Motor Current Signature Analysis Technique Research Articles

Fault Detection of Rotor Bars in Inverter-Fed Induction Motors Based on Current Signature Analysis Technique

Induction motors, which are one key piece of equipment for power plants, waterworks facilities, and factories, must be maintained appropriately for reliable operation. A motor current signature analysis (MCSA) technique, which monitors and detects problems in motors and diagnoses devices, has already been marketed by some companies. Recently, applications of inverter-fed motors have increased for greater energy conservation. However, a fault-detection method in inverter-fed motors has been inadequately studied despite the risk of misdetection due to inverter noise. This paper shows our results that detected a broken rotor bar in an inverter-fed motor based on a MCSA technique. An abnormal motor with a broken rotor bar and a normal motor are driven by an inverter. The current supplied to both motors is measured and the frequency spectra results are compared. In the measurements, the inverter’s drive frequency is varied from 120 to 10 Hz in 10 Hz increments. In each drive frequency, the slip is varied in a range of 0-5% by adjusting the load connected to the motor. The results of comparing the current spectra show significant reinforcement of the signal intensities in abnormal motors. Some signals are reinforced by both inverter noise and a component that originated in the broken bar. The superposition that may lead to a misdiagnosis in inverter-fed motors is avoided by identifying the normal spectrum shapes of the target equipment in advance.

Detection and evaluation of load oscillations in induction motors based on MCSA

Load oscillations of induction motors can be the consequence of electrical or mechanical faults. One of the most important techniques to diagnose load oscillations is the so-called motor current signature analysis (MCSA). It has won widespread popularity owing to its non-intrusive and cost-effective performance. The common practice of MCSA is to detect the characteristic harmonic components of stator current caused by load fluctuations. However, in certain scenarios, the characteristic components can be hardly tracked from the current spectrum. Power frequency interference, nonstationary randomness of the sampled signals, and other factors could all limit the use of classical current spectrum analysis. The presented solution is the joint application of variational mode decomposition (VMD), delay addition method, multiple signal classification (MUSIC), and neural network. Specifically, VMD, delay addition and MUSIC are combined to decompose the raw signals, eliminate the power frequency leakage interference and extract the weak oscillation features with enhanced visualization. Meanwhile, a neural network is generated to describe the statistical features of the processed signals, and further realize the automatic evaluation of the load oscillation severity level. Theoretical analysis and experimental verification have been conducted to support this article. The results suggest that the proposed algorithm contributes to a more sensitive detection of the presence of load oscillations, and a more reliable evaluation of their severity with respect to traditional MCSA techniques.

Density of Maxima Approach for Broken Bar Fault Diagnosis in Low Slip and Variable Load Conditions of Induction Motors

The three-phase induction motor (TIM) is present in most industrial processes, being the main responsible for the electromechanical conversion in the sector. The total breakdown of these machines can compromise the production of entire sectors in a manufacturing environment, and hence, the need for early fault detection. In this context, broken bar failures are the most common on the TIM rotor. This article analyzes the chaotic behavior of one stator current of a TIM, with different levels of broken rotor bar failures. The characterization of the failure is performed by analyzing the behavior of the chaotic variable called density of maxima. The proposed approach has advantages over classic motor current signature analysis techniques in that it does not require long acquisition windows (in this work, it was possible to perform detection using a range of 0.35 s) and enables fault detection even for motors operating under variable load or with low slip. The technique also has a lower computational cost compared with classical detection methods. The obtained results indicate that the method is also able to identify different severity levels with the TIM under nominal load.

Induction motor stator current analysis for planetary gearbox fault diagnosis under time-varying speed conditions

Vibration-based planetary gearbox fault diagnosis under time-varying speed conditions is challenging. This work exploits the advantages of motor stator current signal in easier accessibility and simpler frequency modulation structure, and applies the motor current signature analysis technique on planetary gearbox fault diagnosis. Firstly, the induction motor stator current signal with both planetary gearbox fault and airgap eccentricity under time-varying speed conditions is analytically modeled, and properties of time-varying planetary gearbox fault signatures are summarized. Then, to address the difficulties of revealing and presenting relatively weak and time-varying fault signatures in motor stator current signal, the adaptive iterative generalized demodulation is utilized. The Fourier transform surrogate test is integrated with iterative generalized demodulation algorithm to solve the problem of weak fault signature extraction, and Hilbert spectra of validated mono-components finally compose the time-frequency representation of analyzed motor stator current signal. Both numerical simulation as well as lab experimental evaluations in different gear fault cases, have been conducted to verify the correctness of the derived time-varying gear fault signatures, and the effectiveness of accurate planetary gearbox fault diagnosis.

Support vector machine classifier for diagnosis in electrical machines: Application to broken bar

This paper presents a support vector machine classifier for broken bar detection in electrical induction machine. It is a reliable online method, which has high robustness to load variations and changing operating conditions. The phase current is only physical value to be measured. The steady state current is analyzed for broken bar fault via motor current signature analysis technique based on Hilbert transform. A two dimensional feature space is proposed. The features are: magnitude and frequency of characteristic peak extracted from spectrum of Hilbert transform series of the phase current. For classification task support vector machine is used due to its good robustness and generalization performances. A comparative analysis of linear, Gaussian and quadratic kernel function versus error rate and number of support vectors is done. The proposed classifier successfully detects a broken bar in various operational situations. The proposed method is sufficiently accurate, fast, and robust to load changes, which makes it suitable for use in real-time online applications in industrial drives.

Automated Detection of Rotor Faults for Inverter-Fed Induction Machines Under Standstill Conditions

It is difficult to apply conventional online motor current signature analysis techniques for diagnosis of rotor faults for closed-loop induction motor drives for many applications due to the masking effect of the feedback current controller and/or variable frequency or load operation. Relying solely on traditional offline inspection techniques during regular maintenance does not allow frequent monitoring of rotor problems and is inconvenient since it requires rotor disassembly and/or manual rotor rotation. In this paper, a new automated technique for testing voltage source inverter-fed squirrel-cage induction machines at a standstill for rotor faults is proposed. The main concept is to use the inverter to excite the machine with a pulsating field at a number of angular positions to observe the variation in the impedance pattern due to broken rotor bars, whenever the motor is stopped. An experimental study on a 7.5-hp induction motor verifies that broken bars can be detected with high sensitivity and reliability. It will be shown that the proposed method can provide automated and reliable assessment of rotor condition frequently without motor disassembly, manual rotation, or additional instrumentation. The proposed test can also provide rotor quality assessment independent of variations in motor or load operating conditions since it is a standstill test.

Instantaneous power as a medium for the signature analysis of induction motors

Preventive maintenance of electric drive systems with induction motors involves monitoring of their operation for detection of abnormal electrical and mechanical conditions that indicate, or may lead to, a failure of the system. Intensive research effort has been for sometime focused on the motor current signature analysis (MCSA). The MCSA techniques utilize the results of spectral analysis of the stator current. Reliable interpretation of the spectra is difficult, since distortions of the current waveform caused by the abnormalities in the drive system are usually minute. In this paper, an alternate medium for the motor signature analysis, namely the instantaneous power, is proposed. By theoretical analysis, computer simulations, and laboratory experiments, it is shown that the instantaneous power carries more information than the current itself. Utilization of the instantaneous power is thus enhancing the reliability of diagnostics of induction motor drives.