A fault diagnosis method based on improved parallel convolutional neural network for rolling bearing

Xu, Tao; Lv, Huan; Lin, Shoujin; Tan, Haihui; Zhang, Qing

doi:10.1177/09544100231158421

Cited by 3 publications

(1 citation statement)

References 43 publications

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“…Guo et al [21] combined CNN to extract and analyze features from speed signals, proposing a bearing fault diagnosis method based on motor speed signals. Xu et al [22] introduced an improved deep CNN fault diagnosis model, achieving high-precision fault diagnosis even in noisy environments. Zhang et al [23] transformed the original signal into twodimensional images for feature extraction, eliminating the influence of manual feature engineering; they made improvements to the CNN network for feature extraction and fault diagnosis processes.…”

Section: Related Workmentioning

confidence: 99%

Novelty Detection and Fault Diagnosis Method for Bearing Faults Based on the Hybrid Deep Autoencoder Network

et al. 2023

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In the event of mechanical equipment failure, the fault may not belong to any known category, and existing deep learning methods often misclassify such faults into a known class, leading to erroneous fault diagnosis. In order to address the challenge of identifying new types of faults in mechanical equipment fault diagnosis, this paper proposes a novelty detection and fault diagnosis method for bearing faults based on a hybrid deep autoencoder network. Firstly, a hybrid deep autoencoder network with one input and two outputs was constructed. The original data were then fed into the network to obtain its low-dimensional representation and reconstructed data. By setting a threshold based on the reconstruction error, novel class faults can be detected, while known faults can be classified based on low-dimensional features. Experimental results demonstrate that the proposed method achieves a recognition accuracy of 98.59% (100%) for novel class identification (known fault classification) on the CWRU bearing dataset, 96.79% (98.53%) on the Paderborn dataset, and 84.34% (97.03%) on the MFPT dataset. Therefore, the hybrid deep autoencoder network not only accurately detects unknown types of faults but also effectively classifies known fault types, demonstrating excellent fault identification and classification capabilities.

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Section: Related Workmentioning

confidence: 99%

Novelty Detection and Fault Diagnosis Method for Bearing Faults Based on the Hybrid Deep Autoencoder Network

et al. 2023

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Intelligent fault diagnosis of rolling bearing under unbalanced samples based on simulation data fusion

Mei,

Xu,

Zhang

et al. 2024

Meas. Sci. Technol.

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With the rapid development of intelligent manufacturing, data-driven deep-learning techniques have been widely used in bearing fault diagnosis. However, the problem of unbalanced data samples usually occurs in actual production environments due to the difficulty of collecting comprehensive fault data covering multiple fault types and degrees, which directly affects the diagnosis performance. For this reason, this paper proposes a new method for simulation data-driven bearing fault diagnosis. In this paper, based on the vibration mechanism of rolling bearings, a fault signal simulation model that can accurately simulate different damage degrees of inner and outer rings is constructed. The model cannot only effectively extend the data set but also generate simulated signals that are highly consistent with accurate fault signals in terms of amplitude modulation (AM) characteristics in the absence of actual samples. This paper conducts experiments on the CWRU rolling bearing fault dataset by combining the generated simulation data with deep learning methods. The experimental results show that the model's classification accuracy reaches 98.7% and 93.7% in the case of a small number of samples (small sample scenario) and no actual samples (no sample scenario), respectively. In addition, we conducted experiments with multiple working conditions on a testbed built in the laboratory, and all of them also achieved excellent results.

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Fault diagnosis method of rotating machinery based on MSResNet feature fusion and CAM

2024

J. vibroeng.

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To solve the problem of noise interference, it is difficult to extract multi-scale information from complex vibration signals in fault diagnosis with the single-scale convolution kernel of classical deep learning model convolutional neural network (CNN). Therefore, a fault diagnosis method of rotating machinery based on MSResNet feature fusion and CAM is proposed. The residual network (ResNet) and multi-scale convolutional neural network (MSCNN) are combined to extract multi-scale feature information according to convolution kernels with different sizes, so as to avoid the loss of single-scale feature extraction. Make full use of the advantages of the residual network to skip the connection and prevent the feature information extracted by the multi-scale convolution kernel from being lost when the convolution layer propagates forward. In addition, in order to avoid the interference of invalid features after multi-scale information feature fusion, a channel attention mechanism module (CAM) is introduced to screen important features adaptively. The effectiveness of MSResNet-CAM is verified by the bearing data set of Western Reserve University (CWRU) and the data set of QPZZ-II gearbox, and the anti-noise ability is verified by adding noise to the two data sets. The experimental results show that MSResNet-CAM has the characteristics of high fault classification accuracy, good robustness and strong anti-noise ability.

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A fault diagnosis method based on improved parallel convolutional neural network for rolling bearing

Cited by 3 publications

References 43 publications

Novelty Detection and Fault Diagnosis Method for Bearing Faults Based on the Hybrid Deep Autoencoder Network

Novelty Detection and Fault Diagnosis Method for Bearing Faults Based on the Hybrid Deep Autoencoder Network

Intelligent fault diagnosis of rolling bearing under unbalanced samples based on simulation data fusion

Fault diagnosis method of rotating machinery based on MSResNet feature fusion and CAM

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