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Search Publications by: Rachael Sexton (Fed)

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Displaying 1 - 25 of 28

KPI Extraction from Maintenance Work Orders-A Comparison of Expert Labeling, Text Classification and AI-Assisted Tagging for Computing Failure Rates of Wind Turbines

December 6, 2023
Author(s)
Marc-Alexander Lutz, Bastian Schafermeier, Rachael Sexton, Michael Sharp, Alden A. Dima, Stefan Faulstich, Jagan Mohini Aluri
Maintenance work orders are commonly used to document information about wind turbine operation and maintenance. This includes details about proactive and reactive wind turbine downtimes, such as preventative and corrective maintenance. However, the

An Infrastructure for Curating, Querying, and Augmenting Document Data: COVID-19 Case Study

August 8, 2023
Author(s)
Eswaran Subrahmanian, Guillaume Sousa Amaral, Talapady N. Bhat, Mary C. Brady, Kevin G. Brady, Jacob Collard, Sarra Chouder, Philippe Dessauw, Alden A. Dima, John T. Elliott, Walid Keyrouz, Nicolas Lelouche, Benjamin Long, Rachael Sexton, Ram D. Sriram
With the advent of the COVID-19 pandemic, there was the hope that data science approaches could help discover means for understanding, mitigating, and treating the disease. This manifested itself in the creation of the COVID-19 Open Research Dataset (CORD

LabelVizier: Interactive Validation and Relabeling for Technical Text Annotations

March 30, 2023
Author(s)
Xiaoyu Zhang, Xiwei Xuan, Rachael Sexton, Alden A. Dima
With the rapid accumulation of text data brought forth by advances in data-driven techniques, the task of extracting "data annotations"—concise, high-quality data summaries from unstructured raw text—has become increasingly important. Researchers in the

Adapting natural language processing for technical text

June 29, 2021
Author(s)
Alden A. Dima, Sarah Lukens, Melinda Hodkiewicz, Thurston Sexton, Michael Brundage
Despite recent dramatic successes, Natural Language Processing (NLP) is not ready to address a variety of real-world problems. Its reliance on large standard corpora, a training and evaluation paradigm that favors the learning of shallow heuristics, and

A Visual Analytics Approach for the Diagnosis of Heterogeneous and Multidimensional Machine Maintenance Data

May 10, 2021
Author(s)
Xiaoyu Zhang, Takanori Fujiwara, Senthil K. Chandrasegaran, Michael Brundage, Thurston Sexton, Alden A. Dima, Kwan-Liu Ma
Analysis of large, high-dimensional, and heterogeneous datasets is challenging as no one technique is suitable for visualizing and clustering such data in order to make sense of the underlying information. For instance, heterogeneous logs detailing machine

Discovering Critical KPI Factors from Natural Language in Maintenance Work Orders

April 22, 2021
Author(s)
Michael Sharp, Michael Brundage, Rachael Sexton, Fnu Madhusudanan Navinchandran
Optimizing maintenance practices is a continuous process that must take into account the evolving state of the equipment, resources, workers, and more. To help streamline this process, facilities need a concise procedure for identifying critical tasks and

Rethinking maintenance terminology for an Industry 4.0 future

March 22, 2021
Author(s)
Melinda Hodkiewicz, Sarah Lukens, Michael Brundage, Thurston Sexton
Sensors and mathematical models have been used since the 1990's to assess the health of systems and diagnose anomalous behavior. The advent of the Internet of Things (IoT) increases the range of assets on which data can be collected cost effectively. Cloud

A Data-Driven Framework for Team Formation for Maintenance Tasks

February 16, 2021
Author(s)
Maya Reslan, Emily Hastings, Michael Brundage, Thurston Sexton
Even as maintenance evolves with new technologies, it is still a heavily human-driven domain; multiple steps in the maintenance workflow still require human expertise and intervention. Various maintenance activities require multiple maintainers, all with

Technical Language Processing: Unlocking Maintenance Knowledge

December 11, 2020
Author(s)
Michael P. Brundage, Thurston B. Sexton, Melinda Hodkiewicz, Alden A. Dima, Sarah Lukens
Out-of-the-box natural-language processing (NLP) pipelines need re-imagining to understand and meet the requirements of the engineering sector. Text-based documents account for a significant portion of data collected during the life cycle of asset

