Abstract
Inspired by the concept of the health of the human body, the state of health (SoH) determination of products has been gaining importance for preventive maintenance and product lifetime extension. In electronics, Remaining Useful Life (RUL) estimates often focus on temperature as the key ageing parameter. They neglect other influential factors such as humidity and vibration. This article proposes a method for determining product SoH which combines the analysis methods FMMEA and Fault Tree Analysis (FTA) for a more relevant identification of the causes and reasons of failures. The proposed method allows the estimation of SoH based on several health indicators and hence takes into consideration several factors of product degradation. The product’s SoH is obtained by aggregating the SoH derived from each single ageing factor through the use of multi-criteria techniques, such as Analytic Hierarchy Process (AHP) and weighted sum methods. The article discusses this method’s potential and limitations based on insights gained from its initial application to a consumer product.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Forti, V., Baldé, C.P., Kuehr, R., Bel, G.: The Global E-waste Monitor 2020: Quantities, flows and the circular economy potential, United Nations University (UNU)/United Nations Institute for Training and Research (UNITAR) – co-hosted SCYCLE Programme, International Telecommunication Union (ITU) & International Solid Waste Association (ISWA), Bonn/Geneva/Rotterdam (2020)
International Electrotechnical Commission (IEC): IEC 61508, Functional safety of electrical/electronic/programmable electronic safety-related systems – parts 1–7 (2010)
International Organization for Standardization (ISO): ISO 26262 (all parts), Road vehicles – Functional safety (2018). https://viewerbdc.afnor.org/pdf/viewer/v1-jBrYh6Uw1. Accessed 15 June 2022
Messnarz, R., et al.: Implementing functional safety standards – experiences from the trials about required knowledge and competencies (SafEUr). In: McCaffery, F., O’Connor, R.V., Messnarz, R. (eds.) EuroSPI 2013. Communications in Computer and Information Science, vol. 364, pp. 323–332 . Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-39179-8_29
Monchy, F.: Maintenance: méthodes et organisations pour une meilleure productivité, 3e édition (2012). https://unr-ra-scholarvox-com.sid2nomade-1.grenet.fr/book/88809322. Accessed 23 Mar 2021
Zwingelstein, G.: La maintenance prédictive intelligente pour l’industrie 4.0, Ref: TIP095WEB - “Maintenance” (2019). https://www-techniques-ingenieur-fr.sid2nomade-1.grenet.fr/base-documentaire/genie-industriel-th6/mise-en-uvre-de-la-maintenance-42136210/la-maintenance-predictive-intelligente-pour-l-industrie-4-0-mt9572/. Accessed 28 Apr 2021
Mathew, S., Pecht, M.: Prognostics of Systems: Approaches and Applications. https://www.prognostics.umd.edu/calcepapers/COMADEM2011.pdf. Accessed 19 June 2021
Si, X.S., Wang, W., Hu, C.H., Zhou, D.H.: Remaining useful life estimation - a review on the statistical data driven approaches. Eur. J. Oper. Res. 213(1), 1–14 (2011). https://doi.org/10.1016/j.ejor.2010.11.018.
Bhargava, C., et al.: Review of Health Prognostics and Condition Monitoring of Electronic Components (2020). https://doi.org/10.1109/ACCESS.2020.2989410
Rafiee, J., Arvani, F., Harifi, A., Sadeghi, M.H.: Intelligent condition monitoring of a gearbox using artificial neural network. Mech. Syst. Signal Process. 21(4), 1746–1754 (2007). https://doi.org/10.1016/J.YMSSP.2006.08.005
Gebraeel, N.Z., Lawley, M.A.: A neural network degradation model for computing and updating residual life distributions. IEEE Trans. Autom. Sci. Eng. 5(1), 154–163 (2008). https://doi.org/10.1109/TASE.2007.910302
Ahmad, W., Khan, S.A., Islam, M.M.M., Kim, J.M.: A reliable technique for remaining useful life estimation of rolling element bearings using dynamic regression models. Reliab. Eng. Syst. Saf. 184, 67–76 (2019). https://doi.org/10.1016/j.ress.2018.02.003
Khelif, R., Chebel-Morello, B., Malinowski, S., Laajili, E., Fnaiech, F., Zerhouni, N.: Direct remaining useful life estimation based on support vector regression. IEEE Trans. Ind. Electron. 64(3), 2276–2285 (2017). https://doi.org/10.1109/TIE.2016.2623260
Zhang, J., Hu, C., Si, X., Zhou, Z., Du, D.: Remaining useful life estimation for systems with time-varying mean and variance of degradation processes. Qual. Reliab. Eng. Int. 30(6), 829–841 (2014). https://doi.org/10.1002/qre.1705
Elattar, H.M., Elminir, H.K., Riad, A.M.: Prognostics: a literature review. Complex Intell. Syst. 2(2), 125–154 (2016). https://doi.org/10.1007/s40747-016-0019-3
Pecht, M., Dasgupta, A.: Physics-of-failure: an approach to reliable product development. In: IEEE 1995 International Integrated Reliability Workshop. Final Report, pp. 1–4 (1995). https://doi.org/10.1109/IRWS.1995.493566.
