Requirements and Data Integrity Considerations for Diagnostics Testbeds
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Published
Mar 24, 2021
Ioannis Bardakis
Octavian Niculita
Peter Wallace
Abstract
The process of generating high quality data for the test and evaluation of diagnostic and prognostic algorithms is still of high importance to the Prognostics and Health Management (PHM) research community. To support these efforts a testbed has been designed, manufactured and commissioned. It has specifically been designed in order to replicate several component degradation faults with high accuracy and high repeatability. This paper documents the design, requirements and the data integrity elements of this benchmark hydraulic system. This document consolidates the process of designing diagnostics testbeds as at present there is a lack of literature on how diagnostics testbeds should be built and is intended to serve as a starting point and quick reference guide for engineers and researchers intending to design and develop a testbed to test and validate PHM applications. The first part of this paper highlights design requirements for all the design aspects for such testbeds with great consideration for industry standards and best practices covering the achievement of electromagnetic compatibility (EMC) and noise mitigation, as well as operators’ safety and equipment protection. The second part of the paper put great emphasis on data integrity elements of the data generated by this testbed (describing the system under healthy and faulty conditions) before it is actually used for system characterization or by diagnostics and prognostics algorithms.
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Keywords
testbed, Design, Requirements, PHM Standards, standards, Degradation of Nominal Performance, Data Uncertainty, Data Integrity
References
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Bentley, J. (2005). Principles of measurement systems. 4th edition. New Jersey, USA: Pearson Prentice Hall.
Brotherton, T., Grabill, P., Friend, R., Sotomayer, B., & Berry, J. (2003). A testbed for data fusion for helicopter diagnostics and prognostics. In IEEE Aerospace Conference Proceedings (Cat. No.03TH8652) (Vol. 7, pp. 3357–3370). IEEE.
Childs, P., (2019). Mechanical design engineering handbook. Amsterdam: Butterworth Heinemann/Elsevier. pp.1-47. Daigle, M., Kulkarni, C., & Gorospe, G. (2014). Application of model-based prognostics to a pneumatic valves testbed. In Proceedings of the 2014 IEEE Aerospace Conference (pp. 1–8). IEEE.
Delaney, M., Browder, M., & Flynn, J. (2009). Solder Joint Health Monitoring Testbed. Military/Aerospace Programmable Logic Device (MAPLD 2009).
Diehl, E., & Tang, J. (2016). Predictive Modelling of a Two- Stage Gearbox towards Fault Detection. Shock and Vibration, 2016, vol. 17, pp. 1-13. https://doi.org/10.1155/2016/9638325
DNV GL, (2018). Digital Twins and Sensor Monitoring. Oslo, Norway: DNV GL (Det Norske Veritas Germanischer Lloyd).
Eker, O., Camci, F., & Jennions, I.K. (2016). Physics-based prognostic modelling of filter clogging phenomena. Mechanical Systems and Signal Processing [Online], 75, pp. 395–412.
Feiyi, R., & Jinsong Y. (2015). Fault diagnosis methods for advanced diagnostics and prognostics testbed (ADAPT): A review. In Proceedings of the 12th IEEE International Conference on Electronic Measurement & Instruments (ICEMI) (Vol. 1, pp. 175–180). IEEE.
Frosina, E., Senatore, A., & Rigosi, M. (2017). Study of a High-Pressure External Gear Pump with a Computational Fluid Dynamic Modeling Approach. Energies, 10(8).
Hess, A., Hardman, W., Chin, H., & Gill, J. (2000). The US Navy’s Helicopter Integrated Diagnostics System (HIDS) Program: Power Drive Train Crack Detection Diagnostics and Prognostics Life Usage Monitoring and Damage Tolerance; Techniques, Methodologies, and Experiences. Naval Air Warfare Centre Aircraft Div Patuxent River MD.
Hess, A., & Hardman, W. (2002). Seeded fault testing in support of mechanical systems prognostic development. In Proceedings, IEEE Aerospace Conference. IEEE.
Hess, A., Ahne, R., Hardman, W., & Fila, L. (2003). A USN Strategy for Mechanical and Propulsion Systems Diagnostics and Prognostics, Life Usage Monitoring and Damage Tolerance: Applications to Aging Aircraft Problems. Naval Air Warfare Centre Aircraft Div Patuxent River MD.
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Kulkarni, C., Daigle, M., Gorospe, G., & Goebel, K. (2017). Experimental Validation of Model-Based Prognostics for Pneumatic Valves. International Journal of Prognostics and Health Management, vol. 8(018).
Kwon, O., Lee, N., & Shin, B. (2014). Data quality management, data usage experience and acquisition intention of big data analytics. International Journal of Information Management, 34(3), 387–394.
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Lin, Y., Skaf, Z., & Jennions, I.K. (2017). A Bayesian approach to fault identification in the presence of multi- component degradation. International Journal of Prognostics and Health Management, Volume 8-004.
Liu, Y., Ma, B., Zheng, C., & Zhang, S. (2015). Degradation modelling and experiment of electro-hydraulic shift valve in contamination circumstances. Advances in Mechanical Engineering, 7(5), pp. 1–9.
