Detection of Induction Motor Coupling Unbalanced and Misalignment via Advanced Transient Current Signature Analysis

Antonino-Daviu, José A.; Popaleny, Peter

doi:10.1109/icelmach.2018.8506949

Cited by 30 publications

(20 citation statements)

References 17 publications

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“…On the other hand, a t-f representation without system oriented fine tuning of parameters -which in this case relate to the slip and sampling frequency -might also result in information loss. It should also be noted that the approach of this system oriented parameter tuning can be used for t-f process like the ones described in [32][33][34] or [53][54][55][56][57] as an initialisation pre-setting and finetuning; it is also eligible to define the two extreme limits where these methods or similar types of analyses can be tested, when aiming to extract different pieces of information in two stages. At one of these extremes shown in Fig.…”

Section: Analysis Of the Fem Simulation Resultsmentioning

confidence: 99%

On the broken rotor bar diagnosis using time–frequency analysis: ‘Is one spectral representation enough for the characterisation of monitored signals?’

Panagiotou

Arvanitakis

Lophitis

et al. 2019

IET Electric Power Applications

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Section: Analysis Of the Fem Simulation Resultsmentioning

confidence: 99%

On the broken rotor bar diagnosis using time–frequency analysis: ‘Is one spectral representation enough for the characterisation of monitored signals?’

Panagiotou

Arvanitakis

Lophitis

et al. 2019

IET Electric Power Applications

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“…One recent work regarding MCSA for unbalance and misalignment is proposed by (Antonino-Daviu and Popaleny, 2018). They propose a short-time Fourier transform (STFT) approach to detect problems explicitly due to mistaken installation of induction motors.…”

Section: Introductionmentioning

confidence: 99%

Rotor Unbalance Kind and Severity Identification by Current Signature Analysis with Adaptative Update to Multiclass Machine Learning Algorithms

Avila

Schaberle

Youssef

et al. 2021

SET

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The health of a rotating electric machine can be evaluated by monitoring electrical and mechanical parameters. As more information is available, it easier can become the diagnosis of the machine operational condition. We built a laboratory test bench to study rotor unbalance issues according to ISO standards. Using the electric stator current harmonic analysis, this paper presents a comparison study among Support-Vector Machines, Decision Tree classifies, and One-vs-One strategy to identify rotor unbalance kind and severity problem – a nonlinear multiclass task. Moreover, we propose a methodology to update the classifier for dealing better with changes produced by environmental variations and natural machinery usage. The adaptative update means to update the training data set with an amount of recent data, saving the entire original historical data. It is relevant for engineering maintenance. Our results show that the current signature analysis is appropriate to identify the type and severity of the rotor unbalance problem. Moreover, we show that machine learning techniques can be effective for an industrial application.

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“…This approach is developed out of the variation of magnetic flux in the motor stator current as a result of a transient fault. Protection strategies that are based on MCSA have yielded more promising results, such as their ease of implementation and minimal sensitivity to the control framework and non‐linearities [18, 19].…”

Section: Introductionmentioning

confidence: 99%

Induction motor fault detection based on multi‐sensory control and wavelet analysis

Abubakar

Mekhilef

Gaeid³

et al. 2020

IET Electric Power Applications

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Detection and fault‐tolerant control (FTC) of faults in the early stage is desirable in improving efficiency. An implementation strategy is proposed for the individual controllers that work collectively in induction motor (IM) drive by interswitching from one form of a control strategy to another. The interswitching occurs between voltage by frequency (V/f) open‐loop control, closed‐loop (V/f) control, sensorless vector control and sensor vector control. Optimal performance capabilities are attained with vector control, whereas V/f is a setup that is affordable but with increased speed. In this study, the faults are open and short circuits winding faults, speed sensor failures and stator winding faults. When the severity of the fault is high, an embedded protection entity interrupts the motor. Daubechies 10 wavelet is used due to its significant vanishing moments compared to the other types of Daubechies as fault index with the stator current of 1 kW IM. A novel enhanced model reference adaptive system is employed for sensorless vector control to assess the motor speed. Both the simulation and experiment (using the F28335 DSP controller) indicate that the framework is effective in detecting the fault, ensuring the robustness of the FTC scheme and proving the effectiveness of the proposed algorithm.

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Detection of Induction Motor Coupling Unbalanced and Misalignment via Advanced Transient Current Signature Analysis

Cited by 30 publications

References 17 publications

On the broken rotor bar diagnosis using time–frequency analysis: ‘Is one spectral representation enough for the characterisation of monitored signals?’

On the broken rotor bar diagnosis using time–frequency analysis: ‘Is one spectral representation enough for the characterisation of monitored signals?’

Rotor Unbalance Kind and Severity Identification by Current Signature Analysis with Adaptative Update to Multiclass Machine Learning Algorithms

Induction motor fault detection based on multi‐sensory control and wavelet analysis

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