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Predict The Failure Of Hydraulic Pumps By Different Machine Learning Algorithms, Yifei Zhou, Monika Ivantysynova, Nathan Keller
Predict The Failure Of Hydraulic Pumps By Different Machine Learning Algorithms, Yifei Zhou, Monika Ivantysynova, Nathan Keller
The Summer Undergraduate Research Fellowship (SURF) Symposium
Pump failure is a general concerned problem in the hydraulic field. Once happening, it will cause a huge property loss and even the life loss. The common methods to prevent the occurrence of pump failure is by preventative maintenance and breakdown maintenance, however, both of them have significant drawbacks. This research focuses on the axial piston pump and provides a new solution by the prognostic of pump failure using the classification of machine learning. Different kinds of sensors (temperature, acceleration and etc.) were installed into a good condition pump and three different kinds of damaged pumps to measure 10 of …
Tactile Sensing And Position Estimation Methods For Increased Proprioception Of Soft-Robotic Platforms, Nathan Mcclain Day
Tactile Sensing And Position Estimation Methods For Increased Proprioception Of Soft-Robotic Platforms, Nathan Mcclain Day
Theses and Dissertations
Soft robots have the potential to transform the way robots interact with their environment. This is due to their low inertia and inherent ability to more safely interact with the world without damaging themselves or the people around them. However, existing sensing for soft robots has at least partially limited their ability to control interactions with their environment. Tactile sensors could enable soft robots to sense interaction, but most tactile sensors are made from rigid substrates and are not well suited to applications for soft robots that can deform. In addition, the benefit of being able to cheaply manufacture soft …
Baseline Data From Servo Motors In A Robotic Arm For Autonomous Machine Fault Diagnosis, Jacob Brown
Baseline Data From Servo Motors In A Robotic Arm For Autonomous Machine Fault Diagnosis, Jacob Brown
Mechanical Engineering Undergraduate Honors Theses
Fault diagnosis can prolong the life of machines if potential sources of failure are discovered and corrected before they occur. Supervised machine learning, or the use of training data to enable machines to discover these faults on their own, makes failure prevention much easier. The focus of this thesis is to investigate the feasibility of creating datasets of various faults at both the component and system level for a servomotor and a compatible robotic arm, such that this data can be used in machine learning algorithms for fault diagnosis. The faults induced at the component level in different servomotors include: …
Evolution Of Mg Az31 Twin Activation With Strain: A Machine Learning Study, Andrew D. Orme
Evolution Of Mg Az31 Twin Activation With Strain: A Machine Learning Study, Andrew D. Orme
Undergraduate Honors Theses
Machine learning is being adopted in various areas of materials science to both create predictive models and to uncover correlations which reveal underlying physics. However, these two aims are often at odds with each other since the resultant predictive models generally become so complex that they can essentially be described as a black box, making them difficult to understand. In this study, complex relationships between microstructure and twin formation in AZ31 magnesium are investigated as a function of increasing strain. Supervised machine learning is employed, in the form of J-48 decision trees. In one approach, strain is incorporated as an …
Aeroelastic Simulation Of Wind Turbines Using Free Vortex Methods And Strategies For Accelerating The Computation, Shujian Liu
Aeroelastic Simulation Of Wind Turbines Using Free Vortex Methods And Strategies For Accelerating The Computation, Shujian Liu
Doctoral Dissertations
This dissertation integrated the free vortex method code Wake Induced Dynamics Simulator (WInDS), which was developed by Sebastian et al., into the open source and widely-used software FAST. A range of computational strategies including paral- lelization and Treecode algorithms are used to increase the computational efficiency of the software. Full aero-hydro-servo-elastic simulations with free vortex method are conducted, which focus on an in-depth study on the influence of the aeroelasticity of the wind turbine and platform motions on the unsteadiness of the aerodynamics, and the comparison of aeroelastic responses of two floating wind turbine concepts. This dissertation also applies long …
Implementing Large Eddy Simulation To Numerical Simulation Of Optical Wave Propagation, Diego Alberto Lozano Jimenez
Implementing Large Eddy Simulation To Numerical Simulation Of Optical Wave Propagation, Diego Alberto Lozano Jimenez
Open Access Theses & Dissertations
In this study, we want to simulate long-range laser propagation in atmospheric turbulence. The numerical simulations are carried out to study the impact of strong atmospheric turbulence in spatial, temporal, and related spectral domains. The first section of this study will be concerned with modeling this numerical simulation in Kolmogorov and non-Kolmogorov spectrum. To validate our numerical simulation, we will compare the statistical parameter to theoretical approximation in both Kolmogorov and non-Kolmogorov spectrums. Once the code is validated, we want to integrate Large Eddy Simulation (LES) turbulence modeling. LES simulations allowa us to study strong fluid turbulence and can predict …
Data-Driven Predictive Framework For Modeling Complex Multi-Physics Engineering Applications, Arturo Schiaffino Bustamante
Data-Driven Predictive Framework For Modeling Complex Multi-Physics Engineering Applications, Arturo Schiaffino Bustamante
Open Access Theses & Dissertations
Computational models are often encountered in multiple engineering application, such as structural design, material science, heat transfer and fluid dynamics. These simulations offer the engineers the capability of understanding complex physical situations before putting them to practice, either through experimentation or prototyping. The current advances in computational sciences, hardware architecture, software development and big data technology, have allowed the construction of sturdy predicting frameworks for analyzing a wide array of natural phenomena across different disciplines, either through the implementation of statistical methods, such as big data, and uncertainty quantification, or through high performance computing of a numerical model. The objective …
Novelty Detection Of Machinery Using A Non-Parametric Machine Learning Approach, Enrique Angola
Novelty Detection Of Machinery Using A Non-Parametric Machine Learning Approach, Enrique Angola
Graduate College Dissertations and Theses
A novelty detection algorithm inspired by human audio pattern recognition is conceptualized and experimentally tested. This anomaly detection technique can be used to monitor the health of a machine or could also be coupled with a current state of the art system to enhance its fault detection capabilities. Time-domain data obtained from a microphone is processed by applying a short-time FFT, which returns time-frequency patterns. Such patterns are fed to a machine learning algorithm, which is designed to detect novel signals and identify windows in the frequency domain where such novelties occur. The algorithm presented in this paper uses one-dimensional …
Monitoring Of Hybrid Manufacturing Using Acoustic Emission Sensor, Haythem Gaja
Monitoring Of Hybrid Manufacturing Using Acoustic Emission Sensor, Haythem Gaja
Doctoral Dissertations
"The approach of hybrid manufacturing addressed in this research uses two manufacturing processes, one process builds a metal part using laser metal deposition, and the other process finishes the part using a milling machining. The ability to produce complete functioning parts in a short time with minimal cost and energy consumption has made hybrid manufacturing popular in many industries for parts repair and rapid prototyping. Monitoring of hybrid manufacturing processes has become popular because it increases the quality and accuracy of the parts produced and reduces both costs and production time. The goal of this work is to monitor the …