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Proposal for Improvements of Predictive Maintenance Services and its Promotion
Stránský, Štěpán ; Cabejšek, Tomáš (referee) ; Kaňovská, Lucie (advisor)
Hlavním cílem této bakalářské práce je analyzovat divizi Power společnosti AXIMA a její konkurenty se zaměřením na chytré produkty a služby a na základě těchto analýz vytvořit návrhy na zlepšení a propagaci služeb prediktivní údržby AXIMA Power. První hlavní část je teoretická a obsahuje definice týkající se marketingu, používaných analýz a prediktivní údržby, následuje analytická část, kde je analyzována současná situace společnosti a jsou identifikovány klíčové faktory pro návrhy. Poslední část obsahuje návrhy aktivit zaměřených na zlepšení a propagaci služeb prediktivní údržby a chytrých produktů divize.
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Data collection from 3D printer
Fiala, Jan ; Baštán, Ondřej (referee) ; Arm, Jakub (advisor)
This work is dedicated to design and implementation of a funcitonal model for data processing from 3D printer using sensors in IoT concept. Measured data will be processed and transfered to basic units with connection to the cloud server for any ongoing work. Part of this work is selection of suitable sensors and system members, creation of a functional model of data transfer and its implementation on a 3D printer.
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Train Identification System at Railway Switches And Crossings Using Advanced Machine Learning Methods
Krč, Rostislav ; Vorel,, Jan (referee) ; Plášek, Otto (referee) ; Podroužek, Jan (advisor)
This doctoral thesis elaborates possibilities of automatic train type identification in railway S&C using accelerometer data. Current state-of-the-art was considered, including requirements stated by research projects such as S-Code, In2Track or Turnout 4.0. Conducted experiments considered different architectures of artificial neural networks (ANN) and statistically evaluated multiple use case scenarios. The resulting accuracy reached up to 89.2% for convolutional neural network (CNN), which was selected as a suitable baseline architecture for further experiments. High generalization capability was observed as models trained on data from one location were able to classify locomotive types in the other location. Further experiments evaluated the effect of signal filtering and denoising. Evaluation of allocated memory and processing time for pre-trained models proved feasibility for in-situ application with regard to hardware restrictions. Due to a limited amount of available accelerometer data, distribution grid power demand data were utilized for further refinement of the proposed CNN architecture. Deep multi-layer architecture with regularization techniques such as dropout or batch normalization provides state-of-the-art performance for time series classification problems. Class activation mapping (CAM) allowed an explanation of decisions made by the neural network. Presented results proved that train type identification directly in the S&C is possible. The CNN was selected as optimal architecture for this task due to high classification accuracy, automatic filtration, and pattern recognition capabilities, allowing for the incorporation of the end-to-end learning strategy. Moreover, direct on-site application of pre-trained models is feasible with respect to limitations of in-situ hardware. This thesis contributes to understanding the train type identification problem and provides a solid theoretical background for future research.
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Predictive maintenance of pneumatic pistons
Voronin, Artyom ; Rajchl, Matej (referee) ; Brablc, Martin (advisor)
Tato práce se zabývá vytvořením simulačního modelu dvojčinného pneumatického pístu s mechanickou sestavou, včetně modelů snímačů, s následujícím odhadem parametrů a aproximací chování demonstračního zařízení. Dalším cílem je prezentace různých přístupů prediktivní údržby na datové sadě měřené na demonstračním zařízení. Na měřený datový soubor se aplikovaly signal-based techniky bez použití simulačního modelu a model-based metody, které vyžadují použití simulačního modelu. Výsledkem této práce je ověření možnosti monitorování stavu zařízení pomocí nainstalovaných senzorů a vyhodnocení efektivity senzorů z hlediska přesnosti a finančních nákladů.
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Design of machining process diagnostic system
Wolf, Jonatan ; Klapka, Milan (referee) ; Houfek, Lubomír (advisor)
The master’s thesis is focused on online diagnostics of the machining process. In the theoretical part are presented maintenance possibilities of machine tools. A whole chapter is devoted to vibrodiagnostics, which describes vibration sensors, their attachment to the measured object and methods of vibration signal processing. The practical part lies in creating a software diagnostic solution for chosen PLC and sensors. The functionality of the proposed system was verified during experimental machining, which also provided valuable data for the correct setting of the system.
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Smart bearings
Junek, Jaroslav ; Polnický, Vojtěch (referee) ; Hartl, Martin (advisor)
This thesis deals with the issue of smart bearing, which can be used to refine and determine the condition of the bearing. Due to its advantageous availability of the cyber-physical system as a suitable apparatus for industrial use. The links and connections within this system are mentioned here. The following is a summary of commercially available smart bearings. It is complemented by patent solutions and scientific studies, which could point to future developments in this area.
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Virtual twin and predictive maintenance
Kotrba, Martin ; Holub, Michal (referee) ; Tůma, Jiří (advisor)
This diploma thesis deals with the design of a virtual twin of cross table MCV 754 QUICK from the Czech manufacturer Kovosvit MAS. The research part describes the basic principles of predictive maintenance methodology and virtual twin technology. The practical part includes a simplification of the initial cross table model. This model was then transferred to the simulation software MSC Adams, where it was optimized so that its behavior corresponds as closely as possible to the physical device. Several simulations were subsequently performed on the model. Then a concept for a predictive method for calculating the remaining service life of individual sections of the ball screw was presented. Simulations performed on a virtual twin served as a source of data to test the proposed method.
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Using tribodiagnostics in predictive maintenance in company practice
Trost, Daniel ; Nahodil, Petr (referee) ; Hammer, Miloš (advisor)
The thesis deals with the use of tribodiagnostics in predictive maintenance in corporate practice. It is generally dealt with maintenance, then tribodiagnostics in the company Škoda Auto a.s. Used offline and online diagnostic tools are described. Emphasis is placed on verifying the functionality of the newly purchased online filter unit. The experimental part is focused on detailed analysis of the above, including comparison of measurement results offline and online diagnostics. There is also an economic evaluation of savings obtained by operation the online filter unit. In conclusion, the tribodiagnostics recommendations are given for Škoda Auto a.s.
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