The role of transactional data in prognostics and health management work processes
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Published
Oct 2, 2017
Sarah Lukens
Manjish Naik
Xiaohui Hu
Donald S. Doan
Shaddy Abado
Abstract
Analytics supporting prognostics and health management (PHM) work processes traditionally leverage time-series data to monitor component states and predict fault progressions in order to positively impact performance related to safety, profitability and risk management. Developing analytical models for the purpose of monitoring is asset-specific and assumes that the data is captured and accessible. In practice, monitoring assets in real-time is reserved for highly critical assets, while all assets have transactional data stored in enterprise asset management (EAM) systems. This paper reviews methods for measuring transactional data quality and for measuring asset performance metrics and health indicators from historical maintenance records that can be used in PHM initiatives. Data from both transactional sources and from machine-measured sources should be used together to derive a complete picture of the maintenance strategies and actions in an industrial site.
How to Cite
Lukens, S., Naik, M., Hu, X., Doan, D. S., & Abado, S. (2017). The role of transactional data in prognostics and health management work processes. Annual Conference of the PHM Society, 9(1). https://doi.org/10.36001/phmconf.2017.v9i1.2473
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Keywords
CMMS, Data Quality, Review, Asset performance Management, overall equipment effectiveness (OEE)
References
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Batini, C., & Scannapieco, M. (2006). Data Quality Concepts, Methodologies and Techniques. Berlin Heidelberg: Springer-Verlag .
Bhutta, K. S., & Huq, F. (1999). Benchmarking–best practices: an integrated approach. Benchmarking: An International Journal, 6(3), 254-268.
Carter, M. C., & Kennedy, J. S. (2016). Cost-wise readiness enabled through data driven fleet management (DDFM): Measuring PHM system benefits through post implementation assessment. Prognostics and Health Management (ICPHM), 2016 IEEE International Conference on. (pp. 1-8). IEEE.
Casto, P. (2010, April/May). Creating a New Partner with Reliability Centered Operations. Uptime Magazine, pp. 9-17.
Chen, H., Hailey, D., Wang, N., & Yu, P. (2014). A review of data quality assessment methods for public health information systems. International journal of environmental research and public health, 11(5), 5170-5207.
Coble, J., & Hines, J. W. (2009). Identifying optimal prognostic parameters from data: a genetic algorithms approach. Annual conference of the
prognostics and health management society. San Diego, CA.
Coble, J., & Hines, J.W. (2011). Applying the general path model to estimation of remaining useful life. International Journal of Prognostics and Health
Management, 2(1), 71-82.
Coble, J., Ramuhalli, P., Bond, L. J., Hines, J. W., & Ipadhyaya, B. (2015). A review of prognostics and health management applications in nuclear power
plants. International Journal of Prognostics and Health Management, 6.
Esperon-Miguez, M., John, P., & Jennions, I. K. (2012). Uncertainty of performance requirements for IVHM tools according to business targets. Eur. Conf. Progn. Heal. Manag. Soc, 3, 1-11.
Feldman K., Sandborn, P., & Jazouli, T. (2008). The analysis of return on investment for PHM applied to electronic systems. Prognostics and Health
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He, B. (2016). A Machine Learning Approach for Data Unification and Its Application in Asset Performance Management. Thesis, Virginia Polytechnic Institute and State University, Blacksburg.
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Hodkiewicz, M., and Ho, M.T.W. (2016). Cleaning historical maintenance work order data for reliability analysis. Journal of Quality in Maintenance Engineering, 22(2), 146-163.
Hong, Y., & Meeker, W. Q, (2013). Field-failure predictions based on failure-time data with dynamic covariate information. Technometrics, 55(2), 135-149.
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Kahlert, A., Giljohann, S., & Klingauf, U. (2014). Costbenefit analysis and specification of componentlevel PHM systems in aircrafts. Annual Conference
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Koronios, A., Lin, S., & Gao, J. (2005). A data quality model for asset management in engineering organisations. In IQ.
Kumar, U., Galar, D., Parida, A., Stenström, C., & Berges, L. (2013). Maintenance performance metrics: a stateof-the-art review. Journal of Quality in
Maintenance Engineering, 19(3), 233-277.
Leao, B. P., Fitzgibbon, K. T., Puttini, L. C., & de Melo, G. P. (2008). Cost-benefit analysis methodology for PHM applied to legacy commercial aircraft.
Aerospace Conference, 2008 IEEE (pp. 1-13). IEEE.
Leao, B. P., Yoneyama, T., Rocha, G. C., & Fitzgibbon, K. T. (2008). Prognostics performance metrics and their relation to requirements, design, verification and cost-benefit. Prognostics and Health Management, 2008. PHM 2008. International Conference on (pp. 1-8). IEEE.
Lee, J., Wu, F., Zhao, W., Ghaffari, M., Liao, L., & Siegel, D. (2014). Prognostics and health management design for rotary machinery systems—Reviews,
methodology and applications. Mechanical systems and signal processing, 42.1, 314-334.
Lin, S., Gao, J., Koronios, A., & Chanana, V. S. G. (2007). Developing a data quality framework for asset management in engineering organisations.
International Journal of Information Quality, 1(1), 100-126.
Lu, C. J., & Meeker, W. O (1993). Using degradation measures to estimate a time-to-failure distribution. Technometrics, 35(2), 1610174.
Meeker, W. Q., & Escobar, L. A (1998). Statistical models for reliability data. New York: John Wiley & Sons, Inc.
Meeker, W. Q., & Hong, Y. (2014). Reliability meets big data: opportunities and challenges. Quality Engineering, 26(1), 102-116.
Milje, R. (2011). Engineering methodology for selecting Condition Based Maintenance. MS thesis, University of Stavanger, Norway. Retrieved from https://brage.bibsys.no/xmlui/handle/11250/182746
Molina, R., Unsworth, K., Hodkiewicz, M., & Adriasola, E. (2013). Are managerial pressure, technological control and intrinsic motivation effective in
improving data quality? Reliability Engineering & System Safety, 119, 26-34.
Muchiri, P., & Pintelon, L. (2008). Performance measurement using overall equipment effectiveness (OEE): literature review and practical application
discussion. International Journal of Production Research, 46(13), 3517-3535.
Naik, M. (2016, March 17). How Good Does Data Need to Be for Maintenance Practices? Automation.com.
Nelson, W. B. (2003). Recurrent events data analysis for product repairs, disease recurrences, and other applications. SIAM, Philadelphia, USA, ASA,
Alexandria, VA: ASA-SIAM Series on Statistics and Applied Probability.
O'Connor, P. D., & Kleyner, A. (2012). Practical Reliability Engineering. West Sussex, United Kingdom: John Wiley & Sons, Ltd.
Parida, A., & Kumar, U. (2009). Maintenance Performance Measurement Methods, Tools and Application. Maintworld, 1, 50-53.
Parida, A., Kumar, U., Galar, D., & Stenström, C. (2015). Performance measurement and management for maintenance: a literature review. Journal of Quality in Maintenance Engineering, 21(1), 2-33.
Parida, A., & Tretten, P. (2017). Condition Monitoring and Diagnosis of Modern Dynamic Complex Systems using Criticality aspect of Key Performance
Indicators. International Journal of COMADEM, 20(1), 1-9.
Rajamani, R., & Bird, J. (2016). Introduction to Prognostics and Health Management. In PHM Society Short Course: PHM Fundamentals and Case Studies (pp. 11-27). Denver.
Roberts, W. T., & Barringer, H. P. (2001). Consider using a new reliability tool: Weibull analysis for production data. Hydrocarbon Processing, 80(10), 73-84.
Saxena, A., Celaya, J., Balaban, E., Goebel, K., Saha, B., Saha, S., & Schwabacher, M. (2008). Metrics for evaluating performance of prognostic techniques. Prognostics and Health Management, 2008. PHM 2008. International Conference on (pp. 1-17). IEEE.
Saxena, A., Celaya, J., Saha, B., Saha, S., & Goebel, K. (2010). Metrics for offline evaluation of prognostic performance. International Journal of Prognostics and Health Management, 4-23.
Saxena, A., Roychoudhury, I., Celaya, J., Saha, S., Saha, B., & Goebel, K. (2010). Requirements Specification for Prognostics Performance-An Overview. AIAA Infotech@ Aerospace 2010, (p. 3398).
Saxena, A., Sankararaman, S., & Goebel, K. (2014). Performance evaluation for fleet-based and unitbased prognostic method. Second European
conference of the Prognostics and Health Management society.
Scanff, E., Feldman, K. L., Ghelam, S., Sandborn, P., Glade, M., & Foucher, B. (2007). Life cycle cost impact of using prognostic health management (PHM) for helicopter avionics. Microelectronics Reliability, 47(12), 1857-1864.
Schwabacher, M. (2005). A survey of data-driven prognostics. Infotech@ Aerospace, (p. 7002).
