Reinforcement learning multi-agent system for faults diagnosis of mircoservices in industrial settings

Belhadi, Asma; Djenouri, Youcef; Srivastava, Gautam; Lin, Jerry Chun‐Wei

doi:10.1016/j.comcom.2021.07.010

Cited by 16 publications

(4 citation statements)

References 33 publications

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“…Meanwhile, data-driven models from multiple participants can combine for ensemble learning [28]. Besides, in a multiagent system, local models can merge into a global optimal model through reinforcement learning [29] or knowledge graph [30][31][32][33]. Moreover, Wang et al [34] applied a blockchain in collaborative fault diagnosis, where the blockchain provided a decentralised platform and claimed the immutable ownership of data and knowledge of each participant.…”

Section: Collaborative Fault Diagnosismentioning

confidence: 99%

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Privacy‐preserving gradient boosting tree: Vertical federated learning for collaborative bearing fault diagnosis

Xia

Zheng

et al. 2022

IET Collab Intel Manufact

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Data-driven fault diagnosis approaches have been widely adopted due to their persuasive performance. However, data are always insufficient to develop effective fault diagnosis models in real manufacturing scenarios. Despite numerous approaches that have been offered to mitigate the negative effects of insufficient data, the most challenging issue lies in how to break down the data silos to enlarge data volume while preserving data privacy. To address this issue, a vertical federated learning (FL) model, privacy-preserving boosting tree, has been developed for collaborative fault diagnosis of industrial practitioners while maintaining anonymity. Only the model information will be shared under the homomorphic encryption protocol, safeguarding data privacy while retaining high accuracy. Besides, an Autoencoder model is provided to encourage practitioners to contribute and then improve model performance. Two bearing fault case studies are conducted to demonstrate the superiority of the proposed approach by comparing it with typical scenarios. This present study's findings offer industrial practitioners insights into investigating the vertical FL in fault diagnosis.

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Section: Collaborative Fault Diagnosismentioning

confidence: 99%

“…Meanwhile, data‐driven models from multiple participants can combine for ensemble learning [28]. Besides, in a multi‐agent system, local models can merge into a global optimal model through reinforcement learning [29] or knowledge graph [30–33]. Moreover, Wang et al.…”

Section: Related Workmentioning

confidence: 99%

Privacy‐preserving gradient boosting tree: Vertical federated learning for collaborative bearing fault diagnosis

Xia

Zheng

et al. 2022

IET Collab Intel Manufact

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“…Although these methods can alleviate the cluster imbalance to some extent, it inevitably leads to either excessive data matching or data loss. Deep reinforcement learning has advantages in fault diagnosis, especially in solving complex operating conditions and imbalanced data [8][9][10]. However, most of the rewards obtained by the agent are based on whether the action is consistent with the sample label, so it still depends on the label to a certain extent.…”

Section: Introductionmentioning

confidence: 99%

A deep reinforcement learning-based fault diagnosis algorithm for unlabeled and imbalanced data

Li,

Dong,

Zhang

et al. 2024

Fourth International Conference on Mechanical Engineering, Intelligent Manufacturing, and Automation Technology (MEMAT 2023)

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Aiming at the problems that most of the rotating machinery fault diagnosis algorithms are oriented to labeled data, the cost of label information collection is high and the number of fault samples is far less than that of normal samples, this paper proposes a deep reinforcement learning based rotating machinery fault diagnosis method for unlabeled and imbalanced data. The method leverages the relationship between samples and cluster centers to provide feedback in the form of reward information. It employs mechanical vibration signal samples as model state inputs and fault type selection as selectable actions for the agent. An interactive environment is constructed, allowing the agent to observe, act, and receive rewards in the absence of fault labels. Additionally, a deep neural network is utilized to approximate the Q function, which is then maximized to obtain the optimal policy, enabling fault diagnosis in the absence of labeled data. Through validation with rolling bearing fault data, the proposed method demonstrates a 15% improvement in diagnostic accuracy compared to the K-Means algorithm when dealing with imbalanced data.

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“…In [13], an UIO is constructed for linear MAS with actuator fault only according to the relative output information. In addition, some other algorithms have been presented in [14][15][16][17][18][19][20] for the problem of fault diagnosis in multi-agent systems.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis and Fault-Tolerant Control for Leader-Follower Multi-Agent Systems with Time Delay

Zhang

Yao

2022

Journal of Control Science and Engineering

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Under the guidance of the directed graph, this paper studies the fault diagnosis (FD) and fault-tolerant control (FTC) of the leader-follower multi-agent systems (MASs) with time delay. Actuator faults and disturbances are considered in this study, which make the work of this paper more challenging. Firstly, a robust distributed observer on the basis of the relative estimation error is put forward to obtain the fault estimation information. Then, after getting the fault information obtained by the observer, a dynamic compensation FTC protocol on the basis of the relative output tracking error is developed to make the output of followers can track the leader's output. Finally, the proposed algorithm is verified by an example and satisfactory results are obtained.

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Reinforcement learning multi-agent system for faults diagnosis of mircoservices in industrial settings

Cited by 16 publications

References 33 publications

Privacy‐preserving gradient boosting tree: Vertical federated learning for collaborative bearing fault diagnosis

Privacy‐preserving gradient boosting tree: Vertical federated learning for collaborative bearing fault diagnosis

A deep reinforcement learning-based fault diagnosis algorithm for unlabeled and imbalanced data

Fault Diagnosis and Fault-Tolerant Control for Leader-Follower Multi-Agent Systems with Time Delay

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