A RUL prediction of bearing using fusion network through feature cross weighting

Wang, Zhijian; Li, Y; Dong, Lei; Li, Yanfeng; Du, Wenhua

doi:10.1088/1361-6501/acdf0d

Cited by 10 publications

(4 citation statements)

References 35 publications

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“…Regarding the RUL problem, some scholars have successfully applied various DL-based methods to RUL prediction problems, for instance, Gated Recurrent Unit (GRU) [20], Recurrent Neural Network (RNN) [21], DCNN [22], and Long Short-Term Memory Network (LSTM) [23]. RNN, as a datadriven method, has been proposed for RUL prediction problems due to its cyclic structure, which can effectively process temporal data [24]. However, due to the problem of its architecture, RNN will lead to the disappearance of the model gradient, which will lead to a poor model training effect [25].…”

Section: Introductionmentioning

confidence: 99%

A treelike framework combining fault diagnosis and RUL prediction

Chai,

Dong,

Ren

et al. 2024

Meas. Sci. Technol.

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Most existing deep learning methods consider the remaining useful life (RUL) prediction problem under a single failure mode and cannot solve the RUL prediction problem with multiple failure modes coexisting caused by component coupling in actual engineering systems. Besides, most deep learning methods are always developed as a black box, which may result in unstable results. Thus, considering these issues, this paper proposes a novel tree network framework to address fault classification and RUL prediction in parallel, and the RUL prediction results are fused output, which are suitable for bearing RUL prediction with multiple faults. First, this paper develops a fault recognizer combining a frequency domain classifier and deep convolutional neural network (DCNN) to improve model selection accuracy. Secondly, this paper proposes a feature fusion algorithm based on the Gini coefficient, and the fused indicators are input into the RUL prediction sub-network for model training. Finally, the RUL sub-network prediction results are dynamically weighted and fused with the fault classification results to obtain the RUL based on SoftMax. The bearing dataset XJTU-SY is introduced to verify the efficiency of the proposed method, and computational results show that the developed framework can effectively predict RUL compared with other traditional methods, especially for RUL prediction under multiple failure modes.

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Section: Introductionmentioning

confidence: 99%

A treelike framework combining fault diagnosis and RUL prediction

Chai,

Dong,

Ren

et al. 2024

Meas. Sci. Technol.

Self Cite

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“…The working environment is usually harsh, and the fault signal characteristics are often covered by background noise and mechanical rotation signals, which are not easy to extract. Therefore, it is necessary to develop a fault diagnosis method when working under strong background noise interference conditions [2,3].…”

Section: Introductionmentioning

confidence: 99%

Fault Feature Extraction Method of Ball Screw Based on Singular Value Decomposition, CEEMDAN and 1.5DTES

Wu,

Niu,

Wang

2023

Actuators

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In this article, a method is proposed to effectively extract weak fault features and accurately diagnose faults in ball screws, even in the presence of strong background noise. This method combines singular value decomposition (SVD), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and the 1.5-dimensional spectrum (1.5D) to process and analyze fault vibration signals. The first step involves decomposing the fault signal using the SVD algorithm. The singular values are then screened, and the part of the screen containing more noise information is extracted to complete the first denoising step. The second step involves decomposing the signal after the initial denoising process using CEEMDAN and removing some of the false components from the intrinsic mode function (IMF) components, based on the kurtosis correlation function index. The signal is then reconstructed to complete the second denoising step. Finally, the denoised signal is analyzed using Teager energy operator demodulation and 1.5D spectral analysis to extract the fault frequency and determine the location of the fault in the ball screw. This method has been compared with other denoising methods, such as wavelet packet decomposition combined with CEEMDAN or SVD combined with variational mode decomposition (VMD), and the results show that under the condition of strong background noise, the proposed method can better extract the fault frequency of ball screw.

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“…However, due to the unique operating conditions and precision requirements of the latter, its fault characteristics are not entirely the same as the former. Replicating many fault diagnosis methods from the former to the latter poses several challenges [10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Early Fault Diagnosis of Space Flywheel Rotor System

Liao,

Xie,

Deng

et al. 2023

Sensors

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Three frequently encountered problems—a variety of fault types, data with insufficient labels, and missing fault types—are the common challenges in the early fault diagnosis of space flywheel rotor systems. Focusing on the above issues, this paper proposes an intelligent early fault diagnosis method based on the multi-channel convolutional neural network with hierarchical branch and similarity clustering (HB-SC-MCCNN). First, a similarity clustering (SC) method is integrated into the parameter-shared dual MCCNN architecture to set up as the basic structural block. The hierarchical branch model and additional loss are then added to SC-MCCNN to form a hierarchical branch network, which simplifies the problem of fault multi-classification into binary classification with multi-steps. Based on the self-learning characteristics of the proposed model, the unlabeled data and the missing fault types in the training set are re-labeled to realize the re-training of the network. The results of the experiments for comparing the abilities between the proposed method and several advanced deep learning models confirm that on the established early fault dataset of the space flywheel rotor system, the proposed method successfully achieves the hierarchical diagnosis and presents stronger competitiveness in the case of insufficient labeled data and missing fault types at the same time.

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A RUL prediction of bearing using fusion network through feature cross weighting

Cited by 10 publications

References 35 publications

A treelike framework combining fault diagnosis and RUL prediction

A treelike framework combining fault diagnosis and RUL prediction

Fault Feature Extraction Method of Ball Screw Based on Singular Value Decomposition, CEEMDAN and 1.5DTES

Intelligent Early Fault Diagnosis of Space Flywheel Rotor System

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