Asadzade, Asad

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https://hdl.handle.net/11147/16028
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01. Izmir Institute of Technology
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NO POVERTY1
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ZERO HUNGER2
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GOOD HEALTH AND WELL-BEING3
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QUALITY EDUCATION4
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GENDER EQUALITY5
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CLEAN WATER AND SANITATION6
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AFFORDABLE AND CLEAN ENERGY7
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DECENT WORK AND ECONOMIC GROWTH8
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INDUSTRY, INNOVATION AND INFRASTRUCTURE9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
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REDUCED INEQUALITIES10
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SUSTAINABLE CITIES AND COMMUNITIES11
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RESPONSIBLE CONSUMPTION AND PRODUCTION12
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CLIMATE ACTION13
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LIFE BELOW WATER14
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LIFE ON LAND15
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PEACE, JUSTICE AND STRONG INSTITUTIONS16
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PARTNERSHIPS FOR THE GOALS17
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  • Master Thesis
    Predictive Maintenance for Smart Industry
    (01. Izmir Institute of Technology, 2020) Asadzade, Asad; Ayav, Tolga; Ayav, Tolga; 03.04. Department of Computer Engineering; 01. Izmir Institute of Technology; 03. Faculty of Engineering
    After the internet of things developed rapidly, it started to be used in many several industrial areas. Thanks to IoT, data that affect the health of any equipment or other important systems are collected. When these data are processed correctly, important information about the production process is obtained. For example, thanks to this data, systems based on machine learning are created to predict when various components will fail. Thus, maintenance operations are carried out before the component's breakdown, and replacement operations are performed if necessary. This strategy, called predictive maintenance, provides industries with advantages such as maximizing the life of components, reducing extra costs, and time saving. In this study, we applied ARF method, which is based on stream learning, on Turbofan Engine Degradation Simulation Datasets which are provided by NASA to estimate the remaining useful lifetime of jet engines. As a result, we mentioned about the advantages of streaming learning over batch learning and compared our results with other batch learning based studies which are applied on the same datasets.