Schwabacher, M., & Goebel, K. (2007). A survey of artificial intelligence for prognostics. AAAI fall symposium, (pp. 107-114).
Sikorska, J., Hammond, L., & Kelly, P. (2007). Identifying failure modes retrospectively using RCM data. In ICOMS Asset management conference. Melbourne, Australia.
Smarsly, K., & Law, K. H. K. a. (2014). Decentralized fault detection and isolation in wireless structural health monitoring systems using analytical redundancy. Advances in Engineering Software, 73, 1-10. (2017). SMRP Best Practices. Atlanta, GA: Society for Maintenance & Reliability Professionals (SMRP). Retrieved from https://smrp.org/
Sun, B., Zeng, S., Kang, R., & Pecht, M. G. (2012). Benefits and challenges of system prognostics. IEEE Transactions on reliability, 61(2), 323-335.
Vachtsevanos, V., Frank, L., Michael, R., Andrew, H., & Biqing, W. (2006). Intelligent fault diagnosis and prognosis for engineering systems. Hoboken, New Jersey: John Wiley & Sons, Inc.
Weiskopf, N. G., & Weng, C. N. G. (2013). Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research. Journal of the American Medical Informatics Association, 20(1), 144-151.
Whitt, R.E. (2009). Improving asset performance and process safety. Petroleum Technology Quarterly, Q3, 125-131.
Wikipedia. (2017, June 1). Retrieved from Transaction data: https://en.wikipedia.org/wiki/Transaction_data
Woodall, P., Gao, J., Parlikad, A., & Koronios, A. (2015). Classifying Data Quality Problems in Asset Management. In Engineering Asset Management-
Systems, Professional Practices and Certification (pp. 321-334). Springer International Publishing.
Yu, J. and Gulliver, S. (2011). Improving aircraft maintenance, repair, and overhaul: A novel text mining approach. In International Conference on
Intelligent Computing and Intelligent Systems, Institute of Electrical and Electronics Engineers, Guangzhou, China. (pp. 1-5).
Banks, J., & Merenich, J. (2007). Cost benefit analysis for asset health management technology. Reliability and Maintainability Symposium, 2007. RAMS'07. Annual, (pp. 95-1000).
Banks, J., Reichard, K., Crow, E., & Nickell, K. (2009). How engineers can conduct cost-benefit analysis for PHM systems. IEEE Aerospace and Electronic
Systems Magazine, 24(3), 22-30.
Batini, C., & Scannapieco, M. (2006). Data Quality Concepts, Methodologies and Techniques. Berlin Heidelberg: Springer-Verlag .
Bhutta, K. S., & Huq, F. (1999). Benchmarking–best practices: an integrated approach. Benchmarking: An International Journal, 6(3), 254-268.
Carter, M. C., & Kennedy, J. S. (2016). Cost-wise readiness enabled through data driven fleet management (DDFM): Measuring PHM system benefits through post implementation assessment. Prognostics and Health Management (ICPHM), 2016 IEEE International Conference on. (pp. 1-8). IEEE.
Casto, P. (2010, April/May). Creating a New Partner with Reliability Centered Operations. Uptime Magazine, pp. 9-17.
Chen, H., Hailey, D., Wang, N., & Yu, P. (2014). A review of data quality assessment methods for public health information systems. International journal of environmental research and public health, 11(5), 5170-5207.
Coble, J., & Hines, J. W. (2009). Identifying optimal prognostic parameters from data: a genetic algorithms approach. Annual conference of the
prognostics and health management society. San Diego, CA.
Coble, J., & Hines, J.W. (2011). Applying the general path model to estimation of remaining useful life. International Journal of Prognostics and Health
Management, 2(1), 71-82.
Coble, J., Ramuhalli, P., Bond, L. J., Hines, J. W., & Ipadhyaya, B. (2015). A review of prognostics and health management applications in nuclear power
plants. International Journal of Prognostics and Health Management, 6.
Esperon-Miguez, M., John, P., & Jennions, I. K. (2012). Uncertainty of performance requirements for IVHM tools according to business targets. Eur. Conf. Progn. Heal. Manag. Soc, 3, 1-11.
Feldman K., Sandborn, P., & Jazouli, T. (2008). The analysis of return on investment for PHM applied to electronic systems. Prognostics and Health
Management, 2008. PHM 2008. International Conference on (pp. 1-9). Denver, CO: IEEE.
Gulati, R. (2009). Maintenance and reliability best practices. New York: Industrial Press Inc.
