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Data Analysis for Predictive Maintenance of a Robotic Arm
Žitný, Roland ; Rozman, Jaroslav (referee) ; Janoušek, Vladimír (advisor)
The Mitsubishi MELFA robotic arms used in modern factories work almost without interruption and produce sensory data about their operation. Various analysis techniques can be applied to such data for predictive maintenance, which provide information on the condition and maintenance needs of such robotic arms. The proposed predictive maintenance process consists of a sensory data acquisition system using the slmpclient and mitsubishi-monitor libraries, an analysis method system with anomaly detection using a convolutional autoencoder, anomaly classification using convolutional neural networks, and data segmentation into segments of individual robot actions using hidden Markov models. Such analysis techniques provide information on the severity, type, and location of emerging faults and abnormalities in behavior, which then determine the time required to perform the required maintenance. This work presents a created chain of predictive maintenance processes, where the obtained findings provide valuable insights into the application of predictive maintenance of Mitsubishi MELFA robotic arms in an industrial environment.
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Model and Controlling of Robotic Arm
Bílek, Jiří ; Orság, Filip (referee) ; Rozman, Jaroslav (advisor)
This bachelor's thesis deals with the controlling of Lynx 6 robotic arm using OpenRAVE. I deal with derivation of inverse kinematics of 4DOF robotic arm Lynx 6. The work contains a full description of creation of robotic arms in OpenRAVE XML format. The thesis includes solution of implementation of connecting OpenRAVE program and robotic arm using FITkit. Finally, the work includes consideration of the necessary arrangements for the connection of other robotic arms to the OpenRAVE.
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Data Analysis for Predictive Maintenance of a Robotic Arm
Žitný, Roland ; Rozman, Jaroslav (referee) ; Janoušek, Vladimír (advisor)
The Mitsubishi MELFA robotic arms used in modern factories work almost without interruption and produce sensory data about their operation. Various analysis techniques can be applied to such data for predictive maintenance, which provide information on the condition and maintenance needs of such robotic arms. The proposed predictive maintenance process consists of a sensory data acquisition system using the slmpclient and mitsubishi-monitor libraries, an analysis method system with anomaly detection using a convolutional autoencoder, anomaly classification using convolutional neural networks, and data segmentation into segments of individual robot actions using hidden Markov models. Such analysis techniques provide information on the severity, type, and location of emerging faults and abnormalities in behavior, which then determine the time required to perform the required maintenance. This work presents a created chain of predictive maintenance processes, where the obtained findings provide valuable insights into the application of predictive maintenance of Mitsubishi MELFA robotic arms in an industrial environment.
|
|
|
Model and Controlling of Robotic Arm
Bílek, Jiří ; Orság, Filip (referee) ; Rozman, Jaroslav (advisor)
This bachelor's thesis deals with the controlling of Lynx 6 robotic arm using OpenRAVE. I deal with derivation of inverse kinematics of 4DOF robotic arm Lynx 6. The work contains a full description of creation of robotic arms in OpenRAVE XML format. The thesis includes solution of implementation of connecting OpenRAVE program and robotic arm using FITkit. Finally, the work includes consideration of the necessary arrangements for the connection of other robotic arms to the OpenRAVE.
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