaran, V.; Amarnath, M.; Lee, Sang Kwon

doi:10.17485/ijst/2016/v9i47/107933

Cited by 12 publications

(9 citation statements)

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“…The mean absolute error was found to be 0.0496. It is a measure used to measure how close forecasts or prediction are with the ultimate result [30]. The root mean square error was found to be 0.1621.…”

Section: Resultsmentioning

confidence: 99%

Logistic Model Tree Classifier for Condition Monitoring of Wind Turbine Blades

2019

IJRTE

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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.

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Section: Resultsmentioning

confidence: 99%

Logistic Model Tree Classifier for Condition Monitoring of Wind Turbine Blades

2019

IJRTE

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“…ARMA model was varied from 1 to 100. For each order, the ARMA features were extracted and the dominating feature is selected using J48 decision tree algorithm (Joshuva et al, 2016). The graph plot of the order of ARMA model varied from 1 to 100 with respect to their classification accuracy is shown in Figure 6.…”

Section: Resultsmentioning

confidence: 99%

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

Joshuva

Sugumaran

2018

PIE

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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.

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“…The class-wise accuracy is expressed in terms of the true positive rate (TP), false positive rate (FP), precision, recall and F-Measure [45].‖ TP is used to predict the ratio of positives which are correctly classified as faults. FP is commonly described as a false alarm in which the result that shows a given fault condition has been achieved when it really has not been achieved [46]. -The true positive (TP) rate should be close to 1 and the false positive (FP) rate should be close to 0 to propose the classifier is a better classifier for the problem classification [47].…”

Section: Figure 6: Classification Accuracy For Number Of Featuresmentioning

confidence: 99%

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

2019

IJRTE

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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.”

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Remaining Life-Time Assessment of Gear Box using Regression Model

Cited by 12 publications

References 0 publications

Logistic Model Tree Classifier for Condition Monitoring of Wind Turbine Blades

Logistic Model Tree Classifier for Condition Monitoring of Wind Turbine Blades

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

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

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