Publication type: | Conference other |
Type of review: | Editorial review |
Title: | Scalable deployment of deep learning algorithms for predictive maintenance in commercial machine fleets : bridging the research-industry gap |
Authors: | Goren Huber, Lilach |
et. al: | No |
Conference details: | 14th Annual Conference of the PHM Society, Nashville, USA, 1-4 November 2022 |
Issue Date: | 1-Nov-2022 |
Language: | English |
Subjects: | Deep learning; Predictive maintenance; Anomaly detection; Wind turbine; Upscaling; Condition based maintenance; Data scarcity; Fault detection |
Subject (DDC): | 006: Special computer methods |
Abstract: | Developing deep learning algorithms for predictive maintenance of industrial systems is a growing trend in numerous application fields. Whereas applied research methods have been rapidly advancing, implementations in commercial systems are still lagging behind. One of the main reasons for this delay is the fact that most methodological advances have been focusing on the development of data-driven algorithms for fault detection, diagnosis, or prognosis, ignoring some of the crucial aspects that are required for scaling these algorithms to large fleets of multi-component heterogeneous machines under varying operating conditions, and making sure that their implementation is technically feasible. In this tutorial, we will elaborate on some of these aspects and discuss possible approaches to address them. We will provide the background to data analytical techniques that enable the scalable deployment of deep learning algorithms in commercial machine fleets. Some examples are transfer learning, fleet-level algorithms, physics-informed deep learning, and uncertainty quantification. We will demonstrate these general concepts using concrete use-cases that apply them to operational data from commercial machine fleets. |
URI: | https://phm2022.phmsociety.org/north-america/tutorials/ https://digitalcollection.zhaw.ch/handle/11475/30283 |
Fulltext version: | Published version |
License (according to publishing contract): | Licence according to publishing contract |
Departement: | School of Engineering |
Organisational Unit: | Institute of Data Analysis and Process Design (IDP) |
Appears in collections: | Publikationen School of Engineering |
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Goren Huber, L. (2022, November 1). Scalable deployment of deep learning algorithms for predictive maintenance in commercial machine fleets : bridging the research-industry gap. 14th Annual Conference of the PHM Society, Nashville, USA, 1-4 November 2022. https://phm2022.phmsociety.org/north-america/tutorials/
Goren Huber, L. (2022) ‘Scalable deployment of deep learning algorithms for predictive maintenance in commercial machine fleets : bridging the research-industry gap’, in 14th Annual Conference of the PHM Society, Nashville, USA, 1-4 November 2022. Available at: https://phm2022.phmsociety.org/north-america/tutorials/.
L. Goren Huber, “Scalable deployment of deep learning algorithms for predictive maintenance in commercial machine fleets : bridging the research-industry gap,” in 14th Annual Conference of the PHM Society, Nashville, USA, 1-4 November 2022, Nov. 2022. [Online]. Available: https://phm2022.phmsociety.org/north-america/tutorials/
GOREN HUBER, Lilach, 2022. Scalable deployment of deep learning algorithms for predictive maintenance in commercial machine fleets : bridging the research-industry gap. In: 14th Annual Conference of the PHM Society, Nashville, USA, 1-4 November 2022 [online]. Conference presentation. 1 November 2022. Verfügbar unter: https://phm2022.phmsociety.org/north-america/tutorials/
Goren Huber, Lilach. 2022. “Scalable Deployment of Deep Learning Algorithms for Predictive Maintenance in Commercial Machine Fleets : Bridging the Research-Industry Gap.” Conference presentation. In 14th Annual Conference of the PHM Society, Nashville, USA, 1-4 November 2022. https://phm2022.phmsociety.org/north-america/tutorials/.
Goren Huber, Lilach. “Scalable Deployment of Deep Learning Algorithms for Predictive Maintenance in Commercial Machine Fleets : Bridging the Research-Industry Gap.” 14th Annual Conference of the PHM Society, Nashville, USA, 1-4 November 2022, 2022, https://phm2022.phmsociety.org/north-america/tutorials/.
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