A novel detection method of motor broken rotor bars based on wavelet ridge

Zhang, Zhengping; Ren, Zhaoyu; Wen-ying, Huang

doi:10.1109/tec.2003.815851

Cited by 113 publications

(12 citation statements)

References 19 publications

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“…However, these extra components fade away or coincide with other components, like saturationinduced components that do not contain information related to the fault, when the motor reaches steady state [39]. One of the challenges of using the current during startup, and one of the main reasons why it has not been commonly used for fault detection, is the complexity of extracting fault characteristics as the startup current is extremely unsteady and its period is quite short [40]. Recent analysis of transient current signature in induction motors [28,29] has shown that transient stator current is independent of load conditions that make it suitable for fault detection.…”

Section: Line Start Permanent Magnet Synchronousmentioning

confidence: 99%

Fault detection of broken rotor bar in LS-PMSM using random forests

et al. 2018

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Abstract-This paper proposes a new approach to diagnose broken rotor bar failure in a line start-permanent magnet synchronous motor (LS-PMSM) using random forests. The transient current signal during the motor startup was acquired from a healthy motor and a faulty motor with a broken rotor bar fault. We extracted 13 statistical time domain features from the startup transient current signal, and used these features to train and test a random forest to determine whether the motor was operating under normal or faulty conditions. For feature selection, we used the feature importances from the random forest to reduce the number of features to two features. The results showed that the random forest classifies the motor condition as healthy or faulty with an accuracy of 98.8% using all features and with an accuracy of 98.4% by using only the mean-index and impulsion features. The performance of the random forest was compared with a decision tree, Naïve Bayes classifier, logistic regression, linear ridge, and a support vector machine, with the random forest consistently having a higher accuracy than the other algorithms. The proposed approach can be used in industry for online monitoring and fault diagnostic of LS-PMSM motors and the results can be helpful for the establishment of preventive maintenance plans in factories.

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Section: Line Start Permanent Magnet Synchronousmentioning

confidence: 99%

Fault detection of broken rotor bar in LS-PMSM using random forests

et al. 2018

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“…wavelet polar maps, are used for gear fault diagnostics, to display wavelet amplitude and phase. The detection of broken rotor bars in induction machines by using CWT analysis of current waveforms was presented in Zhang et al (2003) …”

Section: Waveletsmentioning

confidence: 99%

Review on Approaches for Condition Based Maintenance in Applications with Induction Machines located Offshore

Cibulka¹,

Ebbesen²,

Hovland³

et al. 2012

MIC

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This paper presents a review of different approaches for Condition Based Maintenance (CBM) of induction machines and drive trains in offshore applications. The paper contains an overview of common failure modes, monitoring techniques, approaches for diagnostics, and an overview of typical maintenance actions. Although many papers have been written in this area before, this paper puts an emphasis on recent developments and limits the scope to induction machines and drive trains applied in applications located offshore.

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“…Several MCSA-based techniques have been proposed in literature for BRB fault detection, based on spectral analysis of sidebands of the power frequency or its harmonics using methods such as multiresolution Taylor-Kalman approach [5], low-frequency load torque oscillations [6], auto-regression analysis [7], autoregressive-spectrum analysis [8] and high-resolution parameter estimation [9]. However, similar sidebands can also occur in spectral maps of healthy motors due to rotor asymmetries caused by manufacturing/assembly tolerances and connection coupling.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive EMD Technique for Induction Motor Fault Detection

Mahmud¹,

Wang²

2019

JSIP

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Reliable induction motor (IM) fault detection techniques are very useful in industries to diagnose IM defects and improve operational performance. An adaptive empirical mode decomposition (EMD) technology is proposed in this paper for rotor bar fault detection in IMs. As the characteristic fault frequency will change with operating conditions related to load and speed, the proposed adaptive EMD technique correlates fault features over different frequency bands and intrinsic mode function (IMF) sidebands. The adaptive EMD technique uses the first IMF to detect the fault type and the second IMF as an indicator to predict the fault severity. It can overcome the problems of the sensitivity of sideband frequencies related to the speed and load oscillations. The effectiveness of the proposed adaptive EMD technique is verified by experimental tests under different motor conditions.

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A novel detection method of motor broken rotor bars based on wavelet ridge

Cited by 113 publications

References 19 publications

Fault detection of broken rotor bar in LS-PMSM using random forests

Fault detection of broken rotor bar in LS-PMSM using random forests

Review on Approaches for Condition Based Maintenance in Applications with Induction Machines located Offshore

An Adaptive EMD Technique for Induction Motor Fault Detection

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