A Domain Adaption ResNet Model to Detect Faults in Roller Bearings Using Vibro-Acoustic Data

Liu, Yi; Xiang, Hang; Jiang, Zhansi; Xiang, Jiawei

doi:10.3390/s23063068

Cited by 7 publications

(4 citation statements)

References 41 publications

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“…(1) MLP This model consisted of linear layers, and the ReLU activation function was added between layers to improve the generalization ability of this model [62]. The structure of the MLP was as follows: {Input (75 × 14), linear (75 × 14, 512), linear (512, 256), linear (256, Class)}.…”

Section: Multi-task Learning Vs Single-task Learningmentioning

confidence: 99%

Gas Sensor Array Fault Diagnosis Based on Multi-Dimensional Fusion, an Attention Mechanism, and Multi-Task Learning

Huang,

Wang,

Chen

et al. 2023

Sensors

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With the development of gas sensor arrays and computational technology, machine olfactory systems have been widely used in environmental monitoring, medical diagnosis, and other fields. The reliable and stable operation of gas sensing systems depends heavily on the accuracy of the sensors outputs. Therefore, the realization of accurate gas sensor array fault diagnosis is essential to monitor the working status of sensor arrays and ensure the normal operation of the whole system. The existing methods extract features from a single dimension and require the separate training of models for multiple diagnosis tasks, which limits diagnostic accuracy and efficiency. To address these limitations, for this study, a novel fault diagnosis network based on multi-dimensional feature fusion, an attention mechanism, and multi-task learning, MAM-Net, was developed and applied to gas sensor arrays. First, feature fusion models were applied to extract deep and comprehensive features from the original data in multiple dimensions. A residual network equipped with convolutional block attention modules and a Bi-LSTM network were designed for two-dimensional and one-dimensional signals to capture spatial and temporal features simultaneously. Subsequently, a concatenation layer was constructed using feature stitching to integrate the fault details of different dimensions and avoid ignoring useful information. Finally, a multi-task learning module was designed for the parallel learning of the sensor fault diagnosis to effectively improve the diagnosis capability. The experimental results derived from using the proposed framework on gas sensor datasets across different amounts of data, balanced and unbalanced datasets, and different experimental settings show that the proposed framework outperforms the other available methods and demonstrates good recognition accuracy and robustness.

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Section: Multi-task Learning Vs Single-task Learningmentioning

confidence: 99%

Gas Sensor Array Fault Diagnosis Based on Multi-Dimensional Fusion, an Attention Mechanism, and Multi-Task Learning

Huang,

Wang,

Chen

et al. 2023

Sensors

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show abstract

“…Secondly, a preceding feature extractor composed of convolutional and pooling layers is employed to compute shallow features suitable for speech quality prediction from mel-spectrogram segments. Lastly, ResNet is utilized to extract spatial features from the shallow features, and BiLSTM is used to extract temporal features from the shallow features [19]. The extracted features from both ResNet and BiLSTM are concatenated and fused, effectively improving the accuracy of air traffic control speech quality evaluation.…”

Section: Related Workmentioning

confidence: 99%

Non-Intrusive Air Traffic Control Speech Quality Assessment with ResNet-BiLSTM

Wu,

Li,

2023

Applied Sciences

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In the current field of air traffic control speech, there is a lack of effective objective speech quality evaluation methods. This paper proposes a new network framework based on ResNet–BiLSTM to address this issue. Firstly, the mel-spectrogram of the speech signal is segmented using the sliding window technique. Next, a preceding feature extractor composed of convolutional and pooling layers is employed to extract shallow features from the mel-spectrogram segment. Then, ResNet is utilized to extract spatial features from the shallow features, while BiLSTM is used to extract temporal features, and these features are horizontally concatenated. Finally, based on the concatenated spatiotemporal features, the final speech quality score is computed using fully connected layers. We conduct experiments on the air traffic control speech database and compare the objective scoring results with the subjective scoring results. The experimental results demonstrate that the proposed method has a high correlation with the mean opinion score (MOS) of air traffic control speech.

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“…proposed a fault diagnosis method for rolling bearing faults based on ICEEMDAN combined with the Hilbert transform (ICEEMDAN-Hilbert) and a residual network (ResNet) [11]. Based on acoustic and vibration data, Liu et al constructed a domain adaptive residual neural network (DA-ResNet) model based on maximum mean difference (MMD) and residual connections for the cross-domain diagnosis of rolling bearings [12]. Hao et al proposed replacing the fully connected layer portion of the traditional RESNET with global average pooling (GAP) technology.…”

Section: Introductionmentioning

confidence: 99%

Research on Rolling Bearing Fault Diagnosis Method Based on ECA-MRANet

Wang,

Gao,

Shan

et al. 2024

Applied Sciences

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Most fault diagnosis models use a single input and have weak generalization performance. In order to obtain more fault information, a fault diagnosis method based on a Multi-channel Residual Attention Network with Efficient Channel Attention (ECA-MRANet) is proposed in this paper. In this method, the original time domain signal is first processed by a multi-domain transform, the result of which is input to the MRANet for feature extraction. Finally, the extracted features are fused by ECA to realize fault identification. The experimental results show that the proposed method can enhance the ability of the network to discriminate key features, and shows good generalization performance under different working conditions and with small-sample transfer between data sets.

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A Domain Adaption ResNet Model to Detect Faults in Roller Bearings Using Vibro-Acoustic Data

Cited by 7 publications

References 41 publications

Gas Sensor Array Fault Diagnosis Based on Multi-Dimensional Fusion, an Attention Mechanism, and Multi-Task Learning

Gas Sensor Array Fault Diagnosis Based on Multi-Dimensional Fusion, an Attention Mechanism, and Multi-Task Learning

Non-Intrusive Air Traffic Control Speech Quality Assessment with ResNet-BiLSTM

Research on Rolling Bearing Fault Diagnosis Method Based on ECA-MRANet

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