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A Model-based Prognostic Approach to Predict Remaining Useful Life of Components
Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.ORCID-id: 0000-0001-8278-8601
Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.ORCID-id: 0000-0002-0216-5058
2016 (Engelska)Ingår i: Proceedings of 1st International Conference on Maintenance Engineering, IncoME-I, 2016 / [ed] Jyoti K. Sinha, Akilu Yunusa-Kaltungo, Wolfgang Hahn, 2016, artikel-id ME2016_1147Konferensbidrag, Muntlig presentation med publicerat abstract (Refereegranskat)
Abstract [en]

One of the major problems in the industry is the extension of the useful life of high-performance systems. Proper maintenance plays an important role by extending the useful life, reducing the lifecycle costs and improving the reliability and availability. Health management using a proper condition-based maintenance (CBM) deployment is a worldwide accepted strategy and has grown very popular in many industries over the past decades. A case of CBM is when the maintenance decision is taken based on a forecast of the asset state. This strategy is called predictive maintenance or prognostic health management (PHM). PHM is an engineering discipline that aims to maintain the system behaviour and function, and assure the mission success, safety and effectiveness. This strategy is relevant in environments where the prediction of a failure and the prevention and mitigation of its consequences increase the profit and safety of the facilities concerned. Prognosis is the most critical part of this process and is nowadays recognized as a key feature in maintenance strategies since estimation of the remaining useful life (RUL) is essential.

PHM can provide a state assessment of the future health of systems or components, e.g. when a degraded state has been found. The aim of using PHM is to estimate how long it will take before the equipment will reach a failure threshold, in future operating conditions and future environmental conditions.

The aim of the paper is to improve the estimation of bearing RUL by dynamically updating the SKF L10 bearing life length calculation. Using a physics-based prognostic approach, the behaviour of a roller in a paper machine was simulated using the finite element method (FEM). A transfer function representing the relation between bearing acceleration and bearing forces was generated and used to convert the acceleration signal into an estimation of the dynamically changing bearing force. The estimated force is then used as input to the bearing life length calculation generating an updated L10 calculation for each time step. 

Ort, förlag, år, upplaga, sidor
2016. artikel-id ME2016_1147
Nyckelord [en]
Prognostics, Degradation, FEM, Modelling, Particle Filter, CBM, RUL, SKF L10
Nationell ämneskategori
Teknik och teknologier Annan samhällsbyggnadsteknik
Forskningsämne
Drift och underhållsteknik
Identifikatorer
URN: urn:nbn:se:ltu:diva-66625OAI: oai:DiVA.org:ltu-66625DiVA, id: diva2:1158054
Konferens
1st International Conference on Maintenance Engineering, IncoME-I, The University of Manchester, UK, August 30-31, 2016
Projekt
SKF-UTCTillgänglig från: 2017-11-17 Skapad: 2017-11-17 Senast uppdaterad: 2018-05-07Bibliografiskt granskad

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