Secure and communications‐efficient collaborative prognosis

Dhada, Maharshi; Jain, Amit Kumar; Herrera, Manuel; Hernández, Marco Pérez; Parlikad, Ajith Kumar

doi:10.1049/iet-cim.2020.0035

Cited by 11 publications

(9 citation statements)

References 25 publications

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“…One of the primary requirements of data-driven prognosis is for data-sets generated by the participating digital twins to be statistically homogeneous [19]. In the context of PdM, the data-sets generated by two digital twins are statistically homogeneous if they contain similar failure patterns of the corresponding assets working under the same operational conditions.…”

Section: B Privacy Preserving Collaborative Pdmmentioning

confidence: 99%

“…While existing approaches are few and limited to the application of FL [19] [20] [21] [22] without considering the computational challenges in manufacturing environment, it has been observed that these solutions are able to tackle the data privacy issue in collaborative prognosis. The collaborative prognosis mechanism proposed in [19] deploys a digital twin based FL solution that predicts the RUL of an aircraft engine. A LSTM based predictive model is used for local training and traditional federated averaging technique is used for model aggregation.…”

Section: B Privacy Preserving Collaborative Pdmmentioning

confidence: 99%

“…The approach proposed in [22] is designed for cross-silo FL setting. Whereas the one proposed in [19] utilises LSTM as the global learning model which is not suitable for cross-device FL setting. [20] utilises a lightweight MLP based global model (suitable for edge devices) but assumes that the edge devices are equipped with constant availability of computational and communication resources.…”

Section: B Privacy Preserving Collaborative Pdmmentioning

confidence: 99%

“…This section investigates the efficiency of SplitPred by simulating a collaborative prognosis scenario using publicly available benchmark C-MAPSS data-set [9]. We use a multiagent system architecture as described in [19] since the architecture has been shown to be well suited for real world industries. Here, the notion of 'agent' refers to a digital agent representing a manufacturing asset and is equipped with storing historical failure data and processing capabilities.…”

Section: Performance Evaluationmentioning

confidence: 99%

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Privacy-Aware Resource Sharing in Cross-Device Federated Model Training for Collaborative Predictive Maintenance

Bharti¹,

McGibney

2021

IEEE Access

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The proliferation of Industry 4.0 has made modern industrial assets a rich source of data that can be leveraged to optimise operations, ensure efficiency, and minimise maintenance costs. The availability of data is advantageous for asset management, however, attempts to maximise the value of this data often fall short due to additional constraints, such as privacy concerns and data stored in distributed silos that is difficult to access and share. Federated Learning (FL) has been explored to address these challenges and has been demonstrated to provide a mechanism that allows highly distributed data to be mined in a privacy-preserving manner and offering new opportunities for a collaborative approach to asset management. Despite the benefits, FL has some challenges that need to be overcome to make it fully compatible for asset management or more specifically predictive maintenance applications. FL requires a set of clients that participate in the model training process, however, orchestration, device heterogeneity and scalability can hinder the speed and accuracy in the context of collaborative predictive maintenance. To address this challenge, this work proposes a split-learning-based framework (SplitPred) that enables FL clients to maximise available resources within their local network without compromising the benefits of a FL approach (i.e., privacy and shared learning). Experiments performed on the benchmark C-MAPSS dataset demonstrate the advantage of applying SplitPred in the FL process in terms of efficient use of resources, i.e., model convergence time, accuracy, and network load.

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Section: B Privacy Preserving Collaborative Pdmmentioning

confidence: 99%

Section: B Privacy Preserving Collaborative Pdmmentioning

confidence: 99%

Section: B Privacy Preserving Collaborative Pdmmentioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

See 2 more Smart Citations

Privacy-Aware Resource Sharing in Cross-Device Federated Model Training for Collaborative Predictive Maintenance

Bharti¹,

McGibney

2021

IEEE Access

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“…In these circumstances, collaborative prognosis is a technique that enables the assets with insufficient failures to identify similar other assets in the fleet and learn from their data. State-of-the art collaborative prognosis involves clustering similar assets, followed by exchanging the failure data within these clusters [1], [2].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Agent Deployment Strategies for Collaborative Prognosis

Dhada

Hernández

Palau

et al. 2021

2021 IEEE International Conference on Prognostics and Health Management (ICPHM)

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Collaborative prognosis is a technique that enables the industrial assets to learn from similar other assets in a fleet, and improve their data-driven prognosis models. When collaborative prognosis is implemented in a computationally distributed framework, each asset is monitored by its corresponding Digital Twin agent. Distributed collaborative prognosis is particularly beneficial for high value assets where the communication and the processing costs are negligible compared to the maintenance costs. This paper analyses the effects of Digital Twin deployment strategies on the effectiveness of predictive maintenance activities relying on distributed collaborative prognosis. Distributed and heterarchical multi-agent system architectures are analysed for large fleets of assets, with varying failure rates and noise levels in the failure data. The results show that no single architecture or deployment strategy can be deemed best across all failure rates and noise levels. The conclusion derived in this paper provides guidance to the asset owners to choose the most suitable combination for a given application.

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The Non-ergodicity of Assets

Márquez

2022

Springer Series in Reliability Engineering

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Secure and communications‐efficient collaborative prognosis

Cited by 11 publications

References 25 publications

Privacy-Aware Resource Sharing in Cross-Device Federated Model Training for Collaborative Predictive Maintenance

Privacy-Aware Resource Sharing in Cross-Device Federated Model Training for Collaborative Predictive Maintenance

Comparison of Agent Deployment Strategies for Collaborative Prognosis

The Non-ergodicity of Assets

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