Single-channel blind signal separation method for time-frequency overlapped signal based on CEEMD-FastICA

Deng, Xianrong; Pang, Lihui; Jiang, Kaili; Wang, Xinlin

doi:10.1109/icsp.2016.7878064

Cited by 9 publications

(3 citation statements)

References 16 publications

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“…The EEMD adds the white noise in the data analysis, and it can clearly separate the natural scale of signals. The complete ensemble empirical mode decomposition (CEEMD) algorithm [7] adds positive and negative pairs of white noise to the observed signal, which can better eliminate noise residuals. Based on the CEEMD algorithm, the generated adaptive noise is added to the signal in pairs.…”

Section: Afeemdmentioning

confidence: 99%

An intelligent bearing fault diagnosis method based on the AFEEMD and 1D CNNs

Sun¹,

Wang²

2019

Vib. proced.

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To process the non-stationary vibration signals and improve accuracy of bearing fault diagnosis, this paper presents a novel intelligent fault diagnosis method based on the adaptive fast ensemble empirical mode decomposition (AFEEMD) and one-dimensional convolutional neural networks (1D CNNs). First, the AFEEMD algorithm is utilized to decompose the raw signals into intrinsic mode functions (IMFs). Then, the time and frequency statistic features of the first several IMFs are analyzed to form feature vector, which are used as the input of 1D CNNs to identify the bearing fault. The performance of the proposed method is validated using the dataset from the Case Western Reserve University (CWRU). Compared with the traditional back propagation neural network (BPNN), the results show that the proposed AFEEMD-1D CNNs method not only can obtain higher accuracy and achieve more reliable performance, but also can improve the generalization performance. Due to the end-to-end feature learning capacity, it can be extended to other machinery for fault diagnosis.

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Section: Afeemdmentioning

confidence: 99%

An intelligent bearing fault diagnosis method based on the AFEEMD and 1D CNNs

Sun¹,

Wang²

2019

Vib. proced.

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“…Hong and Liang 16 combined wavelet transforms with the ICA algorithm to achieve an accurately diagnosis of compound faults from the single-channel acquisition. Deng et al 17 used the full-set ensemble empirical modal decomposition combined with the FastICA algorithm to achieve better separation of fault source signals from the single-channel acquisition. Shi et al 18–22 proposed a gearbox fault diagnosis method based on EEMD and single-channel blind source separation, and verified the feasibility of the method through experiments.…”

Section: Introductionmentioning

confidence: 99%

The Single-channel blind source separation based on VMD and Tukey M estimation for rolling bearing composite fault diagnosis

Wang

Zhang

Cao

et al. 2023

Measurement and Control

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Rolling bearing is one of the core components in rotating machinery, and its running status directly affects the operation of the whole equipment. Faults of rolling bearings in the actual working process are often multiple faults. To effectively separate fault sources, the blind source separation method is used for the compound fault diagnosis of rolling bearings. Because of the impact of the number of artificially limited decompositions and quadratic penalty factor on VMD in the decomposition process, and the slow convergence and low accuracy in the objective function of traditional FastICA operation, the VMD algorithm based on the energy loss coefficient and the information entropy is proposed, which adaptively determines the number of modal components and the quadratic penalty factor; The Tukey M estimation is selected as the objective convergence function of the FastICA algorithm to improve its robustness. First, VMD is used to decompose the signal; Secondly, the original signal and the decomposed IMF component are reconstructed, the covariance matrix and the singular value decomposition are constructed, the number of fault sources is estimated by the proximity dominance method, and the decomposed IMF components are filtered through correlation analysis and kurtosis index to build a multi-channel feature set; Finally, the constructed multi-channel feature set is input to the FastICA algorithm based on the Tukey M estimation for the separation of fault source signals to achieve composite fault diagnosis. The compound fault experiment shows that the proposed method in this paper can effectively realize the blind source separation of rolling bearing fault features to realize the compound fault diagnosis in different positions.

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“…There are mainly two types of solutions to the SCBS problem, that is, the traditional model-driven approaches and the data-driven approaches. The traditional independent component analysis and sparse component analysis methods exhibit poor separation performance under single-channel conditions [4][5][6][7]. Particle filtering (PF) [8][9][10] and persurvivor processing (PSP) [11][12][13] algorithms have become the mainstream methods to tackle SCBS.…”

Section: Introductionmentioning

confidence: 99%

A novel end‐to‐end deep separation network based on attention mechanism for single channel blind separation in wireless communication

Zheng

et al. 2022

IET Signal Processing

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The traditional methods exhibit unstable performance and high complexity for separating co‐frequency modulated wireless communication signals under single‐channel conditions. In this study, an end‐to‐end deep separation network based on the attention mechanism is proposed, which employs the encoder‐separator‐decoder architecture to implement the separation. The encoder can convert the signal into a high‐dimensional feature representation for the separator to achieve separation of the signal in a high‐dimensional space. The core of the proposed deep separation network is the innovative separator, which is mainly composed of attention‐based convolution units with residual connection. The attention‐based convolution unit integrates the large kernel convolution and modified global context (GC) block to simultaneously capture the local and global information of the signal. Furthermore, we improve the GC block to implement channel weighting and location weighting for the given feature map, thus further enhancing the adaptability of the model. The experimental results show that the proposed method not only outperforms the traditional methods in separating mixed signals with the same modulation, but also enables the separation of mixed signals with different modulations.

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Single-channel blind signal separation method for time-frequency overlapped signal based on CEEMD-FastICA

Cited by 9 publications

References 16 publications

An intelligent bearing fault diagnosis method based on the AFEEMD and 1D CNNs

An intelligent bearing fault diagnosis method based on the AFEEMD and 1D CNNs

The Single-channel blind source separation based on VMD and Tukey M estimation for rolling bearing composite fault diagnosis

A novel end‐to‐end deep separation network based on attention mechanism for single channel blind separation in wireless communication

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