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Niculita, O., Skaf, Z., & Jennions, I.K. (2014). The Application of Bayesian Change Point Detection in UAV Fuel Systems. In Procedia CIRP vol. 22, pp. 115– 121.Elsevier, B.V.
Orsagh, R., Roemer, M., Sheldon, J., & Klenke, C. (2004). A Comprehensive Prognostics Approach for Predicting Gas Turbine Engine Bearing Life. Proceedings of IGTI TurboExpo, Vienna.
Poll, S., Patterson-Hine, A., Camisa, J., Garcia, D., Hall, D., Lee, C., Mengshoel, O.J., Neukom, C., Nishikawa, D.,
Sweet, A., Yentus, S., Roychoudhury, I., Daigle, M., Biswa, G., & Koutsoukos, X. (2007). Advanced Diagnostics and Prognostics Testbed. Nashville, TN, USA: 8th International Workshop on Principles of Diagnosis. pp. 178-185.
Roemer, M., Byington C., & Kacprzynski, G. (2007). Prognosis Algorithm Design and Examples, Technical Workshop. PHM/CBM Workshop & User's Forum. Florida, USA, November 13-15.
Sander, P., & Wang, W. (2000). Maintenance and reliability. International Journal of Production Economics, vol. 67(1), 1–2.
Sands, N., & Slaugenhaupt, R. (2017). Future-proof Automation Systems Design with Standards [technical lecture]. International Society of Automation Online Technical Lecture. Pittsburgh, USA. June 29.
Sankararaman, S., & Goebel, K. (2015). Uncertainty in prognostics and systems health management. International Journal of Prognostics and Health Management, 6.
Skaf, Z., Eker, O., & Jennions, I.K. (2015). A Simple State- Based Prognostic Model for Filter Clogging. In Procedia CIRP (Vol. 38, pp. 177–182). Elsevier B.V.
Wang, D., Tsui, K., & Miao, Q. (2018). Prognostics and Health Management: A Review of Vibration Based Bearing and Gear Health Indicators. IEEE Access, 6, 665–676.
Watson, M., Byington, C., & Behbahani, A. (2007). Very High Frequency Monitoring System for Engine Gearbox and Generator Health Management Pittsburgh, USA: SAE International.
Zhang, Z., & Zhang, P. (2015). Seeing around the corner: an analytic approach for predictive maintenance using sensor data. Journal of Management Analytics, vol. 2(4), 1–18.
Zongbin, C., Zhiqiang, L., Rongwu, X., & Jian, L. (2018). Simulation and Test of Gear Pump Flow Pulsation. International Journal of Fluid Machinery and Systems, 11(3), 265–272.
Bardakis, I. (2019). A system-level, performance-oriented investigation of degradation. Thesis (MSc) Glasgow Caledonian University.
Bentley, J. (2005). Principles of measurement systems. 4th edition. New Jersey, USA: Pearson Prentice Hall.
Brotherton, T., Grabill, P., Friend, R., Sotomayer, B., & Berry, J. (2003). A testbed for data fusion for helicopter diagnostics and prognostics. In IEEE Aerospace Conference Proceedings (Cat. No.03TH8652) (Vol. 7, pp. 3357–3370). IEEE.
Childs, P., (2019). Mechanical design engineering handbook. Amsterdam: Butterworth Heinemann/Elsevier. pp.1-47. Daigle, M., Kulkarni, C., & Gorospe, G. (2014). Application of model-based prognostics to a pneumatic valves testbed. In Proceedings of the 2014 IEEE Aerospace Conference (pp. 1–8). IEEE.
Delaney, M., Browder, M., & Flynn, J. (2009). Solder Joint Health Monitoring Testbed. Military/Aerospace Programmable Logic Device (MAPLD 2009).
Diehl, E., & Tang, J. (2016). Predictive Modelling of a Two- Stage Gearbox towards Fault Detection. Shock and Vibration, 2016, vol. 17, pp. 1-13. https://doi.org/10.1155/2016/9638325
DNV GL, (2018). Digital Twins and Sensor Monitoring. Oslo, Norway: DNV GL (Det Norske Veritas Germanischer Lloyd).
Eker, O., Camci, F., & Jennions, I.K. (2016). Physics-based prognostic modelling of filter clogging phenomena. Mechanical Systems and Signal Processing [Online], 75, pp. 395–412.
Feiyi, R., & Jinsong Y. (2015). Fault diagnosis methods for advanced diagnostics and prognostics testbed (ADAPT): A review. In Proceedings of the 12th IEEE International Conference on Electronic Measurement & Instruments (ICEMI) (Vol. 1, pp. 175–180). IEEE.
Frosina, E., Senatore, A., & Rigosi, M. (2017). Study of a High-Pressure External Gear Pump with a Computational Fluid Dynamic Modeling Approach. Energies, 10(8).
Hess, A., Hardman, W., Chin, H., & Gill, J. (2000). The US Navy’s Helicopter Integrated Diagnostics System (HIDS) Program: Power Drive Train Crack Detection Diagnostics and Prognostics Life Usage Monitoring and Damage Tolerance; Techniques, Methodologies, and Experiences. Naval Air Warfare Centre Aircraft Div Patuxent River MD.
