Evaluation and Classification of Double Bar Breakages Through Three-Axes Vibration Sensor in Induction Motors

The relative positions of broken bars can potentially change the current signature content, filter some of the components and hence lead to misleading in diagnostic process. Especially, if the positions of broken bars are in half pole pitch distance, the characteristic sidebands harmonics get lower which leads to decrease diagnostics ability of stator current based analysis. This paper proposes to analyze double bar breakages faults by monitoring the three-axes vibration (-x, -y, and -z) signals through an accelerometer in detail. The characteristics fault signatures are presented in axial -x, radial -y and gravity -z axis vibrations and a neural network-based classifier (Multi-Layer Perceptron) is utilized to classify the type of double bar breakages. The findings are verified through the experiments. It is shown that some of characteristics fault signatures such as 2sf(s), 3sf(s) and 2f(r)+/- 2f(s) at corresponding vibration spectra can provide more reliable result to detect and classify the broken bar fault in induction motors (IMs)

Reliable Detection of Broken Bar Fault through Negative Sequence of Stray Flux in Induction Motors

As well-known, the broken bar fault affects the harmonic content of the stator current and airgap &amp; stray flux. This paper proposes to detect broken bar fault through negative sequence stray flux spectrum in induction motors (IMs). For this purpose, three stray flux data which have 120o relative phase differences are collected from the vicinity of IMs. Then, stray flux space vector is calculated and analyzed at low frequency range when the motor is operated as both healthy and broken bar&nbsp; cases. It is shown that there are some effective signals such as –((fs-fr)±2sfs) to detect broken bar fault in negative sequence stray flux spectrum while only sidebands of fundamental signatures rise up in positive sequence stray flux spectrum. Comparative 2D finite-element simulations results including current space vector prove that the negative sequence stray flux spectrum can yields more effective results than classical stator current and stray flux analysis in induction motors.&nbsp;</p

Discerning broken rotor bar failure from low-frequency load torque oscillation in DTC induction motor drives

This paper proposes a method for separation of broken rotor bar failures from low-frequency load torque oscillation in direct torque control (DTC) induction motor drives by using v(q) voltage and i(q) current components' spectra. The effect of load torque oscillation should be considered in induction motor drives for reliable broken bar fault detection. Induction machine drivers are run in DTC mode to control its torque and speed. In practice, the presence of load torque fluctuation may sometimes cause false positive alarms on stator current spectrum. However, discerning of broken rotor bar failure from low-frequency load variation for DTC drives remains unexplored. Experimental results show that by using the proposed method broken rotor bar failure can be reliably detected in the presence of low-frequency load torque oscillation in DTC induction motor drives

Detection of Rotor Bar Fault through Stray Flux Based Analytical Signal Angular Fluctuation Method

Stray flux analysis has increasing trend to monitor the machine condition&nbsp; including fault diagnosis. In this paper, the rotor bar fault is detected through the Stray Flux based Analytical Signal Angular Fluctuation (SF-ASAF) method in&nbsp; induction motors (IMs). For this purpose, the stray flux is collected from the vicinity of motor frame and analytical signal of stray flux is calculated using Hilbert Transform (HT). Then, angular fluctuation of obtained signal is utilized to see the harmonic content of stray flux-analytical signal. It is shown that there are some effective signals such as fs-3sfs, 2sfs to detect rotor bar fault in stray flux based analytical signal angular fluctuation spectrum. The presented 2D- FEM based simulation and experimental results prove that the Stray Flux based&nbsp; Analytical Signal Angular Fluctuation (SF-ASAF) method can provide superior and reliable results rather than classical stray flux analysis in induction&nbsp; motors.</p

Discriminating of Rotor Fault and Low Frequency Load Torque Oscillation Using Motor Square Current Signature Analysis

This paper proposes a method to discern broken bar rotor fault from low frequency load torque oscillation in induction motors. If a motor is subjected to load fluctuation, the sideband components show up in phase current and they can exhibit similar behavior that of broken bar which leads misleading diagnostics. Thus, broken rotor bar and load oscillation related harmonics may sometimes overlap in the same spot. In this study, Motor Square Current Signature Analysis (MSCSA) method is used to detect broken rotor bar fault when load torque oscillation frequency overlaps with that of broken bar fault. This method is quite simple, but effective for false positive indication. The 2D-FEM simulations and experiments are carried out to prove the efficacy of proposed method. Based on the presented results, it is shown that broken bar related signatures such as 4f(s)-2ksf(s) do exist, whereas there is no load oscillation related signatures at the sidebands of 4f(s) in the square of phase current spectrum