Abstract
Reliable induction motor (IM) fault detection techniques are very useful in industries to diagnose IM defects and improve operational performance. An adaptive empirical mode decomposition (EMD) technology is proposed in this paper for rotor bar fault detection in IMs. As the characteristic fault frequency will change with operating conditions related to load and speed, the proposed adaptive EMD technique correlates fault features over different frequency bands and intrinsic mode function (IMF) sidebands. The adaptive EMD technique uses the first IMF to detect the fault type and the second IMF as an indicator to predict the fault severity. It can overcome the problems of the sensitivity of sideband frequencies related to the speed and load oscillations. The effectiveness of the proposed adaptive EMD technique is verified by experimental tests under different motor conditions.
Highlights
Induction motors (IMs) are commonly used in various industrial applications such as electric vehicles, machine tools and robots; in addition, induction motor (IM) account for about 40% of the annual global electricity consumption [1]
A comparison of Figure 4(a) and Figure 4(b) indicates that broken rotor bar (BRB) can increase the oscillation of the IMF2 amplitude
An adaptive empirical mode decomposition (EMD) technique is proposed in this work for motor BRB fault detection, using current signals
Summary
Induction motors (IMs) are commonly used in various industrial applications such as electric vehicles, machine tools and robots; in addition, IMs account for about 40% of the annual global electricity consumption [1]. IM defects are related to imperfections in stators, bearings, and rotors [2]. This work focuses on broken rotor bar (BRB) faults. The causes of rotor bar faults include excessive dynamic load. The consequences of BRB defect are increased vibration and noise, deterioration of the motor output efficiency, and early IM failure [4]. Since BRB faults will modulate the stator current signal, motor current signature analysis (MCSA) is more suitable in this fault detection than using vibration analysis. IM fault detection in this work will be based on MCSA
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