Heat recovery unit failure detection in air handling unit

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorMadhikermi, Maniken_US
dc.contributor.authorYousefnezhad, Nargesen_US
dc.contributor.authorFrämling, Karyen_US
dc.contributor.departmentDepartment of Computer Scienceen
dc.contributor.departmentDepartment of Industrial Engineering and Managementen
dc.contributor.groupauthorFrämling Kary groupen
dc.date.accessioned2018-10-16T08:53:55Z
dc.date.available2018-10-16T08:53:55Z
dc.date.embargoinfo:eu-repo/date/embargoEnd/2019-08-25en_US
dc.date.issued2018-01-01en_US
dc.description| openaire: EC/H2020/688203/EU//BIoTope
dc.description.abstractMaintenance 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.en
dc.description.versionPeer revieweden
dc.format.extent8
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationMadhikermi, 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_43en
dc.identifier.doi10.1007/978-3-319-99707-0_43en_US
dc.identifier.isbn9783319997063
dc.identifier.issn1868-4238
dc.identifier.otherPURE UUID: 28b91a90-8505-4580-9cc4-6f6d7a5e7631en_US
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/28b91a90-8505-4580-9cc4-6f6d7a5e7631en_US
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/28377679/SCI_Madhikermi_Heat_Recovery.pdf
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/34266
dc.identifier.urnURN:NBN:fi:aalto-201810165343
dc.language.isoenen
dc.relationinfo:eu-repo/grantAgreement/EC/H2020/688203/EU//BIoTopeen_US
dc.relation.fundinginfoAcknowledgment. The research leading to this publication is supported by the European Union’s Horizon 2020 research and innovation programme (grant 688203) and Academy of Finland (Open Messaging Interface; grant 296096).
dc.relation.ispartofInternational Conference on Advances in Production Management Systemsen
dc.relation.ispartofseriesAdvances in Production Management Systems. Smart Manufacturing for Industry 4.0 - IFIP WG 5.7 International Conference, APMS 2018, Proceedingsen
dc.relation.ispartofseriespp. 343-350en
dc.relation.ispartofseriesIFIP Advances in Information and Communication Technology ; Volume 536en
dc.rightsopenAccessen
dc.subject.keywordAir handling uniten_US
dc.subject.keywordFault detectionen_US
dc.subject.keywordFault diagnosisen_US
dc.subject.keywordFDDen_US
dc.subject.keywordHeat recovery uniten_US
dc.subject.keywordLogistic regressionen_US
dc.subject.keywordPCAen_US
dc.titleHeat recovery unit failure detection in air handling uniten
dc.typeA4 Artikkeli konferenssijulkaisussafi
dc.type.versionacceptedVersion

Files