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Proactive system maintenance machines in engineering practice
Križanová, Bronislava ; Řezníčková, Hana (referee) ; Hammer, Miloš (advisor)
This bachelor thesis deals with the topic of proactive maintenance of machine tools. Different systems of maintenance, most widespread methods of diagnostics are described in the thesis and then there is selection of appropriate method of technical diagnostic state for machine tools in company. Tribological diagnostic is developing method and apposite for any kind of machines with lubricating system or machining emulsion. In practical part, it is focused on the analysis of installed maintenance system in the company, overview of actual analysis and measuring instruments of tribological diagnostic for machine tool. Prior to the end the process in field of proactive solving of systems of maintenance has been recommended for SMC Industrial Automation CZ LLC Vyškov.
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Automatic detection of tool fracture in metal sheet punching
Kluz, Jan ; Rajchl, Matej (referee) ; Brablc, Martin (advisor)
This Bachelor thesis deals with the design and subsequent implementation of the realtime fault detection system during the sheet metal punching process with a tool of small dimensions (0.5 × 12 mm). The proposed system is important for significant ease of the operator's work, acceleration of the process of production, as well as saving of the company finance budget. The first part of this thesis deals with the theoretical background of the studied issue. The following part is a brief theoretical introduction to the field of digital signal processing. The next chapter presents methods developed for fault signals detection including speed enhancing and data flow reducing algorithms. The main examined methods were: frequency peaks, frequency bands, autocorrelation, frequency correlation methods and machine learning including deep machine learning. Deep machine learning of the neural network achieved the best results overall. Features from time and frequency domain were used for purposes of creating the classification model using machine learning. The possibility of developing the predictive maintenance system is also described, including research of this area in a modern industry. Subsequently, the achieved results and their evaluation are presented. The end of this thesis is dedicated to the description of the implementation of classification system into realtime form and connecting this system to the punching press computer using Arduino Uno microcontroller and basic signal control electronics. The proposed system has been successfully assembled, tested and put into on-site testing.
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A Monitoring and Aggregation Device for Large Amounts of Industry Data
Held, Oliver ; Pánek, Richard (referee) ; Smrž, Pavel (advisor)
This bachelor thesis describes hardware and software development of a monitoring device for the collection of industrial big data. The device is based on the development BeagleBone Black kit together with vibration sensors, temperature sensors and strain gauges. The objective of the thesis is to design a device with respect to low production cost, simplicity and possible modularity. The I2C bus and embedded AD converter are used for the collection of the data from the sensors. The data is sent to remote server using UDP protocol. Furthermore, custom strain gauge module is designed and tested. The practical contribution of the thesis is to test the platform functionality, that includes collection, aggregation and transfer of the data.
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Principles of maintenance of the TPM method
Zahradníček, Lukáš ; Hammer, Miloš (referee) ; Řezníčková, Hana (advisor)
This master thesis concerns modern method of TPM used in the production companies for maintenance of machinery. In the theoretical part, general maintenance is first described, as well as the TPM method. There is also described the technical diagnostics, which was emphasized in the practical part in terms of the use of vibrodiagnostics in predictive maintenance. In the practical part there is presented the proposal for introduction of the TPM method at the SMC Industrial Automation s.r.o. in Vyškov.
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Predictive maintenance for automated assembly machines
Janík, Vladimír ; Burget, Radim (referee) ; Mecerod, Václav (advisor)
This thesis deals with data analysis. Data obtained from automated assembly machines and their quick and well-arranged displaying in a format suitable for individual end users. In the thesis web frameworks are compared and database structure as well as final software solution is proposed. The data is loaded using the implemented programming language module. The data is further analyzed and displayed to a user through a web-based application accessible to end user from every device connected to the corporate network.
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Design of a Part of an Information System for the Use of Industrial Data
Held, Oliver ; Klusák, Aleš (referee) ; Luhan, Jan (advisor)
This thesis deals with the description and innovation in an industrial company, which is focused on the diagnostics of production machines using mainly big data collection. The theoretical part of the thesis describes industry 4.0, the demands of big data on storage and also the basics of change management. The analytical part includes a description of the company, an analysis of the current state and a proposal for change. The proposed changes are the transition from a relational database to a non relational one and a new web application for data visualization. The last part of the thesis describes the implementation of these changes and their evaluation.
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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 diagnostics and maintenance of Stäubli robots
Lojková, Pavlína ; Řezníčková, Hana (referee) ; Hammer, Miloš (advisor)
The bachelor thesis deals with the predictive diagnostics and maintenance of Stäubli robots in Bosch Diesel s.r.o. in Jihlava. The parameters monitored so far are described and other suitable ones are proposed for this purpose. The design of the escalation model, its enhancement and visualization is realized. The bachelor thesis also deals with the evaluation of the problem solved.
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Machine tool Life Cycle
Mikulka, Tomáš ; Marek, Jiří (referee) ; Knoflíček, Radek (advisor)
The diploma thesis is focused on the determination of the life cycle state of the production machine. The thesis is divided into several chapters. First, the life cycle of the machine is defined, and the phrase used here is given. Subsequently, the work is devoted to maintenance, repairs and modernization of the production machine. Then there is a demonstration of Schaeffler Skalica's corporate structure and individual methods that determine the state of the machine's life cycle. In the next chapter, the machine is described and then the analysis is made for the current state of the machine. Then the analyses created are evaluated.
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