Multi-Level Federated Network Based on Interpretable Indicators for Ship Rolling Bearing Fault Diagnosis

Wang, Shuangzhong; Zhang, Ying

doi:10.3390/jmse10060743

Cited by 6 publications

(7 citation statements)

References 39 publications

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“…In this case, an interpretable approach is necessary to prevent malicious attacks on the central server if a hostile client knows the gradient parameters of the other clients. In the same context, as the authors correctly point out in [22], how to build a federal network structure in a multi-level federated center still needs to be further explored in future research.…”

Section: Interpretable MLmentioning

confidence: 96%

“…Extension to more complex orientations [20] Security and privacy FL-aided anti-tampering blockchain technology Extension to more complex orientations [21] Fault detection Adaptive FL Convergence improvement [22] Fault detection Interpretable FL Network in a multi-level federated center [23] Underwater applications Federated meta-learning Limited number of nodes…”

Section: Fl-based Gradient Aggregation and Credibility Mechanismsmentioning

confidence: 99%

“…In a similar setting, ref. [22] presented an interpretable multi-level FL network for ship fault diagnosis, dealing with non-i.i.d. data threatening FL performance.…”

Section: Fault Detectionmentioning

confidence: 99%

See 2 more Smart Citations

Federated Learning for Maritime Environments: Use Cases, Experimental Results, and Open Issues

Giannopoulos,

Gkonis,

Bithas

et al. 2024

JMSE

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Maritime transportation is crucial for global trade and responsible for the majority of goods movement worldwide. The optimization of maritime operations is challenged by the complexity and heterogeneity of maritime nodes. This paper presents the emerging deployment of federated learning (FL) in maritime environments to address these challenges. FL enables decentralized machine learning model training, ensuring data privacy and security while overcoming issues associated with non-i.i.d. data. This paper explores various maritime use cases, including fuel consumption reduction, predictive maintenance, and just-in-time arrival. Experimental results using real datasets demonstrate the superiority of FL in predicting the fuel consumption of large cargo ships in terms of accuracy and spatiotemporal complexity over traditional collaborative machine learning approaches. The findings indicate that FL can significantly improve the performance of fuel consumption models in a collaborative way, while ensuring data privacy preservation and no data transmission during the learning process. Finally, this paper discusses open issues and future research directions necessary for the widespread adoption of FL in maritime transportation and settings.

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Section: Interpretable MLmentioning

confidence: 96%

Section: Fl-based Gradient Aggregation and Credibility Mechanismsmentioning

confidence: 99%

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Federated Learning for Maritime Environments: Use Cases, Experimental Results, and Open Issues

Giannopoulos,

Gkonis,

Bithas

et al. 2024

JMSE

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“…Solving the problem of 'data islands' and enabling the application of deep learning models to actual reciprocating machinery fault diagnosis is a pressing challenge to be solved [18]. Federated learning, which is first proposed by Google in 2016, allows participants to jointly establish a deep learning model without sharing data, providing a new way to solve the problem of 'data island' [19]. The federated learning process is shown in figure 1.…”

Section: Introductionmentioning

confidence: 99%

Improved lightweight federated learning network for fault feature extraction of reciprocating machinery

Zhang,

Duan,

et al. 2024

Meas. Sci. Technol.

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he working environment of reciprocating machinery is complex, characterized by nonlinear and non-stationary signals. Deep learning can solve the above problems, but it has its own problems such as complex model and large amount of parameters. Additionally, privacy considerations among enterprises prevent data sharing, leading to the emergence of "data islands" and inadequate training of deep learning models. Based on the above analysis, this paper proposes a reciprocating mechanical feature extraction method based on an improved federated lightweight network. A lightweight network SqueezeNet model is used to solve the problems such as long training time of deep learning. By establishing a federated learning framework, the reciprocating mechanical data can be collectively diagnosed across various enterprises, thereby addressing the problem of limited model training caused by insufficient data. Furthermore, to enhance the accuracy of network training and diagnosis, modifications are made to the SqueezeNet network to reduce the number of model parameters while increasing the number and variety of feature extractions. Experimental results demonstrate that when the number of 1×1 and 3×3 channels is 1 to 7, the fault diagnosis accuracy is the highest, about 97.96%, which enriches the categories of feature extraction. The number of parameters in In-SqueezeNet is 56% of that in SqueezeNet network model, and the training time is reduced by nearly 15%. The fault diagnosis accuracy is increased from 95.1% to 97.3%, and the diversity of extracted features is increased. Compared with other network models such as ResNet, the improved lightweight federated learning network has a fault diagnosis accuracy of 96.6%, an improvement of 10.6%. At the same time, the training time was reduced to 1982s, a reduction of about 41.5%. The validity of the proposed model is further verified.

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“…Zhu et al pointed out that the shared gradients may still lead to data leakage [9]. Wang and Zhang proposed a multi-level federal structure that can effectively prevent data leakage from gradient information [10]. Zhang et al solved the problem of insufficient samples for short-circuit fault detection during motor operation based on FL [11].…”

Section: Introductionmentioning

confidence: 99%

FedSiM: a similarity metric federal learning mechanism based on stimulus response method with Non-IID data

Wang,

Zhang

2023

Meas. Sci. Technol.

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FL (Federal Learning) based on parameter sharing under the assumption that the data obey IID (Independent Identical Distribution) has already achieved good results in areas such as fault diagnosis. Data collected by the decentralized devices often do not obey IID. However, when faced with the scenario of client data obeying Non-IID distribution, its diagnostic accuracy is usually weak. Based on this, we did an investigation on the mechanism causing this phenomenon and found that it was attributed to the weight shift of the network. Therefore, based on the stimulus response principle, we investigated the network similarity of federal clients under different data distributions and explain the reasons for the weight shift. Firstly, it was pointed out that there are differences in the regions where the network is activated when performing different classification tasks. Then, FedSiM (Similarity Metric Federal Learning) was proposed based on the principle that there are differences between the activated regions. Finally, experiments were designed on the Case Western Reserve University bearing failure dataset for different degrees of independent identical distribution cases. The results show that FedSiM can improve the diagnostic accuracy by 15.8 percentage points in the case of Non-IID, and a few shared FedSiM methods to further improve the accuracy were also given.

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Multi-Level Federated Network Based on Interpretable Indicators for Ship Rolling Bearing Fault Diagnosis

Cited by 6 publications

References 39 publications

Federated Learning for Maritime Environments: Use Cases, Experimental Results, and Open Issues

Federated Learning for Maritime Environments: Use Cases, Experimental Results, and Open Issues

Improved lightweight federated learning network for fault feature extraction of reciprocating machinery

FedSiM: a similarity metric federal learning mechanism based on stimulus response method with Non-IID data

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