Heat recovery unit failure detection in air handling unit

No Thumbnail Available
Access rights
openAccess
Journal Title
Journal ISSN
Volume Title
Conference article in proceedings
Date
2018-01-01
Major/Subject
Mcode
Degree programme
Language
en
Pages
8
343-350
Series
Advances in Production Management Systems. Smart Manufacturing for Industry 4.0 - IFIP WG 5.7 International Conference, APMS 2018, Proceedings, IFIP Advances in Information and Communication Technology, Volume 536
Abstract
Maintenance is a complicated task that encompasses various activities including fault detection, fault diagnosis, and fault reparation. The advancement of Computer Aided Engineering (CAE) has increased challenges in maintenance as modern assets have became complex mixes of systems and sub systems with complex interaction. Among maintenance activities, fault diagnosis is particularly cumbersome as the reason of failures on the system is often neither obvious in terms of their source nor unique. Early detection and diagnosis of such faults is turning to one of the key requirements for economical and functional asset efficiency. Several methods have been investigated to detect machine faults for a number of years that are relevant for many application domains. In this paper, we present the process history-based method adopting nominal efficiency of Air Handling Unit (AHU) to detect heat recovery failure using Principle Component Analysis (PCA) in combination of the logistic regression method.
Description
| openaire: EC/H2020/688203/EU//BIoTope
Keywords
Air handling unit, Fault detection, Fault diagnosis, FDD, Heat recovery unit, Logistic regression, PCA
Other note
Citation
Madhikermi, M, Yousefnezhad, N & Främling, K 2018, Heat recovery unit failure detection in air handling unit . in Advances in Production Management Systems. Smart Manufacturing for Industry 4.0 - IFIP WG 5.7 International Conference, APMS 2018, Proceedings . IFIP Advances in Information and Communication Technology, vol. 536, Springer, pp. 343-350, International Conference on Advances in Production Management Systems, Seoul, Korea, Republic of, 26/08/2018 . https://doi.org/10.1007/978-3-319-99707-0_43