Bearing fault diagnosis method using the joint feature extraction of Transformer and ResNet

Hou, Shixi; Lian, Ao; Chu, Yundi

doi:10.1088/1361-6501/acc885

Cited by 23 publications

(7 citation statements)

References 32 publications

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“…For example, Zhang et al 26 proposed a convolutional long-and short-term memory recurrent neural network prediction model, which realizes the precipitation forecast by capturing the temporal relationship between the long and short terms. Hou et al [27][28][29] proposed a convolutional neural network precipitation forecasting method, which effectively predicted the precipitation intensity. He et al 30 proposed a constrained linear data feature mapping neural network model as a mathematical model for classifying radar observation images using convolutional neural networks, which had fewer parameters and more accurate forecasting results for strong convective precipitation.…”

Section: Introductionmentioning

confidence: 99%

A new strong convective precipitation forecasting method based on attention mechanism and spatio-temporal reasoning

Zhao,

Wang,

Zhao

et al. 2024

Preprint

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Radar observation variables reﬂect the precipitation amount of strong convective precipitation processes, which accurate forecast is an important difﬁculty in weather forecasting. Current forecasting methods are mostly based on radar echo extrapolation, which has the insufﬁciency of input information and the ineffectiveness of model architecture. This paper presents a Bidirectional Long Short-Term Memory forecasting method for strong convective precipitation based on the attention mechanism and residual neural network (ResNet-Attention-BiLSTM). First, this paper uses ResNet to effectively extract the key information of extreme weather and solves the problem of regression to the mean of the prediction model by learning the residuals of the radar observation data. Second, this paper uses the attention mechanism to adaptively weight the fusion of the features to enhance the extraction of the important features of the precipitation image data. On this basis, this paper presents a novel spatio-temporal reasoning method for radar observations and establishes a precipitation forecasting model, which captures the past and future time-order relationship of the sequence data. Finally, this paper conducts experiments based on the real collected data of a strong convective precipitation process and compares its performance with the existing models, the mean absolute percentage error of this model was reduced by 15.94% (1km), 18.72% (3km), and 14.91% (7km), and the prediction accuracy (R2) was increased by 10.89% (1km), 9.61% (3km), and 9.29% (7km), which proves the state of the art and effectiveness of this forecasting model.

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Section: Introductionmentioning

confidence: 99%

A new strong convective precipitation forecasting method based on attention mechanism and spatio-temporal reasoning

Zhao,

Wang,

Zhao

et al. 2024

Preprint

View full text Add to dashboard Cite

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“…In recent years, deep learning (DL) methods based on CNNs have seen rapid development in various fields, including denoising of vibration signals [26][27][28], fault diagnosis [29][30][31], and remaining service life prediction of mechanical equipment [32,33]. While DL models referenced in the literature [29][30][31] can attain diagnostic accuracy exceeding 95% in noise-free environments, their performance markedly deteriorates in high-noise environments. This degradation indicates that background noise can obscure the original fault characteristics, leading to diagnostic errors and other issues.…”

Section: Introductionmentioning

confidence: 99%

PSO-optimized SSLMS adaptive filter for signal denoising of rolling bearings under small sample condition

Deng,

Wang

2024

Meas. Sci. Technol.

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To address the issue that the deep learning-based denoising algorithms can hardly effectively eliminate the background noise under small sample data condition, this paper proposes a new denoising method based on spectral subtraction (SS) and least mean square (LMS) adaptive filtering algorithms. To achieve the adaptive selection for the parameters of SS and LMS algorithms, particle swarm optimization (PSO) approach is employed to search and optimize the parameters in the two algorithms, which is helpful for the two algorithms to play an important role in eliminating the noise components with the different properties. Subsequently, the SS algorithm and the LMS algorithm are appropriately combined, and the SS-processed signal is input into the LMS algorithm as a desired signal to actualize the LMS adaptive filtering function. In this way, the denoising performance of both algorithms can be maximally utilized, which achieves effective noise reduction in vibration signal. The effectiveness and superiority of the proposed method are validated through simulation data and rolling bearing experiment data, respectively. The results demonstrate that the proposed method significantly diminishes noise components and retains precise and reliable fault features under small sample data condition, which provides an effective denoising method for rolling bearing vibration signals under small sample data condition in practical engineering scenarios.

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“…Fault diagnosis methods mainly include vibration signalbased methods [6][7][8][9] and thermal signal-based methods [10,11]. The vibration signal has high accuracy, it is the most accessible signal in research and applications.…”

Section: Introductionmentioning

confidence: 99%

“…The precision rate, recall rate, and F-score of this run are shown in figure12. In fault diagnosis, precision rate and recall rate are evaluation metrics, represented respectively as follows: Precision = TP/(TP + FP)(6) Recall = TP/(TP + FN)(7)…”

mentioning

confidence: 99%

Fault diagnosis of bearing-rotor system based on infrared thermography: ReSPP with multi-scaled training method

An,

Liu,

Shao

et al. 2023

Meas. Sci. Technol.

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The fault diagnosis method of bearing-rotor system based on infrared thermography can reflect the global fault information of the equipment, which is an advanced non-contact monitoring measure. Current diagnosis methods focus on the analysis of single-scale input images, and the network only accepts fixed-size inputs. The model is not capable of fusing multi-scaled fault features while processing single-scaled images, and compression for the size constraint will cause geometric distortion. This paper proposed a novel fault diagnosis model ReSPP (Residual Network with Least Spatial Pyramid Pooling (LSPP)) based on the improved LSPP. LSPP solves the problem of fault feature distortion caused by fixed-size constraint of the network. By replacing the single-scaled training with the proposed multi-scaled training method applied to fault diagnosis, the weight parameters of ReSPP pool the deep fault features of the bearing-rotor system at multiple scales, retaining the critical fault semantic information. The proposed model solves the fault feature loss in the process of feature extraction by improving the subsampling residual block. Experimental results show that ReSPP with multi-scaled training method (ReSPP-MSTM) classifies the fault conditions of the bearing-rotor system with an average diagnostic accuracy of 99.18%.

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Bearing fault diagnosis method using the joint feature extraction of Transformer and ResNet

Cited by 23 publications

References 32 publications

A new strong convective precipitation forecasting method based on attention mechanism and spatio-temporal reasoning

A new strong convective precipitation forecasting method based on attention mechanism and spatio-temporal reasoning

PSO-optimized SSLMS adaptive filter for signal denoising of rolling bearings under small sample condition

Fault diagnosis of bearing-rotor system based on infrared thermography: ReSPP with multi-scaled training method

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