MMFSL: A Novel Multimodal Few-Shot Learning Framework for Fault Diagnosis of Industrial Bearings

Cheng, Lan; Zhao, An; Guo, Yuanjun; Ren, Mifeng; Yang, Zhile; McLoone, Seán

doi:10.1109/tim.2023.3289549

Cited by 11 publications

(3 citation statements)

References 35 publications

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“…To demonstrate the effectiveness of the proposed model, six models are applied for validation comparison. They are Baseline [25], maximum mean discrepancy (MMD) [26], multi kernel maximum mean discrepancy (MKMMD) [27], correlation alignment (CORAL) [28], domain adversarial neural networks (DANNs) [29], adversarial discriminative domain adaptation (ADDA) [30], and the proposed MPGCTN. Each transfer learning method adopts the same feature extraction network and configuration of the training set and test set to verify the advantages of this method.…”

Section: Analysis Of Experimental Resultsmentioning

confidence: 99%

Multi-Perception Graph Convolution Transfer Network Bearing Fault Diagnosis Method

Pan,

Chen,

Zhao

et al. 2024

Applied Sciences

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Targeting the challenge of variable working conditions in bearing fault diagnosis, most of the fault diagnosis methods based on transfer learning focus on the transfer of knowledge, resulting in a poor diagnosis effect in the target domain. To solve the problem of transfer performance degradation, a multi-perception graph convolution transfer network (MPGCTN) is proposed. The MPGCTN is composed of a graph generation module, graph perception module, and domain discrimination module. In the graph generation module, a one-dimensional convolution neural network (1-D CNN) is used to extract features from the input, and then the structural features of samples are mined in the graph generation layer to construct the sample graph. In the following graph perception module, a multi-perception graph convolution network is designed to model the sample graph and learn the data structure information of the sample. Finally, in the domain discrimination module, the method is used to align the structural differences of the case graphs in different domains. Experimental results from experiments on Case Western Reserve University (CWRU) and Paderborn University (PU) bearing datasets show that the proposed method is effective and superior.

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Section: Analysis Of Experimental Resultsmentioning

confidence: 99%

Multi-Perception Graph Convolution Transfer Network Bearing Fault Diagnosis Method

Pan,

Chen,

Zhao

et al. 2024

Applied Sciences

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“…If the T 2 or SPE statistics exceeded the threshold, it indicated a fault, and the red dashed rectangle was marked as the fault state. From time 0 to 450, it represents the state at normal time, while from time 450 to 1000, it represents the fault state in order to better show the results, the y-axis values were adjusted to make the difference between SPE and T 2 under normal and fault conditions more obvious [38]. During experimental analysis, SPE and T 2 statistics of PT-Informer method were often utilized for fault detection.…”

Section: Data Preprocessingmentioning

confidence: 99%

PT-Informer: A Deep Learning Framework for Nuclear Steam Turbine Fault Diagnosis and Prediction

Zhou,

An,

Yang

et al. 2023

Machines

Self Cite

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The health status of equipment is of paramount importance during the operation of nuclear power plants. The occurrence of faults not only leads to significant economic losses but also poses risks of casualties and even major accidents, with unimaginable consequences. This paper proposed a deep learning framework called PT-Informer for fault prediction, detection, and localization in order to address the challenges of online monitoring of the operating health of nuclear steam turbines. Unlike traditional approaches that involve separate design and execution of feature extraction for fault diagnosis, classification, and prediction, PT-Informer aims to extract fault features from the raw vibration signal and perform ultra-real-time fault prediction prior to their occurrence. Specifically, the encoding and decoding structure in PT-Informer ensures the capture of temporal dependencies between input features, enabling accurate time series prediction. Subsequently, the predicted data are utilized for fault detection using PCA in the PT-Informer framework, aiming to assess the likelihood of equipment failure in the near future. In the event of potential future failures, t-SNE is utilized to project high-dimensional data into a lower-dimensional space, facilitating the identification of clusters or groups associated with different fault types or operational conditions, thereby achieving precise fault localization. Experimental results on a nuclear steam turbine rotor demonstrate that PT-Informer outperformed the traditional GRU with a 4.94% improvement in R2 performance for prediction. Furthermore, compared to the conventional model, the proposed PT-Informer enhanced the fault classification accuracy of the nuclear steam turbine rotor from 97.4% to 99.6%. Various comparative experiments provide strong evidence for the effectiveness of PT-Informer framework in the diagnosis and prediction of nuclear steam turbine.

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“…Additionally, Ref. [18] introduces a multimodal few-shot learning framework adept at handling unbalanced data in industrial bearing fault diagnosis, while Cen et al [19] propose an anomaly detection model for industrial motors that utilizes reinforcement and ensemble learning under few-shot feature conditions. Moreover, methods like meta-transfer learning [20], customized metalearning frameworks [21], and efficient two-stage learning frameworks [22] offer innovative solutions to address domain-shift challenges and enhance feature invariance to data shifts, ultimately improving fault diagnosis performance.…”

Section: Introductionmentioning

confidence: 99%

A Novel Fault Diagnosis Method of High-Speed Train Based on Few-Shot Learning

Wu,

Chen,

Lei

et al. 2024

Entropy

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Ensuring the safe and stable operation of high-speed trains necessitates real-time monitoring and diagnostics of their suspension systems. While machine learning technology is widely employed for industrial equipment fault diagnosis, its effective application relies on the availability of a large dataset with annotated fault data for model training. However, in practice, the availability of informational data samples is often insufficient, with most of them being unlabeled. The challenge arises when traditional machine learning methods encounter a scarcity of training data, leading to overfitting due to limited information. To address this issue, this paper proposes a novel few-shot learning method for high-speed train fault diagnosis, incorporating sensor-perturbation injection and meta-confidence learning to improve detection accuracy. Experimental results demonstrate the superior performance of the proposed method, which introduces perturbations, compared to existing methods. The impact of perturbation effects and class numbers on fault detection is analyzed, confirming the effectiveness of our learning strategy.

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MMFSL: A Novel Multimodal Few-Shot Learning Framework for Fault Diagnosis of Industrial Bearings

Cited by 11 publications

References 35 publications

Multi-Perception Graph Convolution Transfer Network Bearing Fault Diagnosis Method

Multi-Perception Graph Convolution Transfer Network Bearing Fault Diagnosis Method

PT-Informer: A Deep Learning Framework for Nuclear Steam Turbine Fault Diagnosis and Prediction

A Novel Fault Diagnosis Method of High-Speed Train Based on Few-Shot Learning

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