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1.
DESIGN AND IMPLEMENTATION OF THE SUPERVISORY MODULE AS PART OF A SYSTEM FOR CONDITION MONITORING AND CONTROL OF SOLID OXIDE ELECTROLYSIS CELL SYSTEMS
Amina Uglješa, 2023, master's thesis

Abstract: Hydrogen is playing an important role in many sectors of modern economy (green vehicles, energy conversion and storage in electrical grids, processing industry). Solid oxide electrolysis cell (SOEC) is an emerging technology for the production of hydrogen from steam and electrical energy as well as for renewable energies storage. Unfortunately, operating at high current and electrical transients cause degradation that leads to premature end of life. A remedy is to implement a hardware module capable to perform online condition monitoring and optimization of SOEC systems resulting in improved overall performance and extended lifetime. That is expected to significantly expand their deployment on the market. However, very little has been done so far. The H2020 project REACTT seems to be one of the first attempts to build an embedded system for monitoring, diagnosis, prognostics, and control (MDPC) for SOEC system. The underlying master's thesis contributes to the REACTT project in the segment related to the supervision of different modules of the MDPC system. The supervisor module is aimed to orchestrate the operation of various functional modules (agents) such as data acquisition, system optimization, diagnosis, prognostics, and mitigation. The thesis focuses on the design of the supervisor module and its implementation on a control platform based on Raspberry Pi 4. The main contributions of the thesis are twofold. First, the dynamic operation of the supervisor modelled by using the state transition diagram (STD). Second, the code for implementation of the supervisor on the target platform done in Python in a way that complies with the requirements imposed in the project.
Keywords: supervisor, module, agent, method, solid oxide electrolysis cell system, diagnosis, prognostics, real-time optimization, Python programming, state transition diagram
Published in RUNG: 20.06.2023; Views: 930; Downloads: 15
.pdf Full text (2,94 MB)

2.
Zaznavanje in lokalizacija poškodb ležajev v rotacijskih strojih
Patrik Peršič, 2020, master's thesis

Abstract: Ležaji so pomemben del rotacijskega stroja, saj prenašajo silo vrtečega se dela na ohišje. Zaradi te funkcije so velikokrat podvrženi prekomernim silam in posledično poškodbam. Odkritje poškodbe v zgodnji fazi je zelo pomembno, saj lahko na ta način preprečimo draga popravila strojev in izpad prihodkov zaradi zaustavitve. V literaturi najdemo veliko postopkov za diagnostiko napak na ležajih, ki praviloma temeljijo na zaznavanju sprememb v karakterističnih veličinah, ki jim pravimo značilke. Prvi pomemben korak pri diagnostiki je detekcija, kjer je potrebno zaznati, ali je prišlo do spremembe v vrednosti značilke. Klasične značilke se dobro obnesejo pri konstantnih pogojih obratovanja, slabše pa, če se ti pogoji spreminjajo. Težko je namreč ločiti med spremembami v značilkah vsled poškodbe in spremembami zaradi spremenljivih obratovalnih pogojev. Tradicionalni načini zaznavanja sprememb v značilki temeljijo na ugotavljanju, ali je ta presegla nek prag. Težava, ki se pri tem pojavlja, je, da optimalnega praga ni enostavno sistematično izbrati, poleg tega pa se lahko pojavijo problemi s pogostimi prehodi značilke čezenj in nazaj, kar ima za posledico pogosto vklapljanje in izklapljanje alarma. Temu pravimo diagnostična nestabilnost. Namen magistrskega dela je bil raziskati možnosti za odpravo težav z diagnostično nestabilnostjo pri diagnosticiranju poškodb ležajev s statističnimi koncepti, in sicer konkretno z uporabo Jensen-Renyijeve divergence. Delovanje pristopa smo najprej raziskali na simulacijskih primerih, nato pa ga uporabili na realnih meritvah iz baze podatkov IMS Bearing Dataset. Opisali smo celoten postopek detekcije, tudi določitev optimalnega praga poškodbe po hevristični metodi. Ker je ideja dokaj nova, smo morali za vse pripraviti ustrezne algoritme. Ugotovili smo, da Jensen-Renyijeva divergenca ob pravilni nastavitvi parametrov deluje zelo dobro. Vrednosti značilk naraščajo monotono, posledično je manj možnosti za lažni alarm. Poleg tega pa spremembo zazna že ob najmanjšem povišanju, brez zakasnitve.
Keywords: ležaji, diagnostika, Jensen-Renyijeva divergenca, Fourierjeva transformacija
Published in RUNG: 30.06.2020; Views: 3164; Downloads: 110
.pdf Full text (18,97 MB)

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