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.

模拟系统的混合测试优化方法

[1]Biswas DK, Panja SC, Guha S, 2014. Multi objective optimization method by PSO. Procedia Mater Sci, 6:1815-1822.

[2]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.

[3]Butzen PF, da Rosa LSJr, 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.

[4]Czaja Z, Zielonko R, 2004. On fault diagnosis of analogue electronic circuits based on transformations in multi-dimensional spaces. Measurement, 35(3):293-301.

[5]Guo Z, Savir J, 2006. Coefficient-based test of parametric faults in analog circuits. IEEE Trans Instrum Meas, 55(1):150-157.

[6]Hoffmann K, 1992. An interactive environment for the model-based design of analog circuits. Microprocess Microprogram, 35(1-5):79-85.

[7]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.

[8]Li MC, Yao B, Wang FZ, 2021. Fault diagnosis and reliable configuration of uncertain strip area based on MPSO-SVM. 40^th Chinese Control Conf, p.4636-4639.

[9]Li ZW, Ye G, Ma SL, et al., 2013. The study of spacecraft parallel testing. Telecommun Syst, 53(1):69-76.

[10]Liu G, Lü JW, Hu B, 2017. A new testability allocation method based on improved AHP. 29^th Chinese Control and Decision Conf, p.6390-6394.

[11]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.

[12]Lu B, Mei WJ, Zhou JM, et al., 2018. An novel testing sequence optimization method under dynamic environments. 10^th Int Conf on Communications, Circuits and Systems, p.479-483.

[13]Mandaogade NN, Ingole PV, 2020. Review of fault diagnosis system using soft computing approach. Proc Int Conf on Innovative Computing & Communications, p.1-7.

[14]Mei WJ, Zhen L, Li D, et al., 2015. Mixture test strategy optimization based on heuristic programming. 14^th IEEE Int Conf on Electronic Measurement & Instruments, p.1097-1104.

[15]Mei WJ, Liu Z, Tang L, et al., 2022. Test strategy optimization based on soft sensing and ensemble belief measurement. Sensors, 22(6):2138.

[16]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.

[17]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.

[18]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.

[19]Shima T, Kusaga T, 2010. Oscillation mechanism analysis of the N-stage ring oscillator ORIGAMI. IEEE Trans Electr Electron Eng, 5(6):632-638.

[20]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.

[21]Suryasarman VM, Biswas S, Sahu A, 2018. Automation of test program synthesis for processor post-silicon validation. J Electron Test, 34(1):83-103.

[22]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.

[23]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.

[24]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.

[25]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.

[26]Tu F, Pattipati KR, 2003. Rollout strategies for sequential fault diagnosis. IEEE Trans Syst Man Cybern A Syst Hum, 33(1):86-99.

[27]Vallette F, Vasilescu G, Feruglio S, et al., 2007. Tolerance analysis in MOSFET analog integrated circuits. Proc 7^th WSEAS Int Conf on Systems Theory and Scientific Computation, p.272-275.

[28]Vasan ASS, Long B, Pecht M, 2013. Diagnostics and prognostics method for analog electronic circuits. IEEE Trans Ind Electron, 60(11):5277-5291.

[29]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.

[30]Yang SM, Qiu J, Liu GJ, 2012. Sensor optimization selection model based on testability constraint. Chin J Aeronaut, 25(2):262-268.

[31]Zhang L, Wang KF, Xu LY, et al., 2022. Evolving ensembles using multi-objective genetic programming for imbalanced classification. Knowl Based Syst, 255:1109611.

[32]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.

[33]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.