<![CDATA[Induction motors are the most widely used motors because they are sturdy and inexpensive. However, as the induction motor operates, mechanical damage will occur, one of which is bearing damage. Bearing damage can reach 50% compared to other types of damage. The causes include misalignment when installing the motor with a load or vibration when the motor is operating. In this study, the misalignment phenomenon will be classified based on the level of damage. The damage scenario is 1mm and 1.5mm misalignment. Vibration from normal motor and motor misalignment will be taken using a vibration sensor, then the vibration signal will be transformed using Daubechis wavelet transform. The output in the form of a high frequency signal from the Daubechis wavelet transform will be extracted based on the sum, range, and energy of the signal. Then, the performance of the Fuzzy Subspace Clustering method will be known after testing the data. As a comparison whether the Fuzzy Subspace Clustering method can classify induction motor conditions well, it will be compared with the K-Mean method. The results showed that the combination of Fuzzy Subspace Clustering or K-Means and the first-level Daubechis wavelet transform resulted in the best classification with an accuracy of 95.83%.]]>
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