MSEC: A QUANTITATIVE RETROSPECTIVE

June 25, 2020
Author(s)
Rachael Sexton, Michael Brundage, Alden A. Dima, Michael Sharp
The Manufacturing Science and Engineering Conference (MSEC) in 2020 is the 15th annual conference put on by the Manufacturing Engineering Division (MED) of ASME. MED and ASME MSEC focuses on manufacturing sciences, technology, and applications, including

Nestor: A Tool for Natural Language Annotation of Short Texts

November 1, 2019
Author(s)
Thurston B. Sexton, Michael P. Brundage
Nestor is a software tool that annotates natural language CSV (comma-separated variable) files, with a UTF-8 encoding, using a process called tagging [1]. The outputted annotated datasets (as either a CSV or .h5 file) can be used for different analysis

Agreement Behavior of Isolated Annotators for Maintenance Work-Order Data Mining

September 27, 2019
Author(s)
Emily Hastings, Thurston Sexton, Michael Brundage, Melinda Hodkiewicz
Maintenance work orders (MWOs) are an integral part of the maintenance workflow. These documents allow technicians to capture vital aspects of a maintenance job: observed symptoms, potential causes, solutions implemented, etc. These MWOs have often been

Categorization Errors for Data Entry in Maintenance Work-Orders

September 24, 2019
Author(s)
Thurston B. Sexton, Melinda Hodkiewicz, Michael P. Brundage
In manufacturing, there is a significant push toward the digitization of processes and decision making, by increasing the level of automation and networking via cyber-physical systems, and machine learning methods that can parse useful patterns from these

Where do we start? Guidance for technology implementation in maintenance management for manufacturing

July 23, 2019
Author(s)
Michael P. Brundage, Thurston B. Sexton, Melinda Hodkiewicz, Katherine C. Morris, Jorge Arinez, Farhad Ameri, Jun Ni, Guoxian Xiao
Recent efforts in Smart Manufacturing (SM) have proven quite effective at elucidating system behavior using sensing systems, communications and computational platforms, along with statistical methods to collect and analyze real-time performance data

WHERE DO WE START? GUIDANCE FOR TECHNOLOGY IMPLEMENTATION IN MAINTENANCE MANAGEMENT FOR MANUFACTURING

July 23, 2019
Author(s)
Michael P. Brundage, Thurston B. Sexton, Melinda Hodkiewicz, Katherine C. Morris, Jorge Arinez, Farhad Ameri, Jun Ni, Guoxian Xiao
Recent efforts in Smart Manufacturing (SM) have proven quite effective at elucidating system behavior using sensing systems, communications and computational platforms, along with statistical methods to collect and analyze real-time performance data

Standards Needs for Maintenance Work Order Analysis in Manufacturing

April 3, 2019
Author(s)
Thurston Sexton, Michael Brundage
To bolster the efficiency and performance of maintenance work in manufacturing-maintenance being one of the key components to ensuring successful long-term operations-it is becoming increasingly necessary to ensure that maintenance operations are capable

Predictive Model Markup Language (PMML) Representation of Bayesian Networks: An Application in Manufacturing

October 2, 2018
Author(s)
Saideep Nannapaneni, Anantha Narayanan Narayanan, Ronay Ak, David Lechevalier, Thurston Sexton, Sankaran Mahadevan, Yung-Tsun Lee
Bayesian networks (BNs) represent a promising approach for the aggregation of multiple uncertainty sources in manufacturing networks and other engineering systems for the purposes of uncertainty quantification, risk analysis, and quality control. A

Benchmarking for keyword extraction methodologies in maintenance work orders

September 24, 2018
Author(s)
Thurston B. Sexton, Michael P. Brundage, Melinda Hodkiewicz, Thomas Smoker
Maintenance has largely remained a human-knowledge centered activity, with the primary records of maintenance activity being text-based maintenance work orders (MWOs) that attempt to encode the diagnostic processes of technicians. However, the bulk of