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An intermittent fault diagnosis method of analog circuits based on variational modal decomposition and adaptive dynamic density peak clustering

  • Data analytics and machine learning
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Abstract

Analog circuits are widely used in industrial systems and avionics. Intermittent faults (IFs) as a special type of fault in circuits are difficult to diagnose. Due to the short duration of IFs, it is first necessary to obtain IF samples from the original signal. Therefore, variational modal decomposition (VMD) and autoencoder are proposed to capture the appearing and disappearing moments of IFs. Then, IF samples can be extracted from the original signal by the detected moments of appearance and disappearance. Finally, the adaptive dynamic density peak clustering (ADDPC) method is proposed for automatically identifying IF categories. The dynamic nature of ADDPC is reflected in the fact that the density kernel is not a fixed scanning radius, but a dynamic radius density kernel based on the k nearest neighbor. The adaptability is reflected in the fact that the parameters of ADDPC can be selected automatically by particle swarm optimization according to the change of data distribution. The intelligent diagnosis method is subsequently applied to a typical analog filter circuit under three noise levels. The results show that the proposed framework has a better diagnostic performance in the presence of noise.

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References

  • Abualigah L (2018) Feature selection and enhanced Krill Herd algorithm for text document clustering

  • Abualigah L, Diabat A, Mirjalili S, Abd Elaziz M, Gandomi AH (2021a) The arithmetic optimization algorithm. Comput Methods Appl Mech Eng 376:113609

  • Abualigah L, Yousri D, Abd Elaziz M, Ewees AA, Al-Qaness MA, Gandomi AH (2021b) Aquila optimizer: a novel meta-heuristic optimization algorithm. Comput Ind Eng 157:107250

  • Azar HN, Malazi HT (2018) Decentralized detection of hybrid faults in mobile sensor nodes. Simul Model Pract Theory 87:210–225

    Article  Google Scholar 

  • Breitfelder K, Messina D (2000) The authoritative dictionary of IEEE standards terms

  • Cai B, Liu Y, Xie M (2016) A dynamic-Bayesian-network-based fault diagnosis methodology considering transient and intermittent faults. IEEE Trans Autom Sci Eng 14(1):276–285

    Article  Google Scholar 

  • Chen Z (2022) Research on internet security situation awareness prediction technology based on improved RBF neural network algorithm. J Comput Cogn Eng

  • Cheng X, Lv K, Liu G, Qiu J (2020) Characteristics of intermittent fault in electrical connectors under vibration environment. IEEE Trans Compon Packag Manuf Technol 10(9):1575–1578

    Article  Google Scholar 

  • Correcher A, García E, Morant F, Quiles E, Rodríguez L (2012) Intermittent failure dynamics characterization. IEEE Trans Reliab 61(3):649–658

    Article  Google Scholar 

  • DoD (2015) Electronic test equipment intermittent fault detection isolation: Mil-prf-32516s

  • Dragomiretskiy K, Zosso D (2014) Variational mode decomposition. IEEE Trans Signal Process 62(3):531–544

    Article  MathSciNet  Google Scholar 

  • Ebrahimi H, Kerkhoff HG (2016a) Testing for intermittent resistive faults in CMOS integrated systems. In: 2016 Euromicro Conference on digital system design (DSD). IEEE, pp 703–707

  • Ebrahimi H, Kerkhoff HG (2016b) Testing for intermittent resistive faults in CMOS integrated systems. In: 2016 Euromicro conference on digital system design (DSD). IEEE, pp 703–707

  • Feng L, Chai Y, Xu S, Yang Z (2017) Observer-based fault estimators using iterative learning scheme for nonlinear time-delay systems with intermittent faults. Int J Robust Nonlinear Control 27(17):3412–3432

    MathSciNet  MATH  Google Scholar 

  • Gao T, Yang J, Jiang S (2020) A novel incipient fault diagnosis method for analog circuits based on GMKL-SVM and wavelet fusion features. IEEE Trans Instrum Meas 70:1–15

    Article  Google Scholar 

  • Guo MW, Ni SH, Zhu JH (2012) Intermittent fault diagnosis for built-in test system based on EMD and HMM. J Vib Meas Diagn 32(3):467–470

    Google Scholar 

  • Han C, Park S, Lee H (2019) Intermittent failure in electrical interconnection of avionics system. Reliab Eng Syst Saf 185:61–71

    Article  Google Scholar 

  • Huakang L, Kehong L, Jing Q, Guanjun L, Bailiang C (2018) Selection of test paths for solder joint intermittent connection faults under dc stimulus. Int J Electron 105(6):1011–1024

    Google Scholar 

  • Huakang L, Kehong L, Yong Z, Qiu J, Liu G (2019) Study of solder joint intermittent fault diagnosis based on dynamic analysis. IEEE Trans Compon Packag Manuf Technol 9(9):1748–1758

    Article  Google Scholar 

  • Hubner-Obenland F, Minuth J (1999) A new test equipment for high dynamic real-time measuring of contact resistances. In: Electrical contacts-1999. Proceedings of the forty-fifth IEEE Holm conference on electrical contacts (Cat. No. 99CB36343). IEEE, pp 193–202

  • Li F, Woo PY (2002) Fault detection for linear analog IC-the method of short-circuit admittance parameters. IEEE Trans Circuits Syst I Fundam Theory Appl 49(1):105–108

    Article  Google Scholar 

  • Liu Z, Jia Z, Vong CM, Bu S, Han J, Tang X (2017) Capturing high-discriminative fault features for electronics-rich analog system via deep learning. IEEE Trans Ind Inf 13(3):1213–1226

    Article  Google Scholar 

  • Maul C, McBride JW, Swingler J (2001) Intermittency phenomena in electrical connectors. IEEE Trans Compon Packag Technol 24(3):370–377

    Article  Google Scholar 

  • Obeid NH, Boileau T, Nahid-Mobarakeh B (2016) Modeling and diagnostic of incipient interturn faults for a three-phase permanent magnet synchronous motor. IEEE Trans Ind Appl 52(5):4426–4434

    Article  Google Scholar 

  • Obeid NH, Battiston A, Boileau T, Nahid-Mobarakeh B (2017) Early intermittent interturn fault detection and localization for a permanent magnet synchronous motor of electrical vehicles using wavelet transform. IEEE Trans Transp Electrif 3(3):694–702

    Article  Google Scholar 

  • Principi E, Rossetti D, Squartini S, Piazza F (2019) Unsupervised electric motor fault detection by using deep autoencoders. IEEE/CAA J Automatica Sinica 6(2):441–451

    Article  Google Scholar 

  • Richman JS, Moorman JR (2000) Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol

  • Rodriguez A, Laio A (2014) Clustering by fast search and find of density peaks. Science 344(6191):1492–1496

    Article  Google Scholar 

  • Rousseeuw PJ (1987) Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math 20:53–65

    Article  Google Scholar 

  • Savir J (1978) Detection of intermittent faults in sequential circuits. Stanford University

  • Sorensen BA, Kelly G, Sajecki A, Sorensen P (1994) An analyzer for detecting intermittent faults in electronic devices. In: Proceedings of AUTOTESTCON’94. IEEE, pp 417–421

  • Steadman B, Berghout F, Olsen N, Sorensen B (2008) Intermittent fault detection and isolation system. In: 2008 IEEE AUTOTESTCON. IEEE, pp 37–40

  • Strangas EG, Aviyente S, