Induction Motor Rotor Fault Diagnosis Research Articles

Induction motor rotor fault diagnosis using three-phase current intersection signal

This paper presents a simple and reliable improved method based on the three-phase current intersection signal (TPCIS), for induction motor rotor fault diagnosis. This method consists of finding time and amplitude of intersection points instead of using the traditional zero-crossing-time technique. TPCIS is constructed by searching and interpolating the positive and negative crossing points of the three-phase currents. After that, a spectral analysis of this signal is performed to extract the rotor fault signature. The proposed method is theoretically introduced and experimentally validated by testing three induction machines under different load conditions. The experimental results show the effectiveness of the proposed method in identifying the examined defect even under low-load conditions.

Advanced Induction Motor Rotor Fault Diagnosis Via Continuous and Discrete Time–Frequency Tools

Transient-based fault diagnosis in induction motors has gained increasing attention over the recent years. This is due to its ability to avoid eventual wrong diagnostics of the conventional motor current signature analysis in certain industrial situations (presence of load toque oscillations, light loading conditions, and so on). However, the application of these transient methodologies requires the use of advanced signal processing tools. This paper presents a detailed comparison between the two main groups of transforms that are employed in transient analysis: discrete and continuous. This paper does not focus on trivial fault cases but on difficult real situations where the application of the conventional methods often leads to false diagnostics (outer bar breakages in double-cage motors, motors with rotor axial duct influence, and combined faults). Indeed, it is the first time that continuous tools are applied to some of these controversial situations. The results in this paper prove the special advantages of the continuous transforms, tearing down some false myths about their use.

Application of Discrete Spectrum Correction Technology for Induction Motor Rotor Fault Diagnosis

The extraction method of induction motor’s fault character based on coordinate transformation could restrain the influence of fundamental component leakage by translating fundamental component into DC component. The amplitude of the fault character frequency component was inaccurate by spectrum analysis taking no account of the leakage of itself. So rotor fault diagnosis method of induction motor based on discrete spectrum correction technology is proposed. This method which did not depend on spectral function of the window function improved the traditional phase difference correction method, and the stator current signal analysis by means of an appropriate window function is performed to reduce the interference between each frequency. The proposed method could detect the existence of fault character frequency component and its frequency and amplitude. The simulation and experimental results indicated that using this method could get better precision of frequency and amplitude of the fault character frequency component.

Winding distribution effects on induction motor rotor fault diagnosis

The sidebands around stator currents harmonics as a potential tool for supporting the diagnosis of rotor faults in induction motors are analyzed in this paper. The presence of broken bars introduces high frequency components in the machine currents spectrum in addition to the characteristic sidebands around the fundamental component. These additional components are due to the interaction between, rotor asymmetry and either the voltage harmonics, or winding distribution, or rotor slots. In particular, the components at frequencies near to fifth and seventh harmonics, produced by the interaction between the rotor faults and the harmonics of the spatial distribution of stator windings, are analyzed in this work. A multiple coupled circuit model of the induction motor is used to evaluate the sensitivity of these components for different stator winding configurations, load level, supply voltage conditions, and different number of broken bars. Simulation results showed that a particular analyzed component near to fifth harmonic depends mainly on fifth harmonic of winding distribution, which remains almost constant for most common distributions. Therefore, it is expected that this component should be found in most motors with broken bars. Finally, experimental laboratory results and two industrial cases that validate the analysis are presented.

A Comparison of Different Techniques for Induction Motor Rotor Fault Diagnosis

The problem of failures in induction motors is a large concern due to its significant influence over industrial production. Therefore a large number of detection techniques were presented to avoid this problem. This paper presents the comparison results of induction motor rotor fault detection using three methods: motor current signature analysis (MCSA), surface vibration (SV), and instantaneous angular speed (IAS). These three measurements were performed under different loads with three rotor conditions: baseline, one rotor bar broken and two rotor bar broken. The faults can be detected and diagnosed based on the amplitude difference of the characteristic frequency components of power spectrum. However IAS may be the best technique because it gives the clearest spectrum representation in which the largest amplitude change is observed due to the faults.

Low Cost Rotor Fault Detection System for Inverter Driven Induction Motor

In this paper, the induction motor rotor fault diagnosis system using current signals, which are measured using axis-transformation method, and speed, which is estimated using current information, are presented. In inverter-fed motor drives unlike line-driven motor drives the stator currents have numerous harmonics components and therefore fault diagnosis using stator currents is very difficult. The current and speed signal for rotor fault diagnosis needs to be precise. Also, high resolution information, which means the diagnosis system, demands additional hardware such as low pass filter, high resolution ADC, encoder and etc. Therefore, the proposed axis-transformation and speed estimation method are expected to contribute to low cost fault diagnosis systems in inverter-fed motor drives without the need for an encoder and any additional hardware. In order to confirm validity of the developed algorithms, various experiments for rotor faults are tested and the line current spectrum of each faulty situation using Park transformation and speed estimation method are compared with the results obtained from fast Fourier transforms.

Induction motor rotor fault diagnosis method based on double PQ transformation

This paper presents a new rotor fault diagnosis method for induction motors which is based on the double PQ transformation. We construct the PQ transformation matrix with the positive sequence fundamental voltage components and their Hilbert transformation as elements. The active power P and the reactive power Q are obtained through the PQ transformation of the stator currents. As both P and Q are constant for a healthy motor, they are represented by a dot on the PQ plane. Whereas the P and Q for a rotor broken bar motor are represented by an ellipse because they comprise an additional frequency component 2sf s (s is the slip and f s is the supply frequency). Thus, by distinguishing these two different patterns, the rotor broken bar fault is detected. We use the major radius of the ellipse as the fault indicator and the distance between the point of no-load condition and the center of the ellipse on the PQ plane as its normalization value. We thus arrive at the fault severity factor which is fairly independent of the load level and the inertia value of the induction motors. Experimental results have demonstrated that the proposed method is effective in identifying the rotor-broken-bars fault and at determining the severity of the fault.