Hess, A., & Hardman, W. (2002). Seeded fault testing in support of mechanical systems prognostic development. In Proceedings, IEEE Aerospace Conference. IEEE.
Hess, A., Ahne, R., Hardman, W., & Fila, L. (2003). A USN Strategy for Mechanical and Propulsion Systems Diagnostics and Prognostics, Life Usage Monitoring and Damage Tolerance: Applications to Aging Aircraft Problems. Naval Air Warfare Centre Aircraft Div Patuxent River MD.
Kulkarni, C., Daigle, M., & Goebel, K. (2013). Implementation of prognostic methodologies to cryogenic propellant loading testbed. In 2013 IEEE AUTOTESTCON (pp. 1–7). IEEE.
Kulkarni, C., Daigle, M., Gorospe, G., & Goebel, K. (2017). Experimental Validation of Model-Based Prognostics for Pneumatic Valves. International Journal of Prognostics and Health Management, vol. 8(018).
Kwon, O., Lee, N., & Shin, B. (2014). Data quality management, data usage experience and acquisition intention of big data analytics. International Journal of Information Management, 34(3), 387–394.
Liao, L., & Pavel, R. (2013). Machinery time to failure prediction - Case study and lesson learned for a spindle bearing application. In 2013 IEEE Conference on Prognostics and Health Management (PHM) (pp. 1–11). IEEE.
Limon, S., Yadav, O., & Liao, H. (2017). A literature review on planning and analysis of accelerated testing for reliability assessment. Quality and Reliability Engineering International, vol. 33(8), 2361–2383.
Lin, Y., Skaf, Z., & Jennions, I.K. (2017). A Bayesian approach to fault identification in the presence of multi- component degradation. International Journal of Prognostics and Health Management, Volume 8-004.
Liu, Y., Ma, B., Zheng, C., & Zhang, S. (2015). Degradation modelling and experiment of electro-hydraulic shift valve in contamination circumstances. Advances in Mechanical Engineering, 7(5), pp. 1–9.
Madhikermi, M., Buda, A., Dave, B., & Framling, K. (2017). Key data quality pitfalls for condition-based maintenance. In 2017 2nd International Conference on System Reliability and Safety (ICSRS) vol. 2018, pp. 474–480. IEEE.
Manring, N., & Kasaragadda, S. (2003). The Theoretical Flow Ripple of an External Gear Pump. Journal of Dynamic Systems, Measurement, and Control, 125(3).
Niculita, O., Skaf, Z., & Jennions, I.K. (2014). The Application of Bayesian Change Point Detection in UAV Fuel Systems. In Procedia CIRP vol. 22, pp. 115– 121.Elsevier, B.V.
Orsagh, R., Roemer, M., Sheldon, J., & Klenke, C. (2004). A Comprehensive Prognostics Approach for Predicting Gas Turbine Engine Bearing Life. Proceedings of IGTI TurboExpo, Vienna.
Poll, S., Patterson-Hine, A., Camisa, J., Garcia, D., Hall, D., Lee, C., Mengshoel, O.J., Neukom, C., Nishikawa, D.,
Sweet, A., Yentus, S., Roychoudhury, I., Daigle, M., Biswa, G., & Koutsoukos, X. (2007). Advanced Diagnostics and Prognostics Testbed. Nashville, TN, USA: 8th International Workshop on Principles of Diagnosis. pp. 178-185.
Roemer, M., Byington C., & Kacprzynski, G. (2007). Prognosis Algorithm Design and Examples, Technical Workshop. PHM/CBM Workshop & User's Forum. Florida, USA, November 13-15.
Sander, P., & Wang, W. (2000). Maintenance and reliability. International Journal of Production Economics, vol. 67(1), 1–2.
Sands, N., & Slaugenhaupt, R. (2017). Future-proof Automation Systems Design with Standards [technical lecture]. International Society of Automation Online Technical Lecture. Pittsburgh, USA. June 29.
Sankararaman, S., & Goebel, K. (2015). Uncertainty in prognostics and systems health management. International Journal of Prognostics and Health Management, 6.
Skaf, Z., Eker, O., & Jennions, I.K. (2015). A Simple State- Based Prognostic Model for Filter Clogging. In Procedia CIRP (Vol. 38, pp. 177–182). Elsevier B.V.
Wang, D., Tsui, K., & Miao, Q. (2018). Prognostics and Health Management: A Review of Vibration Based Bearing and Gear Health Indicators. IEEE Access, 6, 665–676.
Watson, M., Byington, C., & Behbahani, A. (2007). Very High Frequency Monitoring System for Engine Gearbox and Generator Health Management Pittsburgh, USA: SAE International.
Zhang, Z., & Zhang, P. (2015). Seeing around the corner: an analytic approach for predictive maintenance using sensor data. Journal of Management Analytics, vol. 2(4), 1–18.
Zongbin, C., Zhiqiang, L., Rongwu, X., & Jian, L. (2018). Simulation and Test of Gear Pump Flow Pulsation. International Journal of Fluid Machinery and Systems, 11(3), 265–272.
Section
Technical Papers