Sökning: "Bolt detection"
Hittade 4 uppsatser innehållade orden Bolt detection.
1. Object Recognition and Tracking of Bolts: A Comparative Analysis of CNN Models and Computer Vision Techniques : A Comparison of CNN Models and Tracking Algorithms
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : The newer generation industry 4.0 focuses on development of both flexibility and autonomy for power tools used by companies in different mechanical areas and assembly lines. One area for automation is the application of computer vision in power tools to detect, identify and track bolts. LÄS MER
2. Implementation of Bolt Detection and Visual-Inertial Localization Algorithm for Tightening Tool on SoC FPGA
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : With the emergence of Industry 4.0, there is a pronounced emphasis on the necessity for enhanced flexibility in assembly processes. In the domain of bolt-tightening, this transition is evident. Tools are now required to navigate a variety of bolts and unpredictable tightening methodologies. LÄS MER
3. Real-time Classification of Multi-sensor Signals with Subtle Disturbances Using Machine Learning : A threaded fastening assembly case study
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Sensor fault detection is an actively researched area and there are a plethora of studies on sensor fault detection in various applications such as nuclear power plants, wireless sensor networks, weather stations and nuclear fusion. However, there does not seem to be any study focusing on detecting sensor faults in the threaded fastening assembly application. LÄS MER
4. Anomaly Detection using LSTM N. Networks and Naive Bayes Classifiers in Multi-Variate Time-Series Data from a Bolt Tightening Tool
Master-uppsats, KTH/Skolan för industriell teknik och management (ITM)Sammanfattning : In this thesis, an anomaly detection framework has been developed to aid in maintenance of tightening tools. The framework is built using LSTM networks and gaussian naive bayes classifiers. The suitability of LSTM networks for multi-variate sensor data and time-series prediction as a basis for anomaly detection has been explored. LÄS MER