An intelligent fault diagnosis method for rolling bearings based on feature transfer with improved DenseNet and joint distribution adaptation

Qian, Chenhui; Jiang, Quan; Shen, Yehu; Huo, Chunran; Zhang, Qingkui

doi:10.1088/1361-6501/ac3b0b

Cited by 32 publications

(22 citation statements)

References 41 publications

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“…An et al 23 exploited self-learning transferable networks for mechanical fault diagnosis with unlabeled and unbalanced data. Similar research based on deep transfer diagnosis can be found in Wu et al 24 and Han et al 25 Qian et al 26 improved DenseNet and joint distribution adaptation for the transfer diagnosis. The core idea of the methods above is automatically learning the feature information of the two working conditions by using the deep learning model, and finally achieve the knowledge transfer by shortening the gap between the two working conditions.…”

Section: Introductionsupporting

confidence: 55%

Diversity maximization-based transfer diagnosis approach of rotating machinery

She

Chen

Yan

et al. 2023

Structural Health Monitoring

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The existing transfer diagnosis methods based on entropy minimization are easy to lead to trivial solution. To solve this problem, a deep diversity maximization-based adversarial transfer diagnosis approach for rotating machinery is presented in this paper. Firstly, the deep convolution neural network is utilized as the feature encoder to learn the characteristics of vibration signals in different working conditions. The diversity maximization strategy is taken to balance the entropy minimization, so as to avoid trivial local minimum. The categories predicted by nontrivial domain adaptation method are more diverse. Moreover, the entropy is conducted to evaluate the uncertainty of the predicted result of the classifier. Using this deterministic strategy based on entropy to adjust the domain discriminator. The experimental study demonstrates the effectiveness of the developed method.

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Section: Introductionsupporting

confidence: 55%

Diversity maximization-based transfer diagnosis approach of rotating machinery

She

Chen

Yan

et al. 2023

Structural Health Monitoring

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“…The main contributions of this paper are as follows: (1) The NGO algorithm is improved by introducing chaotic mapping and leader mutation selection strategy, and it is verified that CLNGO is superior to NGO in both convergence speed and convergence accuracy. (2) The CLNGO algorithm is used to select key parameters in FMD and MNAD, which makes it self-adaptive. Compared with VMD and EMD, FMD, this method can realize better separation of fault information and noise, and the proposed method is superior to MOMEDA and MCKD combined with envelope analysis in terms of fault feature extraction.…”

Section: Discussionmentioning

confidence: 99%

“…where X is the number of eagles, X i is the ith solution, N is the number of populations, m is the number of variables. The objective function value is calculated by Equation (2).…”

Section: Northern Goshawk Optimization Algorithm (Ngo)mentioning

confidence: 99%

“…Therefore, the processing of noise and the complete separation of multiple faults are the difficulties in the study of composite faults. Nowadays, the commonly used fault diagnosis methods are mainly based on expert systems [1], data-driven [2], and model-based [3], etc. The essence of fault diagnosis is a pattern recognition process, including signal preprocessing, feature extraction and fault classification [4].…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Composite Fault Diagnosis of Rolling Bearings Based on the CLNGO Algorithm

2022

Processes

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In this paper, a novel composite fault diagnosis method combining adaptive feature mode decomposition (FMD) and minimum noise amplitude deconvolution (MNAD) is proposed. Firstly, chaos mapping and leader mutation selection strategy were introduced to improve the Northern Goshawk algorithm (NGO), and a chaotic leadership Northern Goshawk optimization (CLNGO) algorithm was proposed. The advantages of the CLNGO algorithm in convergence accuracy and speed were verified by 12 benchmark functions. Then, a new index called sparse pulse and cyclicstationarity (SPC) is proposed to evaluate signal sparsity. Finally, SPC is used as the fitness function of CLNGO to optimize FMD and MNAD. The optimal decomposition mode n and filter length of FMD, and filter length L and noise ratio ρ of MNAD are selected. The CLNGO-FMD is used to decompose signal into different modes. The signal is reconstructed based on the kurtosis criterion and the CLNGO-MNAD method is used to reduce the noise of the reconstructed signal twice. The experimental results show that the proposed method can achieve the enhancement of weak features and the removal of noise to extract the fault feature frequency adaptively. Compared with EMD, VMD, MOMEDA, MCKD and other methods, the proposed method has better performance in fault feature frequency extraction, and it is effective for the diagnosis of single faults and composite faults.

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“…As the rapid development of CNN, its classic network structures like AlexNet [25], VGG-Nets [26], GoogLeNet [27], ResNet [28], and DenseNet [29] have appeared successively. Although the architectures of CNN are getting more and more complex, their image processing capabilities are getting stronger and stronger.…”

Section: Inverse Design DL Approachmentioning

confidence: 99%

Deep Learning for Inverse Design of Broadband Quasi-Yagi Antenna

Zhou

Mei

et al. 2023

International Journal of RF and Microwave Computer-Aided Engine

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Deep learning (DL) approaches have been increasingly adopted to design antenna autonomously. For obtaining geometry of the broadband quasi-Yagi antenna from its physic response images directly, we propose an inverse design approach based on the optimized bidirectional symmetry GoogLeNet, which can extract the required bandwidth information to redesign the geometric parameters of antenna without changing its physical structure. It demonstrates that the bandwidth of a reference quasi-Yagi antenna is improved from 0.6 GHz to 1.15 GHz through the proposed inverse design DL approach, and the measured bandwidth value of this redesigned quasi-Yagi antenna achieves 1.16 GHz, which is improved 93% actually. The numerical and measured results indicate that the proposed DL approach could significantly improve the performance of the existed quasi-Yagi antenna and present a new attempt to apply the image processing techniques in resolving physical problem.

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An intelligent fault diagnosis method for rolling bearings based on feature transfer with improved DenseNet and joint distribution adaptation

Cited by 32 publications

References 41 publications

Diversity maximization-based transfer diagnosis approach of rotating machinery

Diversity maximization-based transfer diagnosis approach of rotating machinery

Adaptive Composite Fault Diagnosis of Rolling Bearings Based on the CLNGO Algorithm

Deep Learning for Inverse Design of Broadband Quasi-Yagi Antenna

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