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Using artificial intelligence to monitor the state of the machine
Kubisz, Jan ; Kroupa, Jiří (referee) ; Kovář, Jiří (advisor)
Diploma thesis focus on creation of neural network’s internal structure with goal of creation Artificial Neural Network capable of machine state monitoring and predicting its remaining usefull life. Main goal is creation of algorithm’s and library for design and learning of Artificial Neural Network, and deeper understanding of the problematics in the process, then by utilising existing libraries. Selected method was forward-propagation network with multi-layered perceptron architecture, and backpropagation learning. Achieved results was, that the network was able to determine parts state from vibration measurement and on its basis predict remaining usefull life.
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Power Inverter Online Diagnostics
Knobloch, Jan ; Chlebiš,, Petr (referee) ; Drábek,, Pavel (referee) ; Klíma, Bohumil (advisor)
This doctoral thesis focuses on the problems of IGBT failure prediction in pulse converters using measurable changes of selected parameters (so--called trending variables) being influenced of transistor degradation during aging. Firstly the state--of--the--art in this field is presented in the dizertation. The description of designed and constructed automated measurement stand follows, enabling monitoring and recording of switching processes during accelerated aging. Further the problems of high--bandwidth measurement of electrical quantities during IGBT switching are described. Especially the problems of current sensing are analyzed and the most suitable sensor is selected. The data recorded using the developed apparatus served to identify potential trending variables allowing the failure prediction. Here the dependence of trending variables on aging and on parasitic influences (current, temperature, voltage) had to be distinguished. Finally the evaluation of trending variables is performed. Their insignificant sensitivity on accelerated aging is shown which complicates their practical implementation for the purpose of failure prediction.
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Using artificial intelligence to monitor the state of the machine
Kubisz, Jan ; Kroupa, Jiří (referee) ; Kovář, Jiří (advisor)
Diploma thesis focus on creation of neural network’s internal structure with goal of creation Artificial Neural Network capable of machine state monitoring and predicting its remaining usefull life. Main goal is creation of algorithm’s and library for design and learning of Artificial Neural Network, and deeper understanding of the problematics in the process, then by utilising existing libraries. Selected method was forward-propagation network with multi-layered perceptron architecture, and backpropagation learning. Achieved results was, that the network was able to determine parts state from vibration measurement and on its basis predict remaining usefull life.
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Power Inverter Online Diagnostics
Knobloch, Jan ; Chlebiš,, Petr (referee) ; Drábek,, Pavel (referee) ; Klíma, Bohumil (advisor)
This doctoral thesis focuses on the problems of IGBT failure prediction in pulse converters using measurable changes of selected parameters (so--called trending variables) being influenced of transistor degradation during aging. Firstly the state--of--the--art in this field is presented in the dizertation. The description of designed and constructed automated measurement stand follows, enabling monitoring and recording of switching processes during accelerated aging. Further the problems of high--bandwidth measurement of electrical quantities during IGBT switching are described. Especially the problems of current sensing are analyzed and the most suitable sensor is selected. The data recorded using the developed apparatus served to identify potential trending variables allowing the failure prediction. Here the dependence of trending variables on aging and on parasitic influences (current, temperature, voltage) had to be distinguished. Finally the evaluation of trending variables is performed. Their insignificant sensitivity on accelerated aging is shown which complicates their practical implementation for the purpose of failure prediction.
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