EEMD-based notch filter for induction machine bearing faults detection

Amirat, Yassine; Benbouzid, Mohamed; Wang, T.; Bacha, Khmais; Feld, Gilles

doi:10.1016/j.apacoust.2017.12.030

Cited by 55 publications

(43 citation statements)

References 49 publications

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“…EEMD is a noise-assisted data analysis method, which can effectively avoid the mode mixing problem in EMD by adding various artificial white noises to the input signal [12]. The true IMFs is defined as the average of an ensemble of trials.…”

Section: B Eemdmentioning

confidence: 99%

“…In this study, the homopolar current was decomposed into several intrinsic mode functions (IMF) and the experimental results show that the 4th intrinsic mode functions can be used as an indicator for the bearing condition monitoring. In 2018, Amirat et al proposed another induction machine bearing faults detection method based on EEMD combined to Pearson correlation [12]. Although EMD and EEMD have been extensively used to extract sensitive fault information, they have certain major drawbacks, such as the mode mixing problem, time consuming and large reconstruction error.…”

Section: Introductionmentioning

confidence: 99%

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Detection of Bearing Outer Race Fault in Induction Motors using Motor Current Signature Analysis

Song

Wang

2019

2019 22nd International Conference on Electrical Machines and Systems (ICEMS)

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Bearing damage is the most common fault type in induction motors. In recent years, bearing fault detection based on motor current signature analysis (MCSA) has been gained extensive attention. However, the changes in the stator current signal which is caused by the bearing fault are usually very weak. In order to detect bearing fault effectively, a method for bearing fault detection based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) analysis of motor current signals is proposed. The CEEMDAN is used to decompose the stator current signal into several independent intrinsic mode functions (IMF), then the most sensitive IMF can be extracted. The experimental results show that the proposed approach is effective for bearing outer race fault detection.

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Section: B Eemdmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detection of Bearing Outer Race Fault in Induction Motors using Motor Current Signature Analysis

Song

Wang

2019

2019 22nd International Conference on Electrical Machines and Systems (ICEMS)

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show abstract

“…Using vibration signals from journal bearings and wavelet transformation, some induced faults on journal bearing are classified by artificial neural network [11]. The motor current is decomposed into the intrinsic mode function components by empirical mode decomposition technique and the signals which are similar to the main signal are removed, then the rest of the components are used to extract the feature for induction motor bearing defect detection [12]. The wavelet decomposition and statistical features of the guided ultrasonic wave are extracted to analyze faults on thick steel based on support vector machine (SVM) 2 [13].…”

Section: Introductionmentioning

confidence: 99%

Machine‐learning approach for fault detection in brushless synchronous generator using vibration signals

Rahnama

Vahedi

Mohammad‐Alikhani

et al. 2019

IET Science, Measurement & Technology

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In order to maintain continuous production and to avoid the maintenance cost increment in power plants, it is important to monitor the condition of equipment, especially the generator. Regarding the impossibility of direct access to rotating diodes in brushless synchronous generators, the condition monitoring of these elements is very important. In this paper, a novel fault detection method is proposed for the diode rectifier of brushless synchronous generator. At the first stage of this method, the vibration signals are recorded and feature extraction is performed by calculating the relative energy of discrete wavelet transform components. Multiclass support vector machine (MSVM) is used for classification, and the best mother wavelet and number of decomposition level are chosen based on classification performance. To enhance the performance of the classification, a modified sequential forward subset selection approach is included by which the best statistical features are selected. In this approach, besides selecting the best subset of statistical features, the classification parameter is tuned according to the selected subset to achieve the best performance. The result of the proposed method is eventually compared with those results of classification performance using conventional subset selection. Experimental results show that the proposed method can detect rectifier faults effectively.

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“…Consistently, in [8], denoising techniques are applied to highlight the faulty components in the current spectrum. Other advanced spectral techniques have also been proposed, such as wavelets [9,10], Short-Time Fourier Transform [11], Gabor spectrogram [11] Hilbert-Huang Transform [12,13], Empirical Mode Decomposition [14], Ensemble Empirical Mode Decomposition [15], Modulation Signal Bispectrum [16], Spectral Kurtosis [17], Spectral Subtraction [18], and space vector angular fluctuation method [19]. These techniques have the drawback of a high computational cost.…”

Section: Introductionmentioning

confidence: 99%

Condition Monitoring of Bearing Faults Using the Stator Current and Shrinkage Methods

et al. 2019

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Condition monitoring of bearings is an open issue. The use of the stator current to monitor induction motors has been validated as a very advantageous and practical way to detect several types of faults. Nevertheless, for bearing faults, the use of vibrations or sound generally offers better results in the accuracy of the detection, although with some disadvantages related to the sensors used for monitoring. To improve the performance of bearing monitoring, it is proposed to take advantage of more information available in the current spectra, beyond the usually employed, incorporating the amplitude of a significant number of sidebands around the first eleven harmonics, growing exponentially the number of fault signatures. This is especially interesting for inverter-fed motors. But, in turn, this leads to the problem of overfitting when applying a classifier to perform the fault diagnosis. To overcome this problem, and still exploit all the useful information available in the spectra, it is proposed to use shrinkage methods, which have been lately proposed in machine learning to solve the overfitting issue when the problem has many more variables than examples to classify. A case study with a motor is shown to prove the validity of the proposal.

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EEMD-based notch filter for induction machine bearing faults detection

Cited by 55 publications

References 49 publications

Detection of Bearing Outer Race Fault in Induction Motors using Motor Current Signature Analysis

Detection of Bearing Outer Race Fault in Induction Motors using Motor Current Signature Analysis

Machine‐learning approach for fault detection in brushless synchronous generator using vibration signals

Condition Monitoring of Bearing Faults Using the Stator Current and Shrinkage Methods

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