One-stage self-supervised momentum contrastive learning network for open-set cross-domain fault diagnosis

Wang, Weicheng; Wang, Tao; Li, Aimin; Li, Fudong; Chen, Jinglong; Zhang, Tianci

doi:10.1016/j.knosys.2023.110692

Cited by 18 publications

(4 citation statements)

References 23 publications

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“…where Ã = A + I, I is the unit matrix, the node itself is considered, and in order to consider the phenomenon that the feature vectors are continuously summed up when aggregating the features of the neighboring nodes of a node, which leads to a larger feature of the node with more neighboring nodes, symmetric normalization is used in this paper. Constructing a two-layer GCN with ReLU and Softmax as activation functions, the forward propagation formula of the GCN can be expressed in (15) as follows:…”

Section: Node Graph Feature Learning Stagementioning

confidence: 99%

“…Therefore, self-supervised learning based on unlabeled data has received more and more attention and research from scholars. Wang et al [ 15 ] proposed a one-stage self-supervised momentum contrastive learning model (OSSMCL) for open-set cross-domain fault diagnosis. The method is based on momentum encoders of self-supervised contrastive learning to capture distinguishable features between sample pairs and incorporates a one-stage framework of the meta-learning paradigm through which OSSMLC can learn to identify new faults with a small number of labeled samples in the target domain.…”

Section: Introductionmentioning

confidence: 99%

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WPD-Enhanced Deep Graph Contrastive Learning Data Fusion for Fault Diagnosis of Rolling Bearing

et al. 2023

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Rolling bearings are crucial mechanical components in the mechanical industry. Timely intervention and diagnosis of system faults are essential for reducing economic losses and ensuring product productivity. To further enhance the exploration of unlabeled time-series data and conduct a more comprehensive analysis of rolling bearing fault information, this paper proposes a fault diagnosis technique for rolling bearings based on graph node-level fault information extracted from 1D vibration signals. In this technique, 10 categories of 1D vibration signals from rolling bearings are sampled using a sliding window approach. The sampled data is then subjected to wavelet packet decomposition (WPD), and the wavelet energy from the final layer of the four-level WPD decomposition in each frequency band is used as the node feature. The weights of edges between nodes are calculated using the Pearson correlation coefficient (PCC) to construct a node graph that describes the feature information of rolling bearings under different health conditions. Data augmentation of the node graph in the dataset is performed by randomly adding nodes and edges. The graph convolutional neural network (GCN) is employed to encode the augmented node graph representation, and deep graph contrastive learning (DGCL) is utilized for the pre-training and classification of the node graph. Experimental results demonstrate that this method outperforms contrastive learning-based fault diagnosis methods for rolling bearings and enables rapid fault diagnosis, thus ensuring the normal operation of mechanical systems. The proposed WPDPCC-DGCL method offers two advantages: (1) the flexibility of wavelet packet decomposition in handling non-smooth vibration signals and combining it with the powerful multi-scale feature encoding capability of GCN for richer characterization of fault information, and (2) the construction of graph node-level fault samples to effectively capture underlying fault information. The experimental results demonstrate the superiority of this method in rolling bearing fault diagnosis over contrastive learning-based approaches, enabling fast and accurate fault diagnoses for rolling bearings and ensuring the normal operation of mechanical systems.

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Section: Node Graph Feature Learning Stagementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

WPD-Enhanced Deep Graph Contrastive Learning Data Fusion for Fault Diagnosis of Rolling Bearing

et al. 2023

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“…Most of the above models generally focus on inter-class information (such as cross-entropy), while ignoring intraclass information. The metric-based FSL methods effectively extract inter-class and intra-class information by quantifying the similarity between samples, providing new perspectives for classification or prediction tasks, leading to significant advancements [24][25][26][27]. Chen et al [28] introduced a straightforward visual representation contrast learning framework that is less complex and does not necessitate a specialized architecture.…”

Section: Introductionmentioning

confidence: 99%

Contrast learning with hard example mining for few-shot fault diagnosis of rolling bearings

An,

Wang,

Yan

et al. 2024

Meas. Sci. Technol.

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Deep learning has become a powerful technique for effectively extracting features in the field of rolling bearing fault diagnosis. However, in the case of sparse labeled sample data, the feature distribution is quite different, and simple samples are used repeatedly, leading to challenges in fault diagnosis, including insufficient feature learning and inaccurate fault classification. Therefore, a contrastive learning method is proposed for few-shot scenarios, incorporating hard example mining to address these challenges. In this framework, the similarities and differences of samples are used to construct positive and negative sample pairs, which help deepen the model to recognize and learn the internal correlation and pattern between samples. On this basis, the hard example mining strategy is adopted to further optimize the sample distribution in the feature space. These learned features are then classified by a classifier. Experimental results show that the fault diagnosis model proposed in this paper achieves high accuracy.

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“…Small sample fault diagnosis has attracted the attention of researchers and several achievements have been made. With limited labeled samples, many methods have been studied, including transfer learning [11], self-supervised learning [12] and meta-learning [13]. Among them, transfer learning [14,15] uses existing knowledge from sufficient labeled samples to alleviate the need for data in the target domain.…”

Section: Introductionmentioning

confidence: 99%

Semi-supervised adaptive anti-noise meta-learning for few-shot industrial gearbox fault diagnosis

Hu,

Xie

2024

Meas. Sci. Technol.

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Real-time and accurate predictive maintenance of industrial equipment is fundamental for ensuring the safety and stability of advanced manufacturing processes. Current fault diagnosis methods based on data mining rely on a large number of labeled samples, and obtaining sufficient labeled data for diagnosing industrial equipment faults is challenging. Meta-learning can achieve the diagnosis of few-shot samples to a certain extent, but the effect is not ideal. Semi-supervision can effectively leverage a large number of unlabeled samples, which is of great practical significance for handling scenarios involving limited labeled samples. However, noise interference can occur when unlabeled samples appear that do not belong to known categories. Therefore, this study proposes adaptive semi-supervised meta-learning networks for noisy few-shot gearbox fault diagnosis. Firstly, a residual network with a Morlet Wavelet layer is used to extract signal features. Next, sample-level attention is defined to select unlabeled samples that are more similar to labeled sample prototypes, thereby reducing the influence of noisy samples. The adaptive metric is used to obtain the relational distance functions of labeled samples and unlabeled samples. Adaptive semi-supervised meta-learning networks uses unlabeled data to refine prototypes for better fault diagnosis. The effectiveness and anti-noise performance of the proposed method are verified by using two gearbox datasets with various few-shot noise scenarios.

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One-stage self-supervised momentum contrastive learning network for open-set cross-domain fault diagnosis

Cited by 18 publications

References 23 publications

WPD-Enhanced Deep Graph Contrastive Learning Data Fusion for Fault Diagnosis of Rolling Bearing

WPD-Enhanced Deep Graph Contrastive Learning Data Fusion for Fault Diagnosis of Rolling Bearing

Contrast learning with hard example mining for few-shot fault diagnosis of rolling bearings

Semi-supervised adaptive anti-noise meta-learning for few-shot industrial gearbox fault diagnosis

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