Goebel, K., Daigle, M., Saxena, A., Sankararaman, S., Roychoudhury, I. & Celaya, J.R. (2017). Prognostics: The science of making predictions. Publisher: Author.
He, B. (2016). A Machine Learning Approach for Data Unification and Its Application in Asset Performance Management. Thesis, Virginia Polytechnic Institute and State University, Blacksburg.
Hines, W., & Usynin, A (2008). Current computational trends in equipment prognostics. International Journal of Computational Intelligence Systems,
1(1), 94-102.
Hodkiewicz, M., Kelly, P., Sikorska, J., & Gouws, L. (2006). A framework to assess data quality for reliability variables. Engineering Asset Management, 137-147.
Hodkiewicz, M., and Ho, M.T.W. (2016). Cleaning historical maintenance work order data for reliability analysis. Journal of Quality in Maintenance Engineering, 22(2), 146-163.
Hong, Y., & Meeker, W. Q, (2013). Field-failure predictions based on failure-time data with dynamic covariate information. Technometrics, 55(2), 135-149.
International Standards Organization (ISO) (2013). Asset management -- Overview, principles and terminology. In ISO 55000. Genève, Switzerland:
International Standards Organization.
International Standards Organization (ISO) (2004). Petroleum, petrochemical and natural gas industries—Collection and exchange of reliability
and maintenance data for equipment. In ISO 14224. Genève, Switzerland: International Standards Organization.
Kacprzynski, G. J., Roemer, M. J., & Hess, A. J. (2002). Health management system design: Development, simulation and cost/benefit optimization. Aerospace Conference Proceedings IEEE, 6.
Kahlert, A., Giljohann, S., & Klingauf, U. (2014). Costbenefit analysis and specification of componentlevel PHM systems in aircrafts. Annual Conference
of the Prognostics and Health Management Society.
Kaydos, W. (1999). Operational performance measurement: increasing total productivity. Boca Raton, Florida: CRC Press LLC.
Koronios, A., Lin, S., & Gao, J. (2005). A data quality model for asset management in engineering organisations. In IQ.
Kumar, U., Galar, D., Parida, A., Stenström, C., & Berges, L. (2013). Maintenance performance metrics: a stateof-the-art review. Journal of Quality in
Maintenance Engineering, 19(3), 233-277.
Leao, B. P., Fitzgibbon, K. T., Puttini, L. C., & de Melo, G. P. (2008). Cost-benefit analysis methodology for PHM applied to legacy commercial aircraft.
Aerospace Conference, 2008 IEEE (pp. 1-13). IEEE.
Leao, B. P., Yoneyama, T., Rocha, G. C., & Fitzgibbon, K. T. (2008). Prognostics performance metrics and their relation to requirements, design, verification and cost-benefit. Prognostics and Health Management, 2008. PHM 2008. International Conference on (pp. 1-8). IEEE.
Lee, J., Wu, F., Zhao, W., Ghaffari, M., Liao, L., & Siegel, D. (2014). Prognostics and health management design for rotary machinery systems—Reviews,
methodology and applications. Mechanical systems and signal processing, 42.1, 314-334.
Lin, S., Gao, J., Koronios, A., & Chanana, V. S. G. (2007). Developing a data quality framework for asset management in engineering organisations.
International Journal of Information Quality, 1(1), 100-126.
Lu, C. J., & Meeker, W. O (1993). Using degradation measures to estimate a time-to-failure distribution. Technometrics, 35(2), 1610174.
Meeker, W. Q., & Escobar, L. A (1998). Statistical models for reliability data. New York: John Wiley & Sons, Inc.
Meeker, W. Q., & Hong, Y. (2014). Reliability meets big data: opportunities and challenges. Quality Engineering, 26(1), 102-116.
Milje, R. (2011). Engineering methodology for selecting Condition Based Maintenance. MS thesis, University of Stavanger, Norway. Retrieved from https://brage.bibsys.no/xmlui/handle/11250/182746
Molina, R., Unsworth, K., Hodkiewicz, M., & Adriasola, E. (2013). Are managerial pressure, technological control and intrinsic motivation effective in
improving data quality? Reliability Engineering & System Safety, 119, 26-34.
Muchiri, P., & Pintelon, L. (2008). Performance measurement using overall equipment effectiveness (OEE): literature review and practical application
discussion. International Journal of Production Research, 46(13), 3517-3535.
Naik, M. (2016, March 17). How Good Does Data Need to Be for Maintenance Practices? Automation.com.
