Engineering Commons^™

Structural Health Monitoring Using Machine Learning And Synthetic Data, Michail Tzimas

Graduate Theses, Dissertations, and Problem Reports

Structural health monitoring spans many decades of research across multiple engineering fields. However, typical monitoring processes for damage detection of complex structures usually prohibit real-time or fast detection of debilitating damage to the structure. One of the major issues of real-time detection of damage is the enormity of data that needs to be processed, which is worsened by the relative inability of fast relaying of data to structural engineers. With the rapid advancement of Machine Learning, both issues can be overcome, and detection of failure is achieved with non-invasive techniques. This dissertation explores the applicability of Machine Learning as a …

Development Of Machine Learning Based Approach To Predict Fuel Consumption And Maintenance Cost Of Heavy-Duty Vehicles Using Diesel And Alternative Fuels, Sasanka Katreddi

Graduate Theses, Dissertations, and Problem Reports

One of the major contributors of human-made greenhouse gases (GHG) namely carbon dioxide (CO2), methane (CH4), and nitrous oxide (NOX) in the transportation sector and heavy-duty vehicles (HDV) contributing to about 27% of the overall fraction. In addition to the rapid increase in global temperature, airborne pollutants from diesel vehicles also present a risk to human health. Even a small improvement that could potentially drive energy savings to the century-old mature diesel technology could yield a significant impact on minimizing greenhouse gas emissions. With the increasing focus on reducing emissions and operating costs, there is a need for efficient and …

Development Of A Machine Learning Model To Characterize The Performance Of A Selective Catalytic Reduction On Filter After-Treatment System For A Heavy-Duty Diesel Engine, Samuel A. Okeleye

Graduate Theses, Dissertations, and Problem Reports

Particulate matter (PM) and Oxides of Nitrogen (NOx) are the major pollutants in diesel engines, an attempt to control one leads to an increase in the other, a phenomenon known as PM-NOx trade-off in diesel engine emission control. Currently, these two pollutants are controlled by the Diesel Particulate Filter (DPF) and the Selective Catalytic Reduction (SCR) after-treatment system respectively, in addition to the Diesel Oxidation Catalyst (DOC) which helps to provide 1:1 split of NO/NO₂ and helps with raising exhaust gas temperatures. Today, heavy-duty diesel engines feature a DPF, a primary SCR and a secondary SCR. Despite this complex …

Dynamic Hvac Energy Management Using Commercial Building Occupancy Metrics & Neural Networks, Krishna Chaitanya Jagadeesh Simma

Civil Engineering ETDs

With the rise of technology use in buildings, it is now possible to collect data that can be used to improve building energy consumption. One factor that has significant impact on building energy consumption is occupancy. Recent studies have shown promising results in obtaining occupancy information from existing infrastructure such as WiFi router networks. However, these existing frameworks require additional investments through software upgrades, added infrastructure, computational resources, and may raise occupant privacy concerns. Additionally, with occupant thermal comfort statistics being lower than ASHAREA specified standards, a novel approach for indoor climate control is needed. To address the limitations in …

Artificial Intelligence Approaches For Structural Health Monitoring Of Aerospace Structures, Kimberly A. Cardillo

Theses and Dissertations

Structural health monitoring (SHM) and non-destructive evaluation (NDE) have been a significant research topic to help with damage detection in aerospace structures. SHM and NDE techniques are based on extracting damage sensitive features to determine the criticality of damage and lifetime of a structure. Acoustic emission (AE) signal detection is an important technique in SHM and NDE especially for fatigue crack growth. AE signals for thin aerospace structures consist of ultrasonic guided Lamb waves that propagate through the structure. This thesis focuses on AE signal repeatability, load at which AE signals occur, feature extraction, artificial intelligence and electro-mechanical impedance of …

Short Term Energy Forecasting For A Microgird Load Using Lstm Rnn, Akhil Soman

Masters Theses

Decentralization of the electric grid can increase resiliency (during natural disasters) and can reduce T&D energy losses and emissions. Microgrids and DERs can enable this to happen. It is important to optimally control microgrids and DERs to extract the greatest economic, environmental and resiliency benefits. This is enabled by robust forecasting to optimally control loads and energy sources. An integral part of microgrid control is power side and load side demand forecasting.

