Pendeteksian Misalignment Menggunakan Multi Level Transformasi Wavelet Haar dan Coiflet pada Motor Induksi
DOI:
https://doi.org/10.48056/jeetech.v1i1.1Keywords:
Transformasi Wavelet, Coiflet, Ekstraksi Ciri, MisalignmetAbstract
Currently induction motors are widely used in industry due to strong construction, high efficiency, and cheap maintenance. Machine maintenance is needed to prolong the life of the induction motor. As studied, bearing faults may account for 42% -50% of all motor failures. In general it is due to manufacturing faults, lack of lubrication, and installation errors. Misalignment of motor is one of the installation errors. This paper is concerned to simulation of discrete wavelet transform for identifying misalignment in induction motor. Modelling of motor operation is introduced in this paper as normal operation and two variations of misalignment. For this task, haar and coiflet discrete wavelet transform in first level until fifth level is used to extract vibration signal of motor into high frequency of signal. Then, energy signal and other signal extraction gotten from high frequency signal is evaluated to analysis condition of motor. The results show that haar discrete wavelet transform at thirth level can identify normal motor and misalignment motor conditions well
References
Harmouche, J., Delpha, C., & Diallo, D. (2015). Improved fault diagnosis of ball bearings based on the global spectrum of vibration signals. IEEE Transactions on Energy Conversion, 30(1), 376–383. https://doi.org/10.1109/TEC.2014.2341620
O. V. Thorsen and M. Dalva, “Failure identification and analysis forhigh voltage induction motors in the petrochemical industry,” IEEETransactions on Industry Applications, vol. 35, no. 4, pp. 810–818, 1999.
S. Barker, “Avoiding premature bearing failure with inverter fed induction motors,” Power Engineering Journal, vol. 14, no. 4, pp. 182–189,2000.
EPRI, “Improved motors for utility applications,” Publication EL-2678-V1, final report, 1982.
D. A. Asfani, P. P. Surya Saputra, I. M. Yulistya Negara, I. G. N. Satriyadi Hernanda and R. Wahyudi, "Simulation analysis on high impedance temporary short circuit in induction motor winding," 2013 International Conference on QiR, Yogyakarta, 2013, pp. 202-207. doi: 10.1109/QiR.2013.6632565
A. Starr B.K.N. Rao. Condition Monitoring and Diagnostic Engineering Management. Proceedengs of the 14th Intrnational Congress. Elsevier, 2001.
Anton Asfani, Dimas &Yulistya Negara, I Made & Surya, Pressa. (2015). Short Circuit Detection in Stator Winding Of Three Phase Induction Motor Using Wavelet Transform and Quadratic Discriminant Analysis. 361-366. 10.12792/icisip2015.068.
C. Jettanasen, A. Ngaopitakkul, D. A. Asfani and I. M. Y. Negara, "Fault classification in transformer using low frequency component," 2017 IEEE 10th International Workshop on Computational Intelligence and Applications (IWCIA), Hiroshima, 2017, pp. 199-202. doi: 10.1109/IWCIA.2017.8203584
Asfani, Dimas & ,Syafaruddin & HeryPurnomo, Mauridhi & Hiyama, Takashi. (2014). Neural network based real time detection of temporary short circuit fault on induction motor winding through wavelet transformation. International journal of innovative computing, information & control: IJICIC. 10. 1-14.