For a long time ago, induction motor has been used in various industry due to strong construction, high efficiency, and cheap maintenance. Induction motor needs to be maintained regularly so that it can operate for a long time. Based on studied, bearing faults result in failure of 42% -50% of all motor failures. One of the causes of bearing failure is misalignment when installing induction motor. This research proposes classification misalignment in induction using coiflet discrete wavelet transform and Quadratic Discriminant Analysis. Simulation of motor condition is introduced in this research as normal operation and two misalignment variations. And then, various type of coiflet discrete wavelet transform in first level until third level is used to extract motor vibration signal into high frequency signal. Then, three types of signal extraction, namely sum, range and energy level, will be used for input to Linear and Quadratic Discriminant Analysis. Linear and Quadratic Discriminant Analysis will analysis signal extraction and classify them into normal operation and two misalignment condition. The results show that first level of coiflet discrete wavelet transform is the best level for classification misalignment on induction motor, both using the Linear Discriminant Analysis and Quadratic Discriminant Analysis methods. The accuracy obtained from the two methods is the same or almost the same.
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
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