—Induction motors are the most widely ysed type of motor in industry. Condition monitoring is crucial for the machine health. Whole system can be out of service when the motors are out of order. The correct diagnosis of beginning of the faults is very important for the system in which electricity motors are used in. Most of the motor failures occur due to the bearing faults. The reason of the fault is recognized by obtained information regarding to the signals from motors. The currently used Fourier transformation techniques fail to provide insight to the frequency analysis so it leads to apply time-frequency analysis including wavelet transform in the important field of signal analysis. Using only time domain or frequency domain restrict the analysis result. Especially, application of wavelet transformation to the non-stationary signals for getting frequency and time information at the same time gives excellent results. Discrete wavelet transformation that is a multi resolution algorithm analyses the signal by separating the signal into frequency components. The three phase motor currents and vibration data are obtained using four poles induction motor with size of 1 Hp. Then, the whole data set regarding to the healthy and faulty conditions are saved in a computer by digitizing using a data acquisition card. The sampled signals from experimental system are analyzed by using discrete wavelet transformation and motor bearing faults have been diagnosed. Ball and inner race faults are recognized by analyzing current signal information. Besides, the outer race fault has been found at the beginning stage.
—Bearing faults, induction motor, motor faults, Wavelet transformation, condition monitoring.
Hakan Çalış is with the Suleyman Demirel University, Department of Electrical-Electronics Engineering, Isparta, Turkey (e-mail: firstname.lastname@example.org).
Hüseyin Fidan is with the Mehmet Akif Ersoy University, Bucak Emin Gulmez Technical Sciences and Vocational School, Department of Computer Technology and Progr. Education, Burdur, Turkey (e-mail: email@example.com).
Cite: Hakan Çalış and Hüseyin Fidan, "Motor Condition Monitoring Based on Time-Frequency Analysis of Stator Current Signal," International Journal of Modeling and Optimization vol. 5, no. 1, pp. 36-39, 2015.