2018
Santos, Pedro; Maudes-Raedo, Jesús; Bustillo, Andrés
Identifying maximum imbalance in datasets for fault diagnosis of gearboxes Journal Article
In: Journal of Intelligent Manufacturing, vol. 29, no. 2, pp. 333-351, 2018, ISSN: 0956-5515.
Abstract | Links | BibTeX | Tags: Fault diagnosis, Metrics Gearbox, Multi-class imbalance, Wind turbines Ensembles
@article{Santos2018,
title = {Identifying maximum imbalance in datasets for fault diagnosis of gearboxes},
author = {Pedro Santos and Jesús Maudes-Raedo and Andrés Bustillo},
url = {https://link.springer.com/article/10.1007%2Fs10845-015-1110-0},
doi = {10.1007/s10845-015-1110-0},
issn = {0956-5515},
year = {2018},
date = {2018-02-01},
journal = {Journal of Intelligent Manufacturing},
volume = {29},
number = {2},
pages = {333-351},
abstract = {Research into fault diagnosis in rotating machinery with a wide range of variable loads and speeds, such as the gearboxes of wind turbines, is of great industrial interest. Although appropriate sensors have been identified, an intelligent system that classifies machine states remains an open issue, due to a paucity of datasets with sufficient fault cases. Many of the proposed solutions have been tested on balanced datasets, containing roughly equal percentages of wind-turbine failure instances and instances of correct performance. In practice, however, it is not possible to obtain balanced datasets under real operating conditions. Our objective is to identify the most suitable classification technique that will depend least of all on the level of imbalance in the dataset. We start by analysing different metrics for the comparison of classification techniques on imbalanced datasets. Our results pointed to the Unweighted Macro Average of the F-measure, which we consider the most suitable metric for this diagnosis. Then, an extensive set of classification techniques was tested on datasets with varying levels of imbalance. Our conclusion is that a Rotation Forest ensemble of C4.4 decision trees, modifying the training phase of the classifier with a cost-sensitive approach, is the most suitable prediction model for this industrial task. It maintained its good performance even when the minority classes rate was as low as 6.5 %, while the majority of the other classifiers were more sensitive to the level of database imbalance and failed standard performance objectives, when the minority classes rate was lower than 10.5 %.},
keywords = {Fault diagnosis, Metrics Gearbox, Multi-class imbalance, Wind turbines Ensembles},
pubstate = {published},
tppubtype = {article}
}
Research into fault diagnosis in rotating machinery with a wide range of variable loads and speeds, such as the gearboxes of wind turbines, is of great industrial interest. Although appropriate sensors have been identified, an intelligent system that classifies machine states remains an open issue, due to a paucity of datasets with sufficient fault cases. Many of the proposed solutions have been tested on balanced datasets, containing roughly equal percentages of wind-turbine failure instances and instances of correct performance. In practice, however, it is not possible to obtain balanced datasets under real operating conditions. Our objective is to identify the most suitable classification technique that will depend least of all on the level of imbalance in the dataset. We start by analysing different metrics for the comparison of classification techniques on imbalanced datasets. Our results pointed to the Unweighted Macro Average of the F-measure, which we consider the most suitable metric for this diagnosis. Then, an extensive set of classification techniques was tested on datasets with varying levels of imbalance. Our conclusion is that a Rotation Forest ensemble of C4.4 decision trees, modifying the training phase of the classifier with a cost-sensitive approach, is the most suitable prediction model for this industrial task. It maintained its good performance even when the minority classes rate was as low as 6.5 %, while the majority of the other classifiers were more sensitive to the level of database imbalance and failed standard performance objectives, when the minority classes rate was lower than 10.5 %.
2015
Santos, Pedro; Villa, Luisa F; Reñones, Anibal; Bustillo, Andrés; Maudes-Raedo, Jesús
An SVM-Based Solution for Fault Detection in Wind Turbines Journal Article
In: Sensors, vol. 15, no. 3, pp. 5627-5648, 2015, ISSN: 1424-8220.
Abstract | Links | BibTeX | Tags: Fault diagnosis, Neural networks, Support vector machines, wind turbines
@article{Santos2015,
title = {An SVM-Based Solution for Fault Detection in Wind Turbines},
author = {Pedro Santos and Luisa F Villa and Anibal Reñones and Andrés Bustillo and Jesús Maudes-Raedo},
url = {http://www.mdpi.com/1424-8220/15/3/5627},
doi = {10.3390/s150305627},
issn = {1424-8220},
year = {2015},
date = {2015-03-09},
journal = {Sensors},
volume = {15},
number = {3},
pages = {5627-5648},
abstract = {Research into fault diagnosis in machines with a wide range of variable loads and speeds, such as wind turbines, is of great industrial interest. Analysis of the power signals emitted by wind turbines for the diagnosis of mechanical faults in their mechanical transmission chain is insufficient. A successful diagnosis requires the inclusion of accelerometers to evaluate vibrations. This work presents a multi-sensory system for fault diagnosis in wind turbines, combined with a data-mining solution for the classification of the operational state of the turbine. The selected sensors are accelerometers, in which vibration signals are processed using angular resampling techniques and electrical, torque and speed measurements. Support vector machines (SVMs) are selected for the classification task, including two traditional and two promising new kernels. This multi-sensory system has been validated on a test-bed that simulates the real conditions of wind turbines with two fault typologies: misalignment and imbalance. Comparison of SVM performance with the results of artificial neural networks (ANNs) shows that linear kernel SVM outperforms other kernels and ANNs in terms of accuracy, training and tuning times. The suitability and superior performance of linear SVM is also experimentally analyzed, to conclude that this data acquisition technique generates linearly separable datasets.},
keywords = {Fault diagnosis, Neural networks, Support vector machines, wind turbines},
pubstate = {published},
tppubtype = {article}
}
Research into fault diagnosis in machines with a wide range of variable loads and speeds, such as wind turbines, is of great industrial interest. Analysis of the power signals emitted by wind turbines for the diagnosis of mechanical faults in their mechanical transmission chain is insufficient. A successful diagnosis requires the inclusion of accelerometers to evaluate vibrations. This work presents a multi-sensory system for fault diagnosis in wind turbines, combined with a data-mining solution for the classification of the operational state of the turbine. The selected sensors are accelerometers, in which vibration signals are processed using angular resampling techniques and electrical, torque and speed measurements. Support vector machines (SVMs) are selected for the classification task, including two traditional and two promising new kernels. This multi-sensory system has been validated on a test-bed that simulates the real conditions of wind turbines with two fault typologies: misalignment and imbalance. Comparison of SVM performance with the results of artificial neural networks (ANNs) shows that linear kernel SVM outperforms other kernels and ANNs in terms of accuracy, training and tuning times. The suitability and superior performance of linear SVM is also experimentally analyzed, to conclude that this data acquisition technique generates linearly separable datasets.