Application of deep neural networks in a mobile application for classifying failures of an induction motor

• Autorzy: Kuroczycki Sebastian , Górny Konrad , Pietrowski Wojciech

Identyfikatory

DOI

10.21008/j.1897-0737.2019.100.0015

• Konferencja: Computer Applications in Electrical Engineering (15-16.04.2019 ; Poznań, Polska)
• Języki publikacji: EN

Abstrakty

EN

The article presents the use of a deep neural network to classify failures of an induction motor. Failures are related to inter-turn short-circuits occurring in the stator circuit. The classification is applied as a mobile application using the Intel Movidius Neural Compute Stick. The state assessment was made on the basis of a database containing the results of continuous wavelet analysis of the torque waveforms of the motor for a different number of shorted turns. Various database configurations for the neural network used in the application have been considered.

Słowa kluczowe

EN

diagnostics of cage induction motor; intel movidius; neural networks; inter-turn short circuits

• Wydawca: Wydawnictwo Politechniki Poznańskiej
• Czasopismo: Poznan University of Technology Academic Journals. Electrical Engineering
• Rocznik: 2019
• Tom: No. 100
• Strony: 167--176
• Opis fizyczny: Bibliogr. 8 poz., rys.

Twórcy

autor

Kuroczycki Sebastian

• Poznan University of Technology

autor

Górny Konrad

• Poznan University of Technology

autor

Pietrowski Wojciech

• Poznan University of Technology

Bibliografia

• [1] Albrecht P.F., Appiarius J.C., McCoy R.M., Owen E.L., Sharma D.K., Assessment of the Reliability of the Motors in Utility Applications – Updated, IEEE Trans. On Energy Conversion, Vol.1, No. 1, pp. 39–46.
• [2] Pandarakone E.S., Mizuno Y., Nakamura H., Online Slight Inter-Turn ShortCircuit Fault Diagnosis Using the Distortion Ration of Load Current in a LowVoltage Induction Motor, IEEJ Journal of Industry Applications, Vol. 7, No. 6, pp. 473–478, doi: 10.1541/ieejjia.7.473.
• [3] Hsu J.S., Monitoring of defects in induction motors through air-gap torque observation, IEEE Trans. On Industrial Applications, Vol. 31, No. 5, pp. 1016–1021.
• [4] Cusido J., Romeral L., Ortega J.A., Rosero J.A., Garvia Espinosa A., Fault Detection in Induction Machines Using Power Spectral Density in Wavelet Decompositions, IEEE Trans. On Industrial Electronics, Vol. 55, No. 2, pp. 663–643.
• [5] Grubic S., Aller J.M., Lu B., Habetler T.G., A survey on testing and monitoring methods for stator insulation systems of low-Voltage induction machines focusing on turn insulations problems, IEEE Trans. on Industrial Electronics, Vol. 55, No. 12, pp. 4127–4136.
• [6] Alexandru M., Analysis of induction motor fault diagnosis with fuzzy neural network, Appl. Artif. Intell. 17 (2003), pp. 105-133.
• [7] Su H., Chong K.T., Kumar R.R., Vibration signal analysis for electrical fault detection of induction machine using neural networks, Neural Comput. Appl. 20 (2007), pp. 183–194.
• [8] Wenjun S., Siyu S., Rui Z., Ruqiang Y., Xingwu Z., Xuefeng C., A sparse autoencoder-based deep neural network approach for induction motor faults classification, doi.org/10.1016/j.measurement.2016.04.007.

Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).