A Framework for Automated Collaborative Fault Detection in Large-Scale Vehicle Networks

Maroli, John M.; Özgüner, Ü.; Redmill, Keith

doi:10.1109/ivs.2019.8814176

Cited by 1 publication

(1 citation statement)

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“…Zhao, Li, Lu, Lv, Gu & Shang (2020) implemented a fault detection model using collaborative filtering techniques for detecting an incipient fault in large-scale solar farms by sharing current data among photovoltaic systems. Maroli, Özgüner & Redmill (2019) propose a collaborative fault detection framework for largescale vehicle networks using an echo state network. Ng & Srinivasan (2010) developed a multi-agent-based collaborative fault detection and identification system for application in chemical process plants and combines various heterogeneous methods to maximize performance using information fusion.…”

Section: Literature Reviewmentioning

confidence: 99%

Collaborative Prognostics for Machine Fleets Using a Novel Federated Baseline Learner

2021

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Difficulty in obtaining enough run-to-fail datasets is a major barrier that impedes the widespread acceptance of Prognostic and Health Management (PHM) technology in many applications. Recent progress in federated learning demonstrates great potential to overcome such difficulty because it allows one to train PHM models based on distributed databases without direct data sharing. Therefore, this technology can overcome local data scarcity challenges by training the PHM model based on multi-party databases. To demonstrate the ability of federated learning to enhance the robustness and reliability of PHM models, this paper proposes a novel federated Gaussian Mixture Model (GMM) algorithm to build universal baseline models based on distributed databases. A systematic methodology to perform collaborative prognostics is further presented using the proposed federated GMM algorithm. The usefulness and performance are validated through a simulated dataset and the NASA Turbofan Engine Dataset. The proposed federated approach with parameter sharing is shown to perform at par with the traditional approach with data sharing. The proposed model further demonstrates improved robustness of predictions made collaboratively keeping the data private compared to local predictions. Federated collaborative learning can serve as a catalyst for the adaptation of business models based on the servitization of assets in the era of Industry 4.0. The methodology facilitates effective learning of asset health conditions for data-scarce organizations by collaborating with other organizations preserving data privacy. This is most suitable for a servitization model for Overall Equipment Manufacturers who sell to multiple organizations.

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Section: Literature Reviewmentioning

confidence: 99%