In this thesis, we look at the ability of a powerful neural network algorithm to forecast the load side demand for a microgrid using the UMass campus as …

Development Of Horizontal Axis Hydrokinetic Turbine Using Experimental And Numerical Approaches, Abdulaziz Abutunis

Doctoral Dissertations

“Hydrokinetic energy conversion systems (HECSs) are emerging as viable solutions for harnessing the kinetic energy in river streams and tidal currents due to their low operating head and flexible mobility. This study is focused on the experimental and numerical aspects of developing an axial HECS for applications with low head ranges and limited operational space. In Part I, blade element momentum (BEM) and neural network (NN) models were developed and coupled to overcome the BEM’s inherent convergence issues which hinder the blade design process. The NNs were also used as a multivariate interpolation tool to estimate the blade hydrodynamic characteristics …

An Application Of Clustering And Cluster Update Methods To Boiler Sensor Prediction And Case-Based-Reasoning To Boiler Repair, Timothy Edward Rooney

Theses and Dissertations

Driven by demand from both consumers and manufacturers alike, Internet of Things (IoT)

capabilities are being built into more products. Consumers want more control and access to their

devices, while manufacturers can find data gathered from IoT-capable products invaluable. In

this thesis, we use data from a growing fleet of IoT-connected boilers in the residential, lightcommercial, and medium-commercial ranges to demonstrate a framework for cluster initialization

and updating. We compare two methods of dynamically updating clusters: a sequential method

inspired by sequential K-means clustering and a cohesion-based method called DYNC. A predictive

artificial neural network system demonstrates the effectiveness of …

Manufacturing Feature Recognition With 2d Convolutional Neural Networks, Yang Shi

Theses and Dissertations

Feature recognition is a critical sub-discipline of CAD/CAM that focuses on the design and implementation of algorithms for automated identification of manufacturing features. The development of feature recognition methods has been active for more than two decades for academic research. However, in this domain, there are still many drawbacks that hinder its practical applications, such as lack of robustness, inability to learn, limited domain of features, and computational complexity. The most critical one is the difficulty of recognizing interacting features, which arises from the fact that feature interactions change the boundaries that are indispensable for characterizing a feature. This research …

Approximate Dynamic Programming Based Solutions For Fixed-Final-Time Optimal Control And Optimal Switching, Ali Heydari

Doctoral Dissertations

"Optimal solutions with neural networks (NN) based on an approximate dynamic programming (ADP) framework for new classes of engineering and non-engineering problems and associated difficulties and challenges are investigated in this dissertation. In the enclosed eight papers, the ADP framework is utilized for solving fixed-final-time problems (also called terminal control problems) and problems with switching nature. An ADP based algorithm is proposed in Paper 1 for solving fixed-final-time problems with soft terminal constraint, in which, a single neural network with a single set of weights is utilized. Paper 2 investigates fixed-final-time problems with hard terminal constraints. The optimality analysis of …

Numerical Modeling And Optimization Of Waterjet Based Surface Decontamination, Konstantin Babets

Dissertations

The mission of this study is to investigate the high-pressure waterjet based surface decontamination. Our specific objective is to develop a practical procedure for selection of process conditions at given constraints and available knowledge. This investigation is expected to improve information processing in the course of material decontamination and assist in the implementation of the waterjet decontamination technology into practice. The development of a realistic procedure for processing of a chaotic and non-accurate information constitutes the main accomplishment of this study.

The research involved acquisition of representative information about removal of brittle, elastic and viscous deposits. As a result an …