Data-driven fault detection of open circuits in multi-phase inverters based on current polarity using Auto-adaptive and Dynamical Clustering. Pham, T., Lefteriu, S., Duviella, E., & Lecoeuche, S. ISA Transactions, 113:185–195, July, 2021. ![Data-driven fault detection of open circuits in multi-phase inverters based on current polarity using Auto-adaptive and Dynamical Clustering [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex This paper proposes a data-driven method for the detection and isolation of open-circuit faults in multi-phase inverters using measurements of the motor currents. First, feature variables are formulated in terms of the averages of the phase currents and their absolute values. Next, by using an AUto-adaptive and Dynamical Clustering (AUDyC) based on Gaussian Mixture Models, feature data is clustered into different classes characterizing normal and faulty operation modes. Afterwards, these classes are used for deriving appropriate conditions for detecting and labelling faults. The proposed method requires minimal knowledge about the system operation. Furthermore, it allows us to update our knowledge of existing faults online, thus making it possible to detect unknown faults. Moreover, conditions are formulated to describe the influence of the method parameters on the detection time. Once parameters are tuned, the accuracy of the proposed method is illustrated on various experimental data sets, where single and double faults are detected with detection times in the order of the fundamental signal period.
@article{pham_data-driven_2021,
title = {Data-driven fault detection of open circuits in multi-phase inverters based on current polarity using {Auto}-adaptive and {Dynamical} {Clustering}},
volume = {113},
issn = {0019-0578},
url = {https://www.sciencedirect.com/science/article/pii/S0019057820302561},
doi = {10.1016/j.isatra.2020.06.009},
abstract = {This paper proposes a data-driven method for the detection and isolation of open-circuit faults in multi-phase inverters using measurements of the motor currents. First, feature variables are formulated in terms of the averages of the phase currents and their absolute values. Next, by using an AUto-adaptive and Dynamical Clustering (AUDyC) based on Gaussian Mixture Models, feature data is clustered into different classes characterizing normal and faulty operation modes. Afterwards, these classes are used for deriving appropriate conditions for detecting and labelling faults. The proposed method requires minimal knowledge about the system operation. Furthermore, it allows us to update our knowledge of existing faults online, thus making it possible to detect unknown faults. Moreover, conditions are formulated to describe the influence of the method parameters on the detection time. Once parameters are tuned, the accuracy of the proposed method is illustrated on various experimental data sets, where single and double faults are detected with detection times in the order of the fundamental signal period.},
language = {en},
urldate = {2022-01-14},
journal = {ISA Transactions},
author = {Pham, Thanh-Hung and Lefteriu, Sanda and Duviella, Eric and Lecoeuche, Stéphane},
month = jul,
year = {2021},
pages = {185--195},
}
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