Kulkarni, C., Biswas, G., Koutsoukos, X., Goebel, K., Celaya+, J.: Physics of failure models for capacitor degradation in DC-DC converters. In: The Maintenance and Reliability Conference in Knoxville, TN, USA (2010)
Yin, C.Y., Lu, H., Musallam, M., Bailey, C., Johnson, C.M.: A physics-of-failure based prognostic method for power modules, pp. 1190–1195 (2009). https://doi.org/10.1109/EPTC.2008.4763591.
Lan, J.S., Wu, M.L.: Physics of failure based simulation and experimental testing of quad flat no-lead package. In: Proceedings - Electronic Components and Technology Conference, pp. 2144–2149 (2019). https://doi.org/10.1109/ECTC.2019.00-26
McLeish, J.: Physics of failure based simulated aided/guided accelerated life testing. In: Proceedings - Annual Reliability and Maintainability Symposium (2018). https://doi.org/10.1109/RAM.2018.8462987
Zhu, S.P., Huang, H.Z., Peng, W., Wang, H.K., Mahadevan, S.: Probabilistic physics of failure-based framework for fatigue life prediction of aircraft gas turbine discs under uncertainty. Reliab. Eng. Syst. Saf. 146, 1–12 (2016). https://doi.org/10.1016/J.RESS.2015.10.002
Li, H., Huang, H.Z., Li, Y.F., Zhou, J., Mi, J.: Physics of failure-based reliability prediction of turbine blades using multi-source information fusion. Appl. Soft Comput. 72, 624–635 (2018). https://doi.org/10.1016/J.ASOC.2018.05.015
Shao, Y., Kang, R.: A life prediction method for O-ring static seal structure based on physics of failure. In: Proceedings of 2014 Prognostics and System Health Management Conference, PHM 2014, pp. 16–21 (2014). https://doi.org/10.1109/PHM.2014.6988124
Gu, J., Pecht, M.: Prognostics and health management using physics-of-failure. In: Proceedings - Annual Reliability and Maintainability Symposium, pp. 481–487 (2008). https://doi.org/10.1109/RAMS.2008.4925843
Pecht, M., Gu, J.: Physics-of-failure-based prognostics for electronic products 31(3–4), 309–322 (2009). https://doi.org/10.1177/0142331208092031
Fan, J., Yung, K.C., Pecht, M.: Physics-of-failure-based prognostics and health management for high-power white light-emitting diode lighting. IEEE Trans. Device Mater. Reliab. 11(3), 407–416 (2011). https://doi.org/10.1109/TDMR.2011.2157695
Khan, M.A., Kerkhoff, H.G.: An indirect technique for estimating reliability of analog and mixed-signal systems during operational life. In: Proceedings of the 2013 IEEE 16th International Symposium on Design and Diagnostics of Electronic Circuits and Systems, DDECS 2013, pp. 159–164 (2013). https://doi.org/10.1109/DDECS.2013.6549809
Vazquez, J.C., et al.: Built-in aging monitoring for safety-critical applications. In: 2009 15th IEEE International On-Line Testing Symposium, IOLTS 2009, pp. 9–14 (2009). https://doi.org/10.1109/IOLTS.2009.5195976
Civilini, M.: Reliability and field aging time using temperature sensors. In: Proceedings - 4th International Conference on Sensor Technologies and Applications, SENSORCOMM 2010, pp. 210–213 (2010). https://doi.org/10.1109/SENSORCOMM.2010.39
Takeuchi, K., Shimada, M., Okagaki, T., Shibutani, K., Nii, K., Tsuchiya, F.: Wear-out stress monitor utilising temperature and voltage sensitive ring oscillators. IET Circuits Devices Syst. 12(2), 182–188 (2018). https://doi.org/10.1049/IET-CDS.2017.0153
