RT info:eu-repo/semantics/article
T1 Early and extremely early multi-label fault diagnosis in induction motors
A1 Juez Gil, Mario
A1 Saucedo Dorantes, Juan José
A1 Arnaiz González, Álvar
A1 López Nozal, Carlos
A1 García Osorio, César
A1 Lowe, David
K1 Multi-fault detection
K1 Early detection
K1 Multi-label classification
K1 Principal component analysis
K1 Load insensitive model
K1 Prediction at low operating frequencies
K1 Informática
K1 Computer science
AB 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.
PB Elsevier
SN 0019-0578
YR 2020
FD 2020-11
LK http://hdl.handle.net/10259/5575
UL http://hdl.handle.net/10259/5575
LA eng
NO project TIN2015-67534-P (MINECO, Spain/FEDER, UE) of the Ministerio de Economía y Competitividad of the Spanish Government, project BU085P17 (JCyL/FEDER, UE) of the Consejería de Educación of the Junta de Castilla y León, Spain (both projects co-financed through European Union FEDER funds), and by the pre-doctoral grant (EDU/1100/2017), also of the Consejería de Educación of the Junta de Castilla y León, Spain and the European Social Fund.
DS Repositorio Institucional de la Universidad de Burgos
RD 18-abr-2024