Full Text:   <468>

Summary:  <120>

Suppl. Mater.: 

CLC number: TP206

On-line Access: 2023-06-21

Received: 2022-10-25

Revision Accepted: 2023-09-21

Crosschecked: 2023-02-08

Cited: 0

Clicked: 734

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Wenjuan MEI

https://orcid.org/0000-0002-9376-9358

Zhen LIU

https://orcid.org/0000-0003-3406-0039

-   Go to

Article info.
Open peer comments

Frontiers of Information Technology & Electronic Engineering  2023 Vol.24 No.9 P.1302-1315

http://doi.org/10.1631/FITEE.2200512


Mixture test strategy optimization for analog systems


Author(s):  Wenjuan MEI, Zhen LIU, Ouhang LI, Yuanzhang SU, Yusong MEI, Yongji LONG

Affiliation(s):  School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China; more

Corresponding email(s):   meiwenjuan@std.uestc.edu.cn, scdliu@uestc.edu.cn

Key Words:  Fault diagnosis, Heuristic searching, Dynamic programming, Test optimization


Wenjuan MEI, Zhen LIU, Ouhang LI, Yuanzhang SU, Yusong MEI, Yongji LONG. Mixture test strategy optimization for analog systems[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(9): 1302-1315.

@article{title="Mixture test strategy optimization for analog systems",
author="Wenjuan MEI, Zhen LIU, Ouhang LI, Yuanzhang SU, Yusong MEI, Yongji LONG",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="24",
number="9",
pages="1302-1315",
year="2023",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2200512"
}

%0 Journal Article
%T Mixture test strategy optimization for analog systems
%A Wenjuan MEI
%A Zhen LIU
%A Ouhang LI
%A Yuanzhang SU
%A Yusong MEI
%A Yongji LONG
%J Frontiers of Information Technology & Electronic Engineering
%V 24
%N 9
%P 1302-1315
%@ 2095-9184
%D 2023
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2200512

TY - JOUR
T1 - Mixture test strategy optimization for analog systems
A1 - Wenjuan MEI
A1 - Zhen LIU
A1 - Ouhang LI
A1 - Yuanzhang SU
A1 - Yusong MEI
A1 - Yongji LONG
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 24
IS - 9
SP - 1302
EP - 1315
%@ 2095-9184
Y1 - 2023
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2200512


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

梅文娟1,刘震1,李欧行3,苏元章1,2,梅渝松1,龙泳吉4
1电子科技大学自动化学院,中国成都市,611731
2电子科技大学外国语学院,中国成都市,611731
3电子科技大学格拉斯哥学院,中国成都市,611731
4电子科技大学集成电路科学与工程学院(示范性微电子学院),中国成都市,611731
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。

关键词:故障诊断;启发式搜索;动态规划;测试优化

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[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. 40th 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. 29th 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. 10th 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. 14th 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 7th 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.

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Please provide your name, email address and a comment





Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE