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Explainable Data-Driven Motor Condition Monitoring And Fault Disgnosis, Yuming Wang
Explainable Data-Driven Motor Condition Monitoring And Fault Disgnosis, Yuming Wang
Theses and Dissertations--Electrical and Computer Engineering
Industrial motors are widely used in various fields such as power generation, mining, and manufacturing. Motor faults and time-consuming maintenance process will lead to serious economic losses in this context. To monitor motor faults and detect motor conditions, different types of sensors that can test vibration and current signals are mounted on motors. However, the main challenge was how to use information gained by sensors to analyze or diagnose motor conditions.
Machine learning is a popular technology in recent years, and it's very suitable for crunching and analyzing data. As an important subset of machine learning, deep learning is suitable …
Machine Learning Approach For Vigilance State Classification In Mice, Anik Muhury
Machine Learning Approach For Vigilance State Classification In Mice, Anik Muhury
Theses and Dissertations--Electrical and Computer Engineering
Sleep has a significant impact on cognitive abilities such as memory, reaction time, productivity, and creative thinking; however, there are many aspects of this important activity that are not clearly understood. Over the last century, researchers have developed technology and animal models to assist in the study of sleep. Manual sleep scoring is time consuming, reduces productivity, and is impacted by human scorer subjectivity. On the other hand, automatic sleep stage categorization can enhance consistency and reliability, aiding professionals in identifying sleep related health problems.
In recent times various studies reported significant achievements for automatic vigilance detection and overcome the …
Weld Penetration Identification Based On Convolutional Neural Network, Chao Li
Weld Penetration Identification Based On Convolutional Neural Network, Chao Li
Theses and Dissertations--Electrical and Computer Engineering
Weld joint penetration determination is the key factor in welding process control area. Not only has it directly affected the weld joint mechanical properties, like fatigue for example. It also requires much of human intelligence, which either complex modeling or rich of welding experience. Therefore, weld penetration status identification has become the obstacle for intelligent welding system. In this dissertation, an innovative method has been proposed to detect the weld joint penetration status using machine-learning algorithms.
A GTAW welding system is firstly built. Project a dot-structured laser pattern onto the weld pool surface during welding process, the reflected laser pattern …