Sequential degradation-based burn-in test with multiple periodic inspections

Abstract

Burn-in has been proven effective in identifying and removing defective products before they are delivered to customers. Most existing burn-in models adopt a one-shot scheme, which may not be sufficient enough for identification. Borrowing the idea from sequential inspections for remaining useful life prediction and accelerated lifetime test, this study proposes a sequential degradation-based burn-in model with multiple periodic inspections. At each inspection epoch, the posterior probability that a product belongs to a normal one is updated with the inspected degradation level. Based on the degradation level and the updated posterior probability, a product can be disposed, put into field use, or kept in the test till the next inspection epoch. We cast the problem into a partially observed Markov decision process to minimize the expected total burn-in cost of a product, and derive some interesting structures of the optimal policy. Then, algorithms are provided to find the joint optimal inspection period and number of inspections in steps. A numerical study is also provided to illustrate the effectiveness of our proposed model.

Graphical abstract

Keywords

burn-in / degradation / multiple inspections / Wiener process / partially observed Markov decision process

References

[1] Audet C, Dennis Jr J E (2002). Analysis of generalized pattern searches. SIAM Journal on Optimization, 13(3): 889–903

[2] Cha J H (2011). A survey of burn-in and maintenance models for repairable systems. In: Tadj L, Ouali M S, Yacout S, Ait-Kadi D, eds. Replacement Models with Minimal Repair. London: Springer, 179–203

[3] Cha J H, Finkelstein M (2010). Burn-in by environmental shocks for two ordered subpopulations. European Journal of Operational Research, 206(1): 111–117

[4] Chen Z, Pan E, Xia T, Li Y (2020). Optimal degradation-based burn-in policy using Tweedie exponential-dispersion process model with measurement errors. Reliability Engineering & System Safety, 195: 106748

[5] Grall A, Bérenguer C, Dieulle L (2002). A condition-based maintenance policy for stochastically deteriorating systems. Reliability Engineering & System Safety, 76(2): 167–180

[6] Hu J, Chen P (2020). Predictive maintenance of systems subject to hard failure based on proportional hazards model. Reliability Engineering & System Safety, 196: 106707

[7] Hu J, Shen J, Shen L (2020a). Periodic preventive maintenance planning for systems working under a Markovian operating condition. Computers & Industrial Engineering, 142: 106291

[8] Hu J, Sun Q, Ye Z S (2020b). Condition-based maintenance planning for systems subject to dependent soft and hard failures. IEEE Transactions on Reliability, in press, doi: 10.1109/TR.2020.2981136

[9] Hu J, Sun Q, Ye Z S, Zhou Q (2020c). Joint modeling of degradation and lifetime data for RUL prediction of deteriorating products. IEEE Transactions on Industrial Informatics, 17(7): 4521–4531

[10] Huynh K T, Barros A, Bérenguer C (2014). Multi-level decision-making for the predictive maintenance of k-out-of-n: F deteriorating systems. IEEE Transactions on Reliability, 64(1): 94–117

[11] Huynh K T, Barros A, Bérenguer C (2012). Maintenance decision-making for systems operating under indirect condition monitoring: Value of online information and impact of measurement uncertainty. IEEE Transactions on Reliability, 61(2): 410–425

[12] Jiang R, Jardine A K (2007). An optimal burn-in preventive-replacement model associated with a mixture distribution. Quality and Reliability Engineering International, 23(1): 83–93

[13] Kim K O (2011). Burn-in considering yield loss and reliability gain for integrated circuits. European Journal of Operational Research, 212(2): 337–344

[14] Kuo W (1984). Reliability enhancement through optimal burn-in. IEEE Transactions on Reliability, R-33(2): 145–156

[15] Lei Y, Li N, Guo L, Li N, Yan T, Lin J (2018). Machinery health prognostics: A systematic review from data acquisition to RUL prediction. Mechanical Systems and Signal Processing, 104: 799–834

[16] Li X, Rezvanizaniani M, Ge Z, Abuali M, Lee J (2015). Bayesian optimal design of step stress accelerated degradation testing. Journal of Systems Engineering and Electronics, 26(3): 502–513

[17] Liu B, Xie M, Xu Z, Kuo W (2016). An imperfect maintenance policy for mission-oriented systems subject to degradation and external shocks. Computers & Industrial Engineering, 102: 21–32

[18] Peng H, Feng Q, Coit D W (2009). Simultaneous quality and reliability optimization for microengines subject to degradation. IEEE Transactions on Reliability, 58(1): 98–105

[19] Peng W, Ye Z S, Chen N (2019). Joint online RUL prediction for multivariate deteriorating systems. IEEE Transactions on Industrial Informatics, 15(5): 2870–2878

[20] Ponchet A, Fouladirad M, Grall A (2010). Assessment of a maintenance model for a multi-deteriorating mode system. Reliability Engineering & System Safety, 95(11): 1244–1254

[21] Sun Q, Ye Z S, Zhu X (2020). Managing component degradation in series systems for balancing degradation through reallocation and maintenance. IISE transactions, 52(7): 797–810

[22] Tang L C, Yang G Y, Xie M (2004). Planning of step-stress accelerated degradation test. In: Annual Symposium Reliability and Maintainability. Los Angeles, CA: IEEE, 287–292

[23] Tseng S T, Balakrishnan N, Tsai C C (2009). Optimal step-stress accelerated degradation test plan for gamma degradation processes. IEEE Transactions on Reliability, 58(4): 611–618

[24] Tseng S T, Tang J, Ku I H (2003). Determination of burn-in parameters and residual life for highly reliable products. Naval Research Logistics, 50(1): 1–14

[25] Wang Y, Chen X, Tan Y (2017). Optimal design of step-stress accelerated degradation test with multiple stresses and multiple degradation measures. Quality and Reliability Engineering International, 33(8): 1655–1668

[26] Xiang Y, Cassady C R, Pohl E A (2012). Optimal maintenance policies for systems subject to a Markovian operating environment. Computers & Industrial Engineering, 62(1): 190–197

[27] Xiang Y, Coit D W, Feng Q (2013). n subpopulations experiencing stochastic degradation: Reliability modeling, burn-in, and preventive replacement optimization. IIE Transactions, 45(4): 391–408

[28] Ye Z S, Shen Y, Xie M (2012). Degradation-based burn-in with preventive maintenance. European Journal of Operational Research, 221(2): 360–367

[29] Ye Z S, Tang L C, Xie M (2011). A burn-in scheme based on percentiles of the residual life. Journal of Quality Technology, 43(4): 334–345

[30] Yuan T, Ji Y (2015). A hierarchical Bayesian degradation model for heterogeneous data. IEEE Transactions on Reliability, 64(1): 63–70

[31] Zhai Q, Ye Z S (2017). RUL prediction of deteriorating products using an adaptive Wiener process model. IEEE Transactions on Industrial Informatics, 13(6): 2911–2921

[32] Zhai Q, Ye Z S, Yang J, Zhao Y (2016). Measurement errors in degradation-based burn-in. Reliability Engineering & System Safety, 150: 126–135

[33] Zhang Y, Xiong R, He H, Pecht M G (2018a). Long short-term memory recurrent neural network for remaining useful life prediction of lithium-ion batteries. IEEE Transactions on Vehicular Technology, 67(7): 5695–5705

[34] Zhang Z, Si X, Hu C, Lei Y (2018b). Degradation data analysis and remaining useful life estimation: A review on Wiener-process-based methods. European Journal of Operational Research, 271(3): 775–796

RIGHTS & PERMISSIONS

2021 Higher Education Press