A federated transfer learning method with low-quality knowledge filtering and dynamic model aggregation for rolling bearing fault diagnosis

Wang, Ran; Yan, Fucheng; Yu, Liang; Shen, Changqing; Xiong, Hu; Chen, Jin

doi:10.1016/j.ymssp.2023.110413

Cited by 26 publications

(3 citation statements)

References 35 publications

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“…The second LVM used in this work is an ICA methodology based on the negentropy-based ICA formulation proposed in Hyvärinen [38] . This model is used as the interpretationfocused LVM and is developed using online training.…”

Section: Interpretation-focused Lvmsmentioning

confidence: 99%

“…In conventional CM applications, learning-based models are applied in various learning settings: (i) supervised learning [35][36][37] , (ii) transfer learning [38][39][40][41] , and (iii) unsupervised learning [42,43] . Supervised learning is a popular learningbased setting used to automatically identify fault signatures to classify different potential fault states, e.g., Huang et al [44].…”

Section: Introductionmentioning

confidence: 99%

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Temporally-preserving latent variable models: Offline and online training for reconstruction and interpretation of fault data for gearbox condition monitoring

Balshaw,

Heyns,

Wilke

et al. 2024

JDMD

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Latent variable models can effectively determine the condition of essential rotating machinery without needing labelled data. These models analyse vibration data via an unsupervised learning strategy. Temporal preservation is necessary to obtain an informative latent manifold for the fault diagnosis task. In a temporal-preserving context, two approaches exist to develop a condition-monitoring methodology: offline and online. For latent variable models, the available training modes are no different. While many traditional methods use offline training, online training can dynamically adjust the latent manifold, possibly leading to better fault signature extraction from the vibration data. This study explores online training using temporal-preserving latent variable models. Within online training, there are two main methods: one focuses on reconstructing data and the other on interpreting the data components. Both are considered to evaluate how they diagnose faults over time. Using two experimental datasets, the study confirms that models from both training modes can detect changes in machinery health and identify faults even under varying conditions. Importantly, the complementarity of offline and online models is emphasised, reassuring their versatility in fault diagnostics. Understanding the implications of the training approach and the available model formulations is crucial for further research in latent variable model-based fault diagnostics. Conflict of Interest Statement The authors declare no conflicts of interest.

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Section: Interpretation-focused Lvmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Temporally-preserving latent variable models: Offline and online training for reconstruction and interpretation of fault data for gearbox condition monitoring

Balshaw,

Heyns,

Wilke

et al. 2024

JDMD

View full text Add to dashboard Cite

show abstract

“…Wu et al [15] developed conditional distribution-guided adversarial transfer learning network with multi-domain domains for fault diagnosis of bearing installed on different machines. Wang et al [16] applied federated transfer learning method with low-quality knowledge filtering and dynamic model aggregation for rolling bearing fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Interpretable multi-domain meta-transfer learning for few-shot fault diagnosis of rolling bearing under variable working conditions

Che,

Zhang,

Wang

et al. 2024

Meas. Sci. Technol.

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To address the challenges of accurately diagnosing few-shot fault samples obtained from rolling bearings under variable operating conditions, as well as the issues of black box nature and delayed feedback to guide fault handling in intelligent diagnostic models, this paper proposes an interpretable multi-domain meta-transfer learning method. Firstly, vibration monitoring data of rolling bearings under different operating conditions are collected, and time-frequency domain features are extracted to construct multi-channel one-dimensional temporal samples as inputs. A multi-domain meta-transfer learning framework based on deep convolutional neural networks is then built to perform few-shot learning with multiple tasks under different operating conditions. The output results are reverse-reconstructed through a fusion hierarchical class activation mapping, and the feature maps are assigned different weights to obtain saliency maps corresponding to the inputs, thus improving the interpretability of the output results. Finally, the dataset of bearing vibration data under time-varying rotational speed conditions is used to validate the effectiveness of the proposed method. Experimental results show that the proposed method can achieve accurate fault diagnosis results under variable operating conditions with few-shot samples, and the diagnosis results can be fed back to the input for decision-making, enhancing the interpretability of the model. Compared with other models, it also demonstrates better robustness and accuracy.

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Artificial intelligence and edge computing for machine maintenance-review

Bala,

Rashid,

Ismail

et al. 2024

Artif Intell Rev

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Industrial internet of things (IIoT) has ushered us into a world where most machine parts are now embedded with sensors that collect data. This huge data reservoir has enhanced data-driven diagnostics and prognoses of machine health. With technologies like cloud or centralized computing, the data could be sent to powerful remote data centers for machine health analysis using artificial intelligence (AI) tools. However, centralized computing has its own challenges, such as privacy issues, long latency, and low availability. To overcome these problems, edge computing technology was embraced. Thus, instead of moving all the data to the remote server, the data can now transition on the edge layer where certain computations are done. Thus, access to the central server is infrequent. Although placing AI on edge devices aids in fast inference, it poses new research problems, as highlighted in this paper. Moreover, the paper discusses studies that use edge computing to develop artificial intelligence-based diagnostic and prognostic techniques for industrial machines. It highlights the locations of data preprocessing, model training, and deployment. After analysis of several works, trends of the field are outlined, and finally, future research directions are elaborated

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A federated transfer learning method with low-quality knowledge filtering and dynamic model aggregation for rolling bearing fault diagnosis

Cited by 26 publications

References 35 publications

Temporally-preserving latent variable models: Offline and online training for reconstruction and interpretation of fault data for gearbox condition monitoring

Temporally-preserving latent variable models: Offline and online training for reconstruction and interpretation of fault data for gearbox condition monitoring

Interpretable multi-domain meta-transfer learning for few-shot fault diagnosis of rolling bearing under variable working conditions

Artificial intelligence and edge computing for machine maintenance-review

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