2020
Juez-Gil, Mario; Saucedo-Dorantes, Juan José; Arnaiz-González, Álvar; López-Nozal, Carlos; García-Osorio, César; Lowe, David
Early and extremely early multi-label fault diagnosis in induction motors Journal Article
In: ISA Transactions, vol. 106, pp. 367-381, 2020, ISSN: 0019-0578.
Abstract | Links | BibTeX | Tags: Early detection, Load insensitive model, Multi-fault detection, Multi-label classification, Prediction at low operating frequencies, Principal component analysis, SELECTED
@article{Juez-Gil2020,
title = {Early and extremely early multi-label fault diagnosis in induction motors},
author = {Mario Juez-Gil and Juan José Saucedo-Dorantes and Álvar Arnaiz-González and Carlos López-Nozal and César García-Osorio and David Lowe},
url = {https://www.sciencedirect.com/science/article/pii/S0019057820302755},
doi = {https://doi.org/10.1016/j.isatra.2020.07.002},
issn = {0019-0578},
year = {2020},
date = {2020-11-01},
journal = {ISA Transactions},
volume = {106},
pages = {367-381},
abstract = {The detection of faulty machinery and its automated diagnosis is an industrial priority because efficient fault diagnosis implies efficient management of the maintenance times, reduction of energy consumption, reduction in overall costs and, most importantly, the availability of the machinery is ensured. Thus, this paper presents a new intelligent multi-fault diagnosis method based on multiple sensor information for assessing the occurrence of single, combined, and simultaneous faulty conditions in an induction motor. The contribution and novelty of the proposed method include the consideration of different physical magnitudes such as vibrations, stator currents, voltages, and rotational speed as a meaningful source of information of the machine condition. Moreover, for each available physical magnitude, the reduction of the original number of attributes through the Principal Component Analysis leads to retain a reduced number of significant features that allows achieving the final diagnosis outcome by a multi-label classification tree. The effectiveness of the method was validated by using a complete set of experimental data acquired from a laboratory electromechanical system, where a healthy and seven faulty scenarios were assessed. Also, the interpretation of the results do not require any prior expert knowledge and the robustness of this proposal allows its application in industrial applications, since it may deal with different operating conditions such as different loads and operating frequencies. Finally, the performance was evaluated using multi-label measures, which to the best of our knowledge, is an innovative development in the field condition monitoring and fault identification.},
keywords = {Early detection, Load insensitive model, Multi-fault detection, Multi-label classification, Prediction at low operating frequencies, Principal component analysis, SELECTED},
pubstate = {published},
tppubtype = {article}
}
The detection of faulty machinery and its automated diagnosis is an industrial priority because efficient fault diagnosis implies efficient management of the maintenance times, reduction of energy consumption, reduction in overall costs and, most importantly, the availability of the machinery is ensured. Thus, this paper presents a new intelligent multi-fault diagnosis method based on multiple sensor information for assessing the occurrence of single, combined, and simultaneous faulty conditions in an induction motor. The contribution and novelty of the proposed method include the consideration of different physical magnitudes such as vibrations, stator currents, voltages, and rotational speed as a meaningful source of information of the machine condition. Moreover, for each available physical magnitude, the reduction of the original number of attributes through the Principal Component Analysis leads to retain a reduced number of significant features that allows achieving the final diagnosis outcome by a multi-label classification tree. The effectiveness of the method was validated by using a complete set of experimental data acquired from a laboratory electromechanical system, where a healthy and seven faulty scenarios were assessed. Also, the interpretation of the results do not require any prior expert knowledge and the robustness of this proposal allows its application in industrial applications, since it may deal with different operating conditions such as different loads and operating frequencies. Finally, the performance was evaluated using multi-label measures, which to the best of our knowledge, is an innovative development in the field condition monitoring and fault identification.