New vision about the overlap frequencies in the MCSA-FFT technique to diagnose the eccentricity fault in the induction motors

Bessous, Noureddine; Zouzou, S. E.; Sbaa, Salim; Khelil, Abdellatif

doi:10.1109/icee-b.2017.8192034

Cited by 15 publications

(10 citation statements)

References 15 publications

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“…When performing the condition monitoring, this method normalizes the signal according to the amplitude of the first harmonic after performing the FFT, which reduces the effect of motor load conditions. The FFT algorithm is strongly dependent on small frequency changes in the processing of vibration signals, as in case of larger changes, the FFT brings the error to the actual value of the signal [16,17,20,83,93]. This mathematical apparatus is also the basis for condition monitoring methods from the IFSA (Instantaneous Frequency Signature Analysis) group.…”

Section: Fast Fourier Transformmentioning

confidence: 99%

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Advanced Signal Processing Methods for Condition Monitoring

Jaroš

Byrtus

Dohnal

et al. 2022

Arch Computat Methods Eng

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Condition monitoring of induction motors (IM) among with the predictive maintenance concept are currently among the most promising research topics of manufacturing industry. Production efficiency is an important parameter of every manufacturing plant since it directly influences the final price of products. This research article presents a comprehensive overview of conditional monitoring techniques, along with classification techniques and advanced signal processing techniques. Compared methods are either based on measurement of electrical quantities or nonelectrical quantities that are processed by advanced signal processing techniques. This article briefly compares individual techniques and summarize results achieved by different research teams. Our own testbed is briefly introduced in the discussion section along with plans for future dataset creation. According to the comparison, Wavelet Transform (WT) along with Empirical Mode Decomposition (EMD), Principal Component Analysis (PCA) and Park’s Vector Approach (PVA) provides the most interesting results for real deployment and could be used for future experiments.

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Section: Fast Fourier Transformmentioning

confidence: 99%

“…Bessous et al [16] dealt with the processing of signals obtained by the MCSA technique in order to determine the eccentricity fault in the IM. They used FFT to process the stator current signals.…”

Section: Fast Fourier Transformmentioning

confidence: 99%

Advanced Signal Processing Methods for Condition Monitoring

Jaroš

Byrtus

Dohnal

et al. 2022

Arch Computat Methods Eng

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“…However, some deficiencies still exist: (i) The influences of some important design parameters such as winding distribution modes and the number of parallel branches on back‐EMF are ignored. (ii) The effects of the SE ratio on the air gap magnetic field and back‐EMF have been revealed already. However, the effects of SE circumferential angles, which distort the air gap magnetic field and back‐EMF severely, are always neglected. (iii) Most of the eccentric diagnosis methods can only be realised through complicated post‐processing of measurement data in [18–21] or additional devices in [22–24]. As a result, the eccentricity fault cannot be diagnosed quickly or directly or at a low cost.…”

Section: Introductionmentioning

confidence: 99%

“…However, the effects of SE circumferential angles, which distort the air gap magnetic field and back-EMF severely, are always neglected. (iii) Most of the eccentric diagnosis methods can only be realised through complicated post-processing of measurement data in [18][19][20][21] or additional devices in [22][23][24]. As a result, the eccentricity fault cannot be diagnosed quickly or directly or at a low cost.…”

Section: Introductionmentioning

confidence: 99%

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Eccentric position diagnosis of static eccentricity fault of external rotor permanent magnet synchronous motor as an in‐wheel motor

Gao

Wang

et al. 2020

IET Electric Power Applications

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An eccentric position diagnosis method of static eccentricity (SE) fault of external rotor permanent magnet synchronous motor (ER‐PMSM) is presented. Firstly, an analytical model of no‐load radial magnetic field of ER‐PMSM is established. Analytical models of no‐load Back‐EMF of both unit motors and the whole motor are carried out and are verified by finite element method (FEM) and experimental measurements. Then, the influences of SE ratio, SE circumferential angle, winding distribution mode and number of parallel branches on no‐load radial magnetic field and no‐load Back‐EMF are analyzed based on these analytical models. The results show that SE does not affect the frequency characteristics of no‐load radial magnetic field, but changes space order characteristics. On one hand, for ER‐PMSM, of which the number of unit motors is equal to 1, SE causes no‐load Back‐EMF distortion. On the other hand, for ER‐PMSM, of which the number of unit motors is greater than 1, SE does not affect no‐load Back‐EMF of the whole motor, but it still leads to no‐load Back‐EMF distortion of unit motors. Therefore, based on total harmonic distortion (THD) of no‐load Back‐EMF of unit motor, a projection method of intersection lines for SE fault diagnosis of ER‐PMSM is proposed finally.

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A Real-time monitoring and condition-based maintenance system for electrical machines

Bugeja

Seguna

Scicluna

2022

13th Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion (MEDPOWER 2022)

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Maintaining electrical machinery, such as motor control centres, motor pumps and control panels, is critical to increasing safety and ensuring uptime of equipment in all industries. Further, three phase motors are commonly found in any industrial application. In a world where awareness of arc faults is increasing, personnel exposure to live equipment is leading to higher costs and worker safety concerns, making the transition from labour-intensive maintenance and inspection procedures to safer, more efficient, and cost-effective automated data collection is important. This contributes to the development of new Industry 4.0 technologies that can monitor real-time data from electrical machines to improve condition-based maintenance and reduce downtime. This would benefit the industry through higher efficiency, lower operating costs, and reduced energy consumption. Research was conducted to analyse the various faults that can occur in an electrical machine. This work contributed to the development of new hardware and software for a data acquisition system to monitor and detect potential electrical motor failures. Different condition monitoring methods were analysed to detect off-centre loads. The results showed that off-centre loads were detected, and the severity was also determined by introducing different test conditions. It was found that vibration analysis is a much better condition monitoring technique than motor current signature analysis. In addition, the fabricated compact hardware and software solution can be readily adapted for fault detection and prediction using machine learning methodologies.

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New vision about the overlap frequencies in the MCSA-FFT technique to diagnose the eccentricity fault in the induction motors

Cited by 15 publications

References 15 publications

Advanced Signal Processing Methods for Condition Monitoring

Advanced Signal Processing Methods for Condition Monitoring

Eccentric position diagnosis of static eccentricity fault of external rotor permanent magnet synchronous motor as an in‐wheel motor

A Real-time monitoring and condition-based maintenance system for electrical machines

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