Beckler, M., Blanton, R.D.: On-chip diagnosis for early-life and wear-out failures. In: Proceedings - International Test Conference (2012). https://doi.org/10.1109/TEST.2012.6401580
Chauhan, P., Osterman, M., Pecht, M., Yu, Q.: Use of temperature as a health monitoring tool for solder interconnect degradation in electronics. In: Proceedings of IEEE 2012 Prognostics and System Health Management Conference, PHM-2012 (2012). https://doi.org/10.1109/PHM.2012.6228874
Wandji, C., ben Rejeb, H., Zwolinski, P.: Characterization of the state of health of a complex system at the end of use. Procedia CIRP 105, 49–54 (2022). https://doi.org/10.1016/J.PROCIR.2022.02.009
Liu, J., Liu, Y., Jiang, P., Feng, F.: FMMEA automation based on function flow modeling. In: Proceedings of 2014 Prognostics and System Health Management Conference, PHM 2014, pp. 482–487 (2014). https://doi.org/10.1109/PHM.2014.6988220
Messnarz, R., Sporer, H.: Functional safety case with FTA and FMEDA consistency approach. In: Larrucea, X., Santamaria, I., O’Connor, R., Messnarz, R. (eds.) Systems, Software and Services Process Improvement, EuroSPI 2018. Communications in Computer and Information Science, vol. 896, pp. 387–397 (2018). Springer, Cham. https://doi.org/10.1007/978-3-319-97925-0_32
Pecht, M.: Prognostics and health management of electronics. Encycl. Struct. Health Monit. (2008). https://doi.org/10.1002/9780470061626.shm118
Saaty, T.L.: The Analytical Hierarchy Process, Planning, Priority. McGraw-Hill International Book Co., New York/London (1980)
Li, X.J., Bin, G.F., Dhillon, B.S.: Model to evaluate the state of mechanical equipment based on health value. Mech. Mach. Theory 46(3), 305–311 (2011). https://doi.org/10.1016/j.mechmachtheory.2010.11.008
Pries-Heje, J., Johansen, J., Messnarz, R.: SPI MANIFESTO, Software Process Improvement eurospi.net (2010). https://conference.eurospi.net/images/eurospi/spi_manifesto.pdf. Accessed 15 June 2022
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Glossary
- AHP
-
Analytic Hierarchy Process
- CBM
-
Condition-Based Maintenance
- ETA
-
Event Tree Analysis
- FIT
-
Failure-in-Time
- FMEA
-
Failure Mode and Effect Analysis
- FMMEA
-
Failure Mode, Mechanism and Effect Analysis
- FTA
-
Fault Tree Analysis
- IoT
-
Internet of Things
- PHM
-
Prognostic and Health Management
- PoF
-
Physic of Failures
- RFID
-
Radio Frequency Identification
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Wandji, C., Riel, A., Rejeb, H.B., Zwolinski, P. (2022). Characterization of the State of Health of Electronic Devices for Fostering Safety and Circular Economy. In: Yilmaz, M., Clarke, P., Messnarz, R., Wöran, B. (eds) Systems, Software and Services Process Improvement. EuroSPI 2022. Communications in Computer and Information Science, vol 1646. Springer, Cham. https://doi.org/10.1007/978-3-031-15559-8_11
Download citation
DOI: https://doi.org/10.1007/978-3-031-15559-8_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-15558-1
Online ISBN: 978-3-031-15559-8
eBook Packages: Computer ScienceComputer Science (R0)