Publication type: Conference other
Type of review: Not specified
Title: Scaling-up deep learning based predictive maintenance for commercial machine fleets : a case study
Authors: Goren Huber, Lilach
et. al: No
Conference details: 9th Swiss Conference on Data Science (SDS), Lucerne, Switzerland, 22.-23. Juni 2022
Issue Date: 23-Jun-2022
Language: English
Subjects: Predictive maintenance; Deep learning; Scalability; Transfer learning; Fleet PHM; Condition based maintenance; Multi-component system
Subject (DDC): 006: Special computer methods
620: Engineering
Abstract: Developing predictive maintenance algorithms for 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 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 talk I summarize results of an extensive project, developing a deep learning based fault detection scheme for wind farms. I emphasize the elements of this scheme that enabled scaling it up for commercial implementation which took place recently.
Further description: Best Presentation Award
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

Files in This Item:
There are no files associated with this item.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.