Abstract
Since analog systems play an essential role in modern equipment, test strategy optimization for analog systems has attracted extensive attention in both academia and industry. Although many methods exist for the implementation of effective test strategies, diagnosis for analog systems suffers from the impacts of various stresses due to sophisticated mechanism and variable operational conditions. Consequently, the generated solutions are impractical due to the systems’ topology and influence of information redundancy. Additionally, independent tests operating sequentially on the generated strategies may increase the time consumption. To overcome the above weaknesses, we propose a novel approach called heuristic programming (HP) to generate a mixture of test strategies. The experimental results prove that HP and Rollout-HP access the strategy with fewer layers and lower cost consumption than state-of-the-art methods. Both HP and Rollout-HP provide more practical strategies than other methods. Additionally, the cost consumption of the strategy based on HP and Rollout-HP is improved compared with those of other methods because of the updating of the test cost and adaptation of mixture OR nodes. Hence, the proposed HP and Rollout-HP methods have high efficiency.
摘要
由于模拟系统在现代电子设备中起着至关重要作用, 模拟系统测试优化已引起学术界和工业界广泛关注。尽管现有方法能实现测试策略的自动生成, 但是由于复杂结构和多变的运行环境的影响, 模拟系统难以有效生成诊断策略。因此, 受到系统拓扑结构和冗余信息的影响, 生成的测试策略在实际应用中缺乏可行性。此外, 现有方法假设相互独立的测试项需要串行执行, 增加了测试时间消耗。为解决上述问题, 本文提出用于生成混合测试策略的启发式规划方法。实验证明, 相较现有方法生成的策略, 启发式规划方法和卷展启发式规划方法生成的策略具有更少层数和更低测试代价。通过对混合“或”节点的自适应优化和测试代价更新, 该方法能提供更可行的优化方案并降低测试产生的代价。因此, 本文提出的方法具有更高的优化效率。
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Data availability
The data that support the findings of this study are available from the corresponding author upon reasonable request.
References
Biswas DK, Panja SC, Guha S, 2014. Multi objective optimization method by PSO. Procedia Mater Sci, 6:1815–1822. https://doi.org/10.1016/j.mspro.2014.07.212
Boumen R, Ruan S, de Jong ISM, et al., 2009. Hierarchical test sequencing for complex systems. IEEE Trans Syst Man Cybern A Syst Hum, 39(3):640–649. https://doi.org/10.1109/TSMCA.2009.2014550
Butzen PF, da Rosa LS Jr, Chiappetta Filho EJD, et al., 2010. Standby power consumption estimation by interacting leakage current mechanisms in nanoscaled CMOS digital circuits. Microelectron J, 41(4):247–255. https://doi.org/10.1016/j.mejo.2010.03.003
Czaja Z, Zielonko R, 2004. On fault diagnosis of analogue electronic circuits based on transformations in multidimensional spaces. Measurement, 35(3):293–301. https://doi.org/10.1016/j.measurement.2003.10.004
Guo Z, Savir J, 2006. Coefficient-based test of parametric faults in analog circuits. IEEE Trans Instrum Meas, 55(1):150–157. https://doi.org/10.1109/TIM.2005.861490
Hoffmann K, 1992. An interactive environment for the model-based design of analog circuits. Microprocess Microprogram, 35(1–5):79–85. https://doi.org/10.1016/0165-6074(92)90297-K
Kundakcioglu OE, Unluyurt T, 2007. Bottom-up construction of minimum-cost and/or trees for sequential fault diagnosis. IEEE Trans Syst Man Cybern A Syst Hum, 37(5):621–629. https://doi.org/10.1109/TSMCA.2007.893459
Li MC, Yao B, Wang FZ, 2021. Fault diagnosis and reliable configuration of uncertain strip area based on MPSO-SVM. 40th Chinese Control Conf, p.4636–4639. https://doi.org/10.23919/CCC52363.2021.9549825
Li ZW, Ye G, Ma SL, et al., 2013. The study of spacecraft parallel testing. Telecommun Syst, 53(1):69–76. https://doi.org/10.1007/s11235-013-9678-1
Liu G, Lü JW, Hu B, 2017. A new testability allocation method based on improved AHP. 29th Chinese Control and Decision Conf, p.6390–6394. https://doi.org/10.1109/CCDC.2017.7978322
Liu HC, Chen XQ, Duan CY, et al., 2019. Failure mode and effect analysis using multi-criteria decision making methods: a systematic literature review. Comput Ind Eng, 135:881–897. https://doi.org/10.1016/j.cie.2019.06.055
Lu B, Mei WJ, Zhou JM, et al., 2018. An novel testing sequence optimization method under dynamic environments. 10th Int Conf on Communications, Circuits and Systems, p.479–483. https://doi.org/10.1109/ICCCAS.2018.8768976
Mandaogade NN, Ingole PV, 2020. Review of fault diagnosis system using soft computing approach. Proc Int Conf on Innovative Computing & Communications, p.1–7.
