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View article: KPI Extraction from Maintenance Work Orders—A Comparison of Expert Labeling, Text Classification and AI-Assisted Tagging for Computing Failure Rates of Wind Turbines
KPI Extraction from Maintenance Work Orders—A Comparison of Expert Labeling, Text Classification and AI-Assisted Tagging for Computing Failure Rates of Wind Turbines Open
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. How…
View article: KPI Extraction from Maintenance Work Orders -- A Comparison of Expert Labeling, Text Classification and AI-Assisted Tagging for Computing Failure Rates of Wind Turbines
KPI Extraction from Maintenance Work Orders -- A Comparison of Expert Labeling, Text Classification and AI-Assisted Tagging for Computing Failure Rates of Wind Turbines Open
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. How…
View article: An infrastructure for curating, querying, and augmenting document data :
An infrastructure for curating, querying, and augmenting document data : Open
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 Resear…
View article: LabelVizier: Interactive Validation and Relabeling for Technical Text Annotations
LabelVizier: Interactive Validation and Relabeling for Technical Text Annotations Open
With the rapid accumulation of text data produced by data-driven techniques, the task of extracting "data annotations"--concise, high-quality data summaries from unstructured raw text--has become increasingly important. The recent advances…
View article: Impact of Data Quality on Maintenance Work Order Analysis: A Case Study in Historical HVAC Maintenance Work Orders
Impact of Data Quality on Maintenance Work Order Analysis: A Case Study in Historical HVAC Maintenance Work Orders Open
Historical data from maintenance work orders (MWOs) is a powerful source of information to improve maintenance decisions and procedures. However, data quality often impacts an analyst’s ability to calculate important Key Performance Indica…
View article: Adapting natural language processing for technical text
Adapting natural language processing for technical text Open
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 h…
View article: Rethinking Maintenance Terminology for an Industry 4.0 Future
Rethinking Maintenance Terminology for an Industry 4.0 Future Open
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 e…
View article: Data-Driven Framework for Team Formation for Maintenance Tasks
Data-Driven Framework for Team Formation for Maintenance Tasks Open
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 mainta…
View article: Technical language processing: Unlocking maintenance knowledge
Technical language processing: Unlocking maintenance knowledge Open
Out-of-the-box natural-language processing (NLP) pipelines need re-imagining to understand and meet the requirements of engineering data. Text-based documents account for a significant portion of data collected during the life cycle of ass…
View article: Organizing Tagged Knowledge: Similarity Measures and Semantic Fluency in Structure Mining
Organizing Tagged Knowledge: Similarity Measures and Semantic Fluency in Structure Mining Open
Recovering a system’s underlying structure from its historical records (also called structure mining) is essential to making valid inferences about that system’s behavior. For example, making reliable predictions about system failures base…
View article: Nestor: A Tool for Natural Language Annotation of Short Texts
Nestor: A Tool for Natural Language Annotation of Short Texts Open
Nestor is a software tool that annotates natural language CSV (comma-separated variable) files, with a UTF-8 (Unicode Transformation Format – 8-bit) encoding, using a process called tagging [1]. The objective of Nestor is to help analysts …
View article: Studies to Predict Maintenance Time Duration and Important Factors From Maintenance Workorder Data
Studies to Predict Maintenance Time Duration and Important Factors From Maintenance Workorder Data Open
Maintenance Work Orders (MWOs) are a useful way ofrecording semi-structured information regarding maintenanceactivities in a factory or other industrial setting. Analysisof these MWOs could provide valuable insights regardingthe many facet…
View article: Categorization Errors for Data Entry in Maintenance Work-Orders
Categorization Errors for Data Entry in Maintenance Work-Orders Open
In manufacturing, there is a significant push toward the digitizationof processes and decision making, by increasing thelevel of automation and networking via cyber-physical systems,and machine learning methods that can parse usefulpattern…
View article: Agreement Behavior of Isolated Annotators for Maintenance Work-Order Data Mining
Agreement Behavior of Isolated Annotators for Maintenance Work-Order Data Mining Open
Maintenance work orders (MWOs) are an integral part of themaintenance workflow. These documents allow techniciansto capture vital aspects of a maintenance job: observed symptoms,potential causes, solutions implemented, etc. TheseMWOs have …
View article: Where Do We Start? Guidance for Technology Implementation in Maintenance Management for Manufacturing
Where Do We Start? Guidance for Technology Implementation in Maintenance Management for Manufacturing Open
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 the real-time p…
View article: Benchmarking for Keyword Extraction Methodologies in Maintenance Work Orders
Benchmarking for Keyword Extraction Methodologies in Maintenance Work Orders Open
Maintenance has largely remained a human-knowledge centered activity, with the primary records of activity being textbased maintenance work orders (MWOs). However, the bulk of maintenance research does not currently attempt to quantify hum…
View article: Predictive Model Markup Language (PMML) Representation of Bayesian Networks: An Application in Manufacturing
Predictive Model Markup Language (PMML) Representation of Bayesian Networks: An Application in Manufacturing Open
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 c…
View article: Learning an Optimization Algorithm Through Human Design Iterations
Learning an Optimization Algorithm Through Human Design Iterations Open
Solving optimal design problems through crowdsourcing faces a dilemma: On the one hand, human beings have been shown to be more effective than algorithms at searching for good solutions of certain real-world problems with high-dimensional …
View article: Learning Human Search Strategies from a Crowdsourcing Game
Learning Human Search Strategies from a Crowdsourcing Game Open
There is evidence that humans can be more efficient than existing algorithms at searching for good solutions in high-dimensional and non-convex design or control spaces, potentially due to our prior knowledge and learning capability. This …