RT info:eu-repo/semantics/article
T1 Robust detection of incipient faults in VSI-fed induction motors using quality control charts.
A1 García Escudero, Luis Ángel
A1 Duque Pérez, Óscar
A1 Fernández Temprano, Miguel Alejandro
A1 Moríñigo Sotelo, Daniel
K1 Statistics
K1 Fault detection
K1 Motors
K1 Diagnostic expert systems induction motors
K1 maintenance
K1 monitoring
K1 quality control
AB A considerable amount of papers have been published in recent years proposing supervised classifiers to diagnose the health of a machine. The usual procedure with these classifiers is to train them using data acquired through controlled experiments, expecting them to perform well on new data, classifying correctly the condition of a motor. But, obviously, the new motor to be diagnosed cannot be the same that has been used during the training process; it may be a motor with different characteristics and fed from a completely different source. These different conditions between the training process and the testing one can deeply influence the diagnosis. To avoid these drawbacks, in this paper a new method is proposed which is based on robust statistical techniques applied in Quality Control applications. The proposed method is based on the online diagnosis of the operating motor and can detect deviations from the normal operational conditions. A robust approach has been implemented using high-breakdown statistical techniques which can reliably detect anomalous data that often cause an unexpected overestimation of the data variability, reducing the ability of standard procedures to detect faulty conditions in earlier stages. A case study is presented to prove the validity of the proposed approach. Motors of different characteristics, fed from the power line and several different inverters, are tested. Three different fault conditions are provoked, broken bar, a faulty bearing and mixed eccentricity. Experimental results prove that the proposed approach can detect incipient faults.
SN 0093-9994
YR 2016
FD 2016
LK http://uvadoc.uva.es/handle/10324/21441
UL http://uvadoc.uva.es/handle/10324/21441
LA eng
NO IEEE Transactions on Industry Applications,  2017, vol. 53, n. 3, p. 3076-3085.
DS UVaDOC
RD 04-abr-2025