Nelson, W. B. (2003). Recurrent events data analysis for product repairs, disease recurrences, and other applications. SIAM, Philadelphia, USA, ASA,
Alexandria, VA: ASA-SIAM Series on Statistics and Applied Probability.
O'Connor, P. D., & Kleyner, A. (2012). Practical Reliability Engineering. West Sussex, United Kingdom: John Wiley & Sons, Ltd.
Parida, A., & Kumar, U. (2009). Maintenance Performance Measurement Methods, Tools and Application. Maintworld, 1, 50-53.
Parida, A., Kumar, U., Galar, D., & Stenström, C. (2015). Performance measurement and management for maintenance: a literature review. Journal of Quality in Maintenance Engineering, 21(1), 2-33.
Parida, A., & Tretten, P. (2017). Condition Monitoring and Diagnosis of Modern Dynamic Complex Systems using Criticality aspect of Key Performance
Indicators. International Journal of COMADEM, 20(1), 1-9.
Rajamani, R., & Bird, J. (2016). Introduction to Prognostics and Health Management. In PHM Society Short Course: PHM Fundamentals and Case Studies (pp. 11-27). Denver.
Roberts, W. T., & Barringer, H. P. (2001). Consider using a new reliability tool: Weibull analysis for production data. Hydrocarbon Processing, 80(10), 73-84.
Saxena, A., Celaya, J., Balaban, E., Goebel, K., Saha, B., Saha, S., & Schwabacher, M. (2008). Metrics for evaluating performance of prognostic techniques. Prognostics and Health Management, 2008. PHM 2008. International Conference on (pp. 1-17). IEEE.
Saxena, A., Celaya, J., Saha, B., Saha, S., & Goebel, K. (2010). Metrics for offline evaluation of prognostic performance. International Journal of Prognostics and Health Management, 4-23.
Saxena, A., Roychoudhury, I., Celaya, J., Saha, S., Saha, B., & Goebel, K. (2010). Requirements Specification for Prognostics Performance-An Overview. AIAA Infotech@ Aerospace 2010, (p. 3398).
Saxena, A., Sankararaman, S., & Goebel, K. (2014). Performance evaluation for fleet-based and unitbased prognostic method. Second European
conference of the Prognostics and Health Management society.
Scanff, E., Feldman, K. L., Ghelam, S., Sandborn, P., Glade, M., & Foucher, B. (2007). Life cycle cost impact of using prognostic health management (PHM) for helicopter avionics. Microelectronics Reliability, 47(12), 1857-1864.
Schwabacher, M. (2005). A survey of data-driven prognostics. Infotech@ Aerospace, (p. 7002).
Schwabacher, M., & Goebel, K. (2007). A survey of artificial intelligence for prognostics. AAAI fall symposium, (pp. 107-114).
Sikorska, J., Hammond, L., & Kelly, P. (2007). Identifying failure modes retrospectively using RCM data. In ICOMS Asset management conference. Melbourne, Australia.
Smarsly, K., & Law, K. H. K. a. (2014). Decentralized fault detection and isolation in wireless structural health monitoring systems using analytical redundancy. Advances in Engineering Software, 73, 1-10. (2017). SMRP Best Practices. Atlanta, GA: Society for Maintenance & Reliability Professionals (SMRP). Retrieved from https://smrp.org/
Sun, B., Zeng, S., Kang, R., & Pecht, M. G. (2012). Benefits and challenges of system prognostics. IEEE Transactions on reliability, 61(2), 323-335.
Vachtsevanos, V., Frank, L., Michael, R., Andrew, H., & Biqing, W. (2006). Intelligent fault diagnosis and prognosis for engineering systems. Hoboken, New Jersey: John Wiley & Sons, Inc.
Weiskopf, N. G., & Weng, C. N. G. (2013). Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research. Journal of the American Medical Informatics Association, 20(1), 144-151.
Whitt, R.E. (2009). Improving asset performance and process safety. Petroleum Technology Quarterly, Q3, 125-131.
Wikipedia. (2017, June 1). Retrieved from Transaction data: https://en.wikipedia.org/wiki/Transaction_data
Woodall, P., Gao, J., Parlikad, A., & Koronios, A. (2015). Classifying Data Quality Problems in Asset Management. In Engineering Asset Management-
Systems, Professional Practices and Certification (pp. 321-334). Springer International Publishing.
Yu, J. and Gulliver, S. (2011). Improving aircraft maintenance, repair, and overhaul: A novel text mining approach. In International Conference on
Intelligent Computing and Intelligent Systems, Institute of Electrical and Electronics Engineers, Guangzhou, China. (pp. 1-5).
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