Abstract
In this study, the datamining application was achieved for fault detection and diagnosis of induction motors based on wavelet transform and classification models with current signals. Energy values were calculated from transformed signals by wavelet and distribution of the energy values for each detail was used in comparing similarity. The appropriate details could be selected by the fuzzy similarity measure. Through the similarity measure, features of faults could be extracted for fault detection and diagnosis. For fault diagnosis, neural network models were applied, because in this study, it was considered which details are suitable for fault detection and diagnosis.
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References
Vas, P.: Parameter Estimation, Condition Monitoring, and Diagnosis of Electrical Machines. Clarendron Press, Oxford (1993)
Kliman, G.B., Stein, J.: Induction motor fault detection via passive current monitoring. In: International Conference in Electrical Machines, Cambridge, MA, pp. 13–17 (1990)
Abbaszadeh, K., Milimonfared, J., Haji, M., Toliyat, H.A.: Broken Bar Detection in Induction Motor via Wavelet Transformation. In: The 27th Annual Conference of the IEEE Industrial Electronics Society (IECON 2001), vol. 1, pp. 95–99 (2001)
Ye, Z., Wu, B., Sadeghian, A.: Current signature analysis of induction motor mechanical faults by wavelet packet decomposition. IEEE Transactions on Industrial Electronics 50(6), 1217–1228 (2003)
Eren, L., Devaney, M.J.: Bearing Damage Detection via Wavelet Packet Decomposition of the Stator Current. IEEE Transactions on Instrumentation and Measurement 53(2), 431–436 (2004)
Zhongming, Y.E., Bin, W.U.: A Review on Induction Motor Online Fault Diagnosis. In: The Third International Power Electronics and Motion Control Conference (PIEMC 2000), vol. 3, pp. 1353–1358 (2000)
Haji, M., Toliyat, H.A.: Patern Recognition-A Technique for Induction Machines Rotor Fault Detection Eccentricity and Broken Bar Fault. In: Conference Record of the 2001 IEEE Industry Applications Conference, vol. 3, pp. 1572–1578 (2001)
Chan, K.P., Fu, A.W.C.: Efficient time series matching by wavelets. In: 15th International Conference on Data Engineering, pp. 126–133 (1999)
Keogh, E., Kasetty, S.: On the Need for Time Series Datamining Benchmarks: A Survey and Empirical Demonstration. In: The 8th ACM SIGKDD International Conference on Knowledge Discovery and Datamining, pp. 102–111 (2002)
Pappis, C.P., Karacapilidis, N.I.: A comparative assessment of measures of similarity of fuzzy values. Fuzzy Sets and Systems 56, 171–174 (1993)
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© 2005 Springer-Verlag Berlin Heidelberg
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Bae, H., Kim, S., Kim, JM., Kim, KB. (2005). Development of Flexible and Adaptable Fault Detection and Diagnosis Algorithm for Induction Motors Based on Self-organization of Feature Extraction. In: Furbach, U. (eds) KI 2005: Advances in Artificial Intelligence. KI 2005. Lecture Notes in Computer Science(), vol 3698. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11551263_12
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DOI: https://doi.org/10.1007/11551263_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28761-2
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