Comprehensive Diagnostics of Induction Motor Faults Based on Measurement of Space and Time Dependencies of Air Gap Flux

High-efficiency motors are being gradually introduced in many industrial applications because of their positive impacts on the environment by reducing energy consumption and CO 2 emission. In this respect, line start permanent magnet synchronous motors (LS-PMSMs) have been introduced recently. Due to their unique configuration, LS-PMSMs allow the obtaining of super premium efficiency levels, accompanied with a high torque and power factor. However, since the use of LS-PMSMs in the industry is in its infancy, no efficient scheme has yet been reported for broken rotor bar (BRB) fault detection in this type of motor. Accordingly, the main aim of this research is to investigate the fault-related feature for BRB faults on LS-PMSMs. In this regard, a simulation model and experimental setup for the investigation of BRB in LS-PMSM are implemented. The detection strategy for BRB in LS-PMSM proposed here is based on the monitoring of the start-up current signal and discrete wavelet transform. The entropy features are used as fault-related features for BRB faults. Finally, the ability of these features is validated for the detection of BRB in LS-PMSM through statistical analysis. In this research, the importance of the starting load is also considered for BRB detection in LS-PMSMs.

show abstract

“…Entropy " log −energy" X ELog energy = ∑ i log(X 2 n ) (9) with the convention log(0) = 0. where, X n is the reconstructed signal from level n.…”

Section: Background and Methodologymentioning

confidence: 99%

“…[6][7][8][9][10][11][12][13][14][15][16][17][18][19].53] Na A Na A Approximation (7) [19.53-0] Na Na Na Na A: Applicable; Na: Not applicable.…”

Section: Log-energy Entropymentioning

confidence: 99%

Broken Rotor Bar Detection in LS-PMSM Based on Startup Current Analysis Using Wavelet Entropy Features

et al. 2017

View full text Add to dashboard Cite

show abstract

“…Other works have demonstrated the possibility to detect broken rotor bars using this kind of signal and a Hall sensor installed between two stator slots of the machine [19,38,39,44]. In [45], an array of Hall effect sensors was installed around the stator circumference inside the motor air gap to detect rotor bar damage and stator turn-to-turn shorts. In this case, 36 measurements points have been used for fault detection.…”

Section: Air Gap Disturbances Monitoring Using a Hall Effect Sensormentioning

confidence: 99%

“…In this case, 36 measurements points have been used for fault detection. However, the solution proposed by [45] requires a lots of measurement points and the other Hall sensor based solutions did not take into account the motor fed by an inverter or driven by oscillating loads. In this work, the Hall sensor was installed close to the air gap.…”

Section: Air Gap Disturbances Monitoring Using a Hall Effect Sensormentioning

confidence: 99%

Fuzzy-Based Statistical Feature Extraction for Detecting Broken Rotor Bars in Line-Fed and Inverter-Fed Induction Motors

2019

View full text Add to dashboard Cite

This paper presents the use of a fuzzy-based statistical feature extraction from the air gap disturbances for diagnosing broken rotor bars in large induction motors fed by line or an inverter. The method is based on the analysis of the magnetic flux density variation in a Hall Effect Sensor, installed between two stator slots of the motor. The proposed method combines a fuzzy inference system and a support vector machine technique for time-domain assessment of the magnetic flux density, in order to detect a single fault or multiple broken bars in the rotor. In this approach, it is possible to detect not only the existence of failures, but also its severity. Moreover, it is not necessary to estimate the slip of the motor, usually required by other methods and the damaged rotor detection was also evaluated for oscillating load conditions. Thus, the present approach can overcome some drawbacks of the traditional MCSA method, particularly in operational cases where false positive and false negative indications are more frequently. The efficiency of this approach has been proven using some computational simulation results and experimental tests to detect fully broken rotor bars in a 7.5 kW squirrel cage induction machine fed by line and an inverter.

show abstract

“…Most of the significant methods and related techniques are based on monitoring one or more signals and the use of advanced signal processing methods. An overview of these methods is presented in detail in [18][19][20][21][22][23][24][25][26][27], where the ITSCs are detected in the stator phase current [18], in the phase shift between phase currents, phase shift between current and stator voltage [14,19], axial flux [2,20], instantaneous active and reactive power [21] or mechanical vibrations [22]. The most popular signal processing methods are based on motor current signature analysis (MCSA) [1,2,7], symmetrical components analysis (SCA) [23][24][25][26][27][28][29] and the stator current Park's vector approach analysis [29,30].…”

Section: Introductionmentioning

confidence: 99%

Application of Self-Organizing Neural Networks to Electrical Fault Classification in Induction Motors

et al. 2019

View full text Add to dashboard Cite

Electrical winding faults, namely stator short-circuits and rotor bar damage, in total constitute around 50% of all faults of induction motors (IMs) applied in variable speed drives (VSD). In particular, the short circuits of stator windings are recognized as one of the most difficult failures to detect because their detection makes sense only at the initial stage of the damage. Well-known symptoms of stator and rotor winding failures can be visible in the stator current spectra; however, the detection and classification of motor windings faults usually require the knowledge of human experts. Nowadays, artificial intelligence methods are also used in fault recognition. This paper presents the results of experimental research on the application of the stator current symptoms of the converter-fed induction motor drive to electrical fault detection and classification using Kohonen neural networks. The experimental tests of a diagnostic setup based on a virtual measurement and data pre-processing system, designed in LabView, are described. It has been shown that the developed neural detectors and classifiers based on self-organizing Kohonen maps, trained with the instantaneous symmetrical components of the stator current spectra (ISCA), enable automatic distinguishing between the stator and rotor winding faults for supplying various voltage frequencies and load torque values.

show abstract

Comprehensive Diagnostics of Induction Motor Faults Based on Measurement of Space and Time Dependencies of Air Gap Flux

Cited by 63 publications

References 32 publications

Broken Rotor Bar Detection in LS-PMSM Based on Startup Current Analysis Using Wavelet Entropy Features

Broken Rotor Bar Detection in LS-PMSM Based on Startup Current Analysis Using Wavelet Entropy Features

Fuzzy-Based Statistical Feature Extraction for Detecting Broken Rotor Bars in Line-Fed and Inverter-Fed Induction Motors

Application of Self-Organizing Neural Networks to Electrical Fault Classification in Induction Motors

Contact Info

Product

Resources

About