Quality monitoring of aluminum alloy DPMIG welding based on broadband mode decomposition and MMC-FCH

Cheng, Junsheng; Li, Zepei; He, Kuanfang; Liu, Yanfei; Lu, Qinghua; Li, Qingxian; Liu, Liangjiang; Luo, Ruiqiong

doi:10.1016/j.measurement.2020.107683

Cited by 16 publications

(9 citation statements)

References 17 publications

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“…where T j is the modulated differential operator, and it is given in equation ( 7), w ′ � Mw, where w is equivalent to the biggest frequency of r i (n), λ > 0 can be set to 1, and D (2) is the second-order differential operator. (4) Obtain the optimal value T j (j � 1, 2, 3) by solving for P 1 using ACROA, find the minimum value T j , and select the optimal value IMF i (10) is met, the iterative process is terminated. Otherwise, go back to Step 3.…”

Section: Mbmd and Detmentioning

confidence: 99%

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Fault Identification of Low-Speed Hub Bearing of Crane Based on MBMD and BP Neural Network

Guo

Yang

Cheng

et al. 2022

Shock and Vibration

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As the key bearing part of the crane, the low-speed hub bearing of the crane exists in special working conditions of low-speed and alternating heavy load. It is difficult to extract its fault characteristics accurately by existing analysis methods. The main idea of the broadband mode decomposition (BMD) method previously proposed is to search in the association dictionary library containing broadband and narrowband signals. However, when it is applied to the broadband signals interfered by strong noise, the decomposition is easy to produce modal confusion, so the modulated broadband mode decomposition (MBMD) method is proposed. The fault signal just can be analyzed by MBMD, so it is applied to the fault diagnosis of low-speed hub bearing of the crane. To realize the fault identification of low-speed hub bearing of the crane, firstly, the original signal is decomposed by MBMD. Secondly, the eigenvalues of the first three-component signals are calculated, the eigenvalue matrix is constructed, and the marked features are selected by the distance evaluation technique (DET). Finally, the marked features are input into BP neural network for training and testing to identify the types of bearing fault. Compared with EEMD, VMD, and BMD, the MBMD method combined with BP neural network has good performance in feature extraction and fault identification.

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Section: Mbmd and Detmentioning

confidence: 99%

“…In the engineering field, the commonly used time-frequency analysis methods are used to extract fault features from noise interference [2][3][4][5][6][7][8][9]. It can be roughly summarized as ways based on Fourier transform and not based on Fourier transform [10,11]. e former includes WT and VMD.…”

Section: Introductionmentioning

confidence: 99%

Fault Identification of Low-Speed Hub Bearing of Crane Based on MBMD and BP Neural Network

Guo

Yang

Cheng

et al. 2022

Shock and Vibration

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“…As online monitoring technology continues to advance and improve, the development tendency of intelligent AC contactors is becoming increasingly evident [2,3]. The online collection and intelligent analysis of AC contactor operating parameters can provide a scientific basis for equipment maintenance [4][5][6]. The remaining electrical life of the AC contactor reflects the line's on-off performance, it can effectively improve the reliability of the power system and reduce economic losses if it can be predicted in time before failure.…”

Section: Introductionmentioning

confidence: 99%

Remaining electrical life prediction of AC contactor based on CAE-BiGRU-Attention

Xing,

Liu,

Peng

et al. 2023

Meas. Sci. Technol.

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To tackle the challenges of low prediction accuracy caused by single-feature modeling, and the hidden state of the neural network easily loses some information of the l ong time series, a method for predicting the remaining electrical life of AC contactor using a Convolutional Autoencoder-Bidirectional Gated Recurrent Unit-Attention (CAE-BiGRU-Attention) was proposed in this work. Firstly, the feature parameters were extracted from the AC contactor full-life test, and an optimal feature subset was selected using Neighborhood Component Analysis (NCA) and Spearman rank correlation coefficient to characterize the degradation state of electrical life effectively. Then, the deep information of the optimal feature subset was extracted using CAE. Finally, the remaining electrical life of the AC contactor was treated as a long time series problem and predicted in time series by BiGRU-Attention accurately. The case analysis demonstrates that the model has better prediction accuracy than RNN (Recurrent Neural Network), LSTM (Long Short-Term Memory), GRU, BiGRU and CAE-BiGRU models, with an average effective accuracy of 97.12 %. This effectively demonstrates the model’s feasibility to accurately predict temporal sequences in the remaining electrical life prediction of electrical equipment.

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“…It is of great significance to monitor its running state. Many researches use signal processing methods, especially vibration signal processing methods to extract bearing fault features, [1][2][3][4][5] among which resonance demodulation algorithm is a typical method.…”

Section: Introductionmentioning

confidence: 99%

Polynomial fitting detrended autocorrelation kurtosis of 1.5D spectrum and its application in bearing fault feature extraction

Zhang

et al. 2023

Advances in Mechanical Engineering

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Kurtogram is a typical fault diagnosis algorithm, and its derivative methods are widely used in bearing fault signal processing. The key to such methods is to select the indexes that are specifically sensitive to periodic fault characteristics. The traditional time domain kurtosis index is too sensitive to non-periodic transient impulses, and the frequency domain index is too sensitive to harmonic components, which seriously affects the selection of resonance frequency band and the extraction of fault features. In view of this, a polynomial detrended autocorrelation kurtosis of 1.5D spectrum is proposed in this paper. Firstly, the original signal is decomposed by wavelet packet decomposition, and the 1.5D spectrum of each subband signal is calculated. Then, the 1.5D spectral autocorrelation coefficient of each subband is calculated, and the trend term is removed from the specific interval of autocorrelation coefficient by polynomial fitting. Finally, the kurtosis of the autocorrelation coefficient after removing the trend term is calculated to select the optimal resonance frequency band for analysis, so as to extract the fault feature information. The experimental data show that this method is sensitive to the fault characteristic frequency and frequency doubling with coupling relationship, and can suppress non-periodic transient impulse and harmonic interference.

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Quality monitoring of aluminum alloy DPMIG welding based on broadband mode decomposition and MMC-FCH

Cited by 16 publications

References 17 publications

Fault Identification of Low-Speed Hub Bearing of Crane Based on MBMD and BP Neural Network

Fault Identification of Low-Speed Hub Bearing of Crane Based on MBMD and BP Neural Network

Remaining electrical life prediction of AC contactor based on CAE-BiGRU-Attention

Polynomial fitting detrended autocorrelation kurtosis of 1.5D spectrum and its application in bearing fault feature extraction

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