The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Unsupervised Neural-Network-Based Algorithm for an On-Line Diagnosis of Three-Phase Induction Motor Stator Fault
Abstract
In this paper, an automatic algorithm based an unsupervised neural network for an on-line diagnostics of three-phase induction motor stator fault is presented. This algorithm uses the alfa-beta stator currents as input variables. Then, a fully automatic unsupervised method is applied in which a Hebbian-based unsupervised neural network is used to extract the principal components of the stator current data. These main directions are used to decide where the fault occurs and a relationship between the current components is calculated to verify the severity of the fault. One of the characteristics of this method, given its unsupervised nature, is that it does not need a prior identification of the system. The proposed methodology has been experimentally tested on a 1 kW induction motor. The obtained experimental results show the effectiveness of the proposed method.
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Cites
Simplified neuron model as a principal component analyzer
Oja, E. (1982) · Journal of Mathematical Biology · doi:10.1007/bf00275687 · Cited by 2,309
Cited by
Advances in Diagnostic Techniques for Induction Machines
Bellini, A., Filippetti, F., Tassoni, C., & Capolino, G.-A. (2008) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2008.2007527 · Cited by 1,140
From Model, Signal to Knowledge: A Data-Driven Perspective of Fault Detection and Diagnosis
Dai, X., & Gao, Z. (2013) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2013.2243743 · Cited by 728
A Survey of Condition Monitoring and Protection Methods for Medium-Voltage Induction Motors
Zhang, P., Du, Y., Habetler, T. G., & Lu, B. (2011) · IEEE Transactions on Industry Applications · doi:10.1109/tia.2010.2090839 · Cited by 706
Improved Computation for Levenberg–Marquardt Training
Wilamowski, B. M., & Yu, H. (2010) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2010.2045657 · Cited by 622
Grubic, S., Aller, J. M., Lu, B., & Habetler, T. G. (2008) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2008.2004665 · Cited by 490
Metadata
- Authors
- Martins, J. F., Pires, V. F., & Pires, A. J.
- Venue
- IEEE Transactions on Industrial Electronics
- Published
- 2007-02
- Volume
- 54
- Issue
- 1
- Pages
- 259-264
- ISSN
- 0278-0046 (ISSN-L)
- 1557-9948
- OpenAlex
- W2112312998
- Cited by
- 219
- References
- 1
- FWCI
- 35.96
- Type
- journal article
- Language
- en
- Open access
- closed
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- Martins, J. F., Pires, V. F., & Pires, A. J. (2007). Unsupervised Neural-Network-Based Algorithm for an On-Line Diagnosis of Three-Phase Induction Motor Stator Fault. IEEE Transactions on Industrial Electronics. https://doi.org/10.1109/tie.2006.888790
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