A Collaborative Data Library for Testing Prognostic Models

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Published Jul 5, 2016
Joanna Sikorska Melinda Hodkiewicz Ashwin D’Cruz Lachlan Astfalck Adrian Keating

Abstract

A web-based data management system for use by researchers and industry around the world to access suitable datasets for testing prognostic models is developed. The value of the project is in the provision of, and access to, real-world data for asset failure prediction work. In practice, it is difficult for researchers to obtain data from industrial equipment. Industry datasets are rarely shared and hardly ever published. When such data is made available, very little meta-data about the underlying asset is provided. This restricts the number and type of models that can be applied.
The solution is a data management system for three groups: researchers needing datasets, industry and academics with datasets. This paper identifies the data being sought, the system requirements and architecture, and discusses how the design is being implemented using an Agile development approach. Crucially, meta-data is stored in the database and accessed using a secure web-based front-end so as to maximize the available information, whilst obfuscating any corporate-sensitive material. The success of this prognostics data library depends on the support of the prognostic community to contribute and use the data; similar projects have been successful in the Machine Learning and Big Data communities.

How to Cite

Sikorska, J., Hodkiewicz, M., D’Cruz, A., Astfalck, L., & Keating, A. (2016). A Collaborative Data Library for Testing Prognostic Models. PHM Society European Conference, 3(1). https://doi.org/10.36001/phme.2016.v3i1.1579
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Keywords

benchmarking datasets, relational database, engineering prognostics

References
Acoustics and Vibration Database. (2013). Acoustics and Vibration Database Retrieved 8th March, 2016, from http://data-acoustics.com/
Bechhoefer, E. (2013). Condition Based Maintenance Fault Database for Testing of Diagnostic and Prognostics Algorithms Retrieved 8th March, 2016, from http://www.mfpt.org/FaultData/FaultData.htm
CALCE. (2012). CALCE Battery Group Data Retrieved 8th March, 2016, from http://www.calce.umd.edu/batteries/data.htm
Goebel, K. (2015). PCoE Datasets Retrieved 8th March, 2016, from http://ti.arc.nasa.gov/tech/dash/pcoe/prognostic-data-repository/
Heng, A., Zhang, S., Tan, A. C. C., & Mathew, J. (2009). Rotating machinery prognostics: State of the art, challenges and opportunities. Mechanical Systems and Signal Processing, 23(3), 724-739. doi: http://dx.doi.org/10.1016/j.ymssp.2008.06.009
Hodkiewicz, M. R., Kelly, P., Sikorska, J. Z., & Gouws, L. (2006). A framework to assess data quality for reliability variables. Paper presented at the World Congress on Engineering Asset Management (WCEAM), Gold Coast, Australia.
IEEE. (2012). IEEE PHM 2012 Data Challenge Retrieved 11th March 2016, 2016, from http://www.femto-st.fr/en/Research-departments/AS2M/Research-groups/PHM/IEEE-PHM-2012-Data-challenge.php
IEEE. (2014). IEEE PHM Data Challenge Retrieved 11th March 2016, 2016, from http://eng.fclab.fr/ieee-phm-2014-data-challenge/
Jardine, A. K. S., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20(7), 1483-1510. doi: http://dx.doi.org/10.1016/j.ymssp.2005.09.012
Jouin, M., Gouriveau, R., Hissel, D., Pera, M.-C., & Zerhouni, N. (2016). Particle filter-based prognostics: Review, discussion and perspectives. Mechanical Systems and Signal Processing, 72-73, 2-31.
Lee, Y. W., & Strong, D. M. (2004). Knowing-Why about Data processes and Data quality. Journal of Management Information Systems, 20(3), 13-39.
Lichman, M. (2013). UCI Machine Learning Repository Retrieved 8th March, 2016, from http://archive.ics.uci.edu/ml
MaHeMM. (2009). Railway Turnout Systems Retrieved 8th March, 2016, from http://www.aiu.edu.tr/staff/fatih.camci/datasets.html
MIT. (2016). MIT Big Data Initiative Retrieved 8th March, 2016, from http://bigdata.csail.mit.edu/
NASA. (2008). PHM08 Prognostics Data Challenge Dataset 2008. Retrieved 11th March 2016, 2016, from http://ti.arc.nasa.gov/c/13/
NASA. (2016). NASA DASHlink Retrieved 8th March, 2016, from https://c3.nasa.gov/dashlink/resources/
Paulk, M. C. (2002). Agile methodologies and process discipline. Institute for Software Research, 3.
PHM Society. (2009). 2009 PHM Challenge Competition Data Set 2009. Retrieved 11th March 2016 2016, from https://www.phmsociety.org/references/datasets
PHM Society. (2010). 2010 PHM Society Conference Data Challenge Retrieved 11th March 2016, 2016, from https://www.phmsociety.org/competition/phm/10
PHM Society. (2011). 2011 PHM Challenge Competition Data Set Retrieved 11th March 2016, 2016, from https://www.phmsociety.org/competition/phm/11
Sankararaman, S. (2015). Significance, interpretation, and quantification of uncertainty in prognostics and remaining useful life prediction. Mechanical Systems and Signal Processing, 52-53, 228-247.
Sikorska, J. Z., Hodkiewicz, M., & Ma, L. (2011). Prognostic modelling options for remaining useful life estimation by industry. Mechanical Systems and Signal Processing, 25(5), 1803-1836. doi: 10.1016/j.ymssp.2010.11.018
Vachtsevanos, G., Lewis, F., Roemer, M., Hess, A., & Wu, B. (2006). Intelligent Fault Diagnosis and Prognosis for Engineering Systems: John Wiley & Sons, Inc.
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Technical Papers

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