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dc.contributor.advisorSchjølberg, Per
dc.contributor.authorFerrero, Matías Nicolas
dc.date.accessioned2019-09-11T09:10:10Z
dc.date.created2018-01-12
dc.date.issued2018
dc.identifierntnudaim:18184
dc.identifier.urihttp://hdl.handle.net/11250/2615299
dc.description.abstractThe current project is structured as follows: first, a brief description of the three industrial revolutions that has occurred in the past years. Then, the introduction and thorough description of the fourth industrial revolution, or industry 4.0. Industry 4.0 is a concept that comprises a whole technological ecosystem. Therefore, there are many important terms and technologies that need to be defined along Industry 4.0, for example Internet of Things, Machine Learning,Big Data, Blockchain, etc. The project attempts to shed light on how the combination of these technologies bring industry 4.0 -materialized in the smart factory- into life, what are the benefits and the challenges of these new technologies, and how feasible is its implementation for companies today. The paper continues introducing the definition of maintenance, and its importance as a driver to increase reliability and safety of the industrial assets, as well as a tool to save operating costs in the long term. The state of the art in maintenance practices in industry is reviewed, and how companies are increasingly starting to implement predictive maintenance 4.0 projects, or have future plans to do so. Finally, the maintenance strategy applied in a control valve of a steam turbine is examined. Currently, the maintenance of the turbine is carried out according to recommendations of the OEM, Siemens. However, Elkem has noticed that some of the recommendations are not tailored towards the specific use and environment where the turbine operates. In other words, The recommendations are addressed to a "general" customer, who produces an stable output. Nevertheless, Elkem, given the nature of its process, produces an output which is fluctuating, and therefore some components of the turbine are exposed to an alternating stress. With this problematic in mind, the goal of the project is to develop a rule which ultimately saves costs and extends the life of the turbine. In order to optimize when in the future the spindle should be replaced, a Gamma process model is proposed to predict the expected remaining useful lifetime of this component. The steam turbine is part of the energy recovering system implemented next to the smelter plant located in Thamshavn, property of Elkem. A steam turbine is an expensive equipment and therefore it is important to apply a good maintenance strategy on the components that constitute the turbine, in order to avoid, for one side, failures and unscheduled downtimes, and for the other side, excessive and costly maintenance.en
dc.languageeng
dc.publisherNTNU
dc.subjectReliability, Availability, Maintainability and Safety (RAMS)en
dc.titleIndustry 4.0 and Predictive Maintenance in a Steam Turbineen
dc.typeMaster thesisen
dc.source.pagenumber93
dc.contributor.departmentNorges teknisk-naturvitenskapelige universitet, Fakultet for ingeniørvitenskap,Institutt for maskinteknikk og produksjonnb_NO
dc.date.embargoenddate10000-01-01


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