Mei WJ, Zhen L, Li D, et al., 2015. Mixture test strategy optimization based on heuristic programming. 14th IEEE Int Conf on Electronic Measurement & Instruments, p.1097–1104. https://doi.org/10.1109/ICEMI46757.2019.9101550
Mei WJ, Liu Z, Tang L, et al., 2022. Test strategy optimization based on soft sensing and ensemble belief measurement. Sensors, 22(6):2138. https://doi.org/10.3390/s22062138
Ojstersek R, Brezocnik M, Buchmeister B, 2020. Multi-objective optimization of production scheduling with evolutionary computation: a review. Int J Ind Eng Comput, 11(3):359–376. https://doi.org/10.5267/j.ijiec.2020.1.003
Pattipati KR, Alexandridis MG, 1990. Application of heuristic search and information theory to sequential fault diagnosis. IEEE Trans Syst Man Cybern, 20(4):872–887. https://doi.org/10.1109/21.105086
Roy S, Rashid AU, Abbasi A, et al., 2019. Silicon carbide bipolar analog circuits for extreme temperature signal conditioning. IEEE Trans Electron Dev, 66(9):3764–3770. https://doi.org/10.1109/TED.2019.2928484
Shima T, Kusaga T, 2010. Oscillation mechanism analysis of the N-stage ring oscillator ORIGAMI. IEEE Trans Electr Electron Eng, 5(6):632–638. https://doi.org/10.1002/tee.20585
Sun L, Zhang XY, Qian YH, et al., 2019. Feature selection using neighborhood entropy-based uncertainty measures for gene expression data classification. Inform Sci, 502:18–41. https://doi.org/10.1016/j.ins.2019.05.072
Suryasarman VM, Biswas S, Sahu A, 2018. Automation of test program synthesis for processor post-silicon validation. J Electron Test, 34(1):83–103. https://doi.org/10.1007/s10836-018-5709-x
Tang YC, Zhou DY, Chan FTS, 2018. AMWRPN: ambiguity measure weighted risk priority number model for failure mode and effects analysis. IEEE Access, 6:27103–27110. https://doi.org/10.1109/ACCESS.2018.2836139
Terry SC, Blalock BJ, Jackson JR, et al., 2004. Development of robust analog and mixed-signal electronics for extreme environment applications. IEEE Aerospace Conf, p.2569–2579. https://doi.org/10.1109/AERO.2004.1368051
Tian ZP, Wang JQ, Zhang HY, 2018. An integrated approach for failure mode and effects analysis based on fuzzy best-worst, relative entropy, and VIKOR methods. Appl Soft Comput, 72:636–646. https://doi.org/10.1016/j.asoc.2018.03.037
Tsukahara T, Ito R, Arimura K, 2015. Complex signal processing used in modern RF transceivers. IEEE Int Symp on Radio-Frequency Integration Technology, p.100–102. https://doi.org/10.1109/RFIT.2015.7377900
Tu F, Pattipati KR, 2003. Rollout strategies for sequential fault diagnosis. IEEE Trans Syst Man Cybern A Syst Hum, 33(1):86–99. https://doi.org/10.1109/TSMCA.2003.809206
Vallette F, Vasilescu G, Feruglio S, et al., 2007. Tolerance analysis in MOSFET analog integrated circuits. Proc 7th WSEAS Int Conf on Systems Theory and Scientific Computation, p.272–275.
Vasan ASS, Long B, Pecht M, 2013. Diagnostics and prognostics method for analog electronic circuits. IEEE Trans Ind Electron, 60(11):5277–5291. https://doi.org/10.1109/TIE.2012.2224074
Wang SP, Zhao DM, Yuan JZ, et al., 2019. Application of NSGA-II algorithm for fault diagnosis in power system. Electr Power Syst Res, 175:105893. https://doi.org/10.1016/j.epsr.2019.105893
Yang SM, Qiu J, Liu GJ, 2012. Sensor optimization selection model based on testability constraint. Chin J Aeronaut, 25(2):262–268. https://doi.org/10.1016/S1000-9361(11)60386-5
Zhang L, Wang KF, Xu LY, et al., 2022. Evolving ensembles using multi-objective genetic programming for imbalanced classification. Knowl Based Syst, 255:1109611. https://doi.org/10.1016/J.KNOSYS.2022.109611
Zhang SG, Hu Z, Wen XS, 2013. Test sequencing problem arising at the design stage for reducing life cycle cost. Chin J Aeronaut, 26(4):1000–1007. https://doi.org/10.1016/j.cja.2013.04.054
Zhang SG, Song LJ, Zhang W, et al., 2015. Optimal sequential diagnostic strategy generation considering test placement cost for multimode systems. Sensors, 15(10):25592–25606. https://doi.org/10.3390/s151025592
Acknowledgements
The authors would like to thank Zhigang WANG for his help with the analog system and Yanfei JING for his help with the mathematic work.
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Wenjuan MEI and Zhen LIU designed the research. Ouhang LI, Yusong MEI, and Yongji LONG processed the data. Wenjuan MEI drafted the paper. Zhen LIU and Yuanzhang SU helped organize the paper. Wenjuan MEI, Zhen LIU, and Ouhang LI revised and finalized the paper.
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Wenjuan MEI, Zhen LIU, Ouhang LI, Yuanzhang SU, Yusong MEI, and Yongji LONG declare that they have no conflict of interest.
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Project supported by the Youth and Middle-Aged Scientific and Technological Innovation Leading Talents Program of the Corps, China (No. 2020 JDT0008)
List of supplementary materials
1 Proof of Property 1
2 Proof of Property 2
3 Proof of Property 3
4 Proof of Property 4
5 Proof of Property 5
6 Proof of Property 6
7 Proof of Property 7
8 Proof of Property 8
9 Proof of Property 9
10 Proof of Property 10
11 Proof of Property 11
12 Proof of Property 12
13 Details of the real-world applications
Fig. S1 System board of the real-world applications
Fig. S2 System structure of the real-world applications
Fig. S3 Signal topology of the real-world applications
Table S1 Dependence matrix of the real-world cases
Supplementary materials
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Mei, W., Liu, Z., Li, O. et al. Mixture test strategy optimization for analog systems. Front Inform Technol Electron Eng 24, 1302–1315 (2023). https://doi.org/10.1631/FITEE.2200512
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DOI: https://doi.org/10.1631/FITEE.2200512