NASA LogoNTRSNTRS - NASA Technical Reports ServerSearchmore_vertCollectionsAboutNewsHelpLogin Back to ResultsEngine Icing Data - An Analytics ApproachEngine icing researchers at the NASA Glenn Research Center use the Escort data acquisition system in the Propulsion Systems Laboratory (PSL) to generate and collect a tremendous amount of data every day. Currently these researchers spend countless hours processing and formatting their data, selecting important variables, and plotting relationships between variables, all by hand, generally analyzing data in a spreadsheet-style program (such as Microsoft Excel). Though spreadsheet-style analysis is familiar and intuitive to many, processing data in spreadsheets is often unreproducible and small mistakes are easily overlooked. Spreadsheet-style analysis is also time inefficient. The same formatting, processing, and plotting procedure has to be repeated for every dataset, which leads to researchers performing the same tedious data munging process over and over instead of making discoveries within their data. This paper documents a data analysis tool written in Python hosted in a Jupyter notebook that vastly simplifies the analysis process. From the file path of any folder containing time series datasets, this tool batch loads every dataset in the folder, processes the datasets in parallel, and ingests them into a widget where users can search for and interactively plot subsets of columns in a number of ways with a click of a button, easily and intuitively comparing their data and discovering interesting dynamics. Furthermore, comparing variables across data sets and integrating video data (while extremely difficult with spreadsheet-style programs) is quite simplified in this tool. This tool has also gathered interest outside the engine icing branch, and will be used by researchers across NASA Glenn Research Center. This project exemplifies the enormous benefit of automating data processing, analysis, and visualization, and will help researchers move from raw data to insight in a much smaller time frame.Document ID20170004522Acquisition SourceGlenn Research CenterDocument TypeTechnical Memorandum (TM)AuthorsFitzgerald, Brooke A.*(Hampshire Coll. Amherst, MA, United States)*Flegel, Ashlie B.*(NASA Glenn Research Center Cleveland, OH United States)*Date AcquiredMay 10, 2017Publication DateMay 1, 2017Subject CategoryComputer Programming And SoftwareReport/Patent NumberGRC-E-DAA-TN40097E-19354NASA/TM-2017-219487Funding Number(s)WBS: WBS 081876.02.03.08Distribution LimitsPublicCopyrightPublic Use Permitted.KeywordsEngine IcingAnalyticsMachine LearningBig DataAvailable DownloadsNameType20170004522.pdfSTIcloud_downloadcontent_copyvisibilityRelated RecordsThere are no records associated with this record.visibility_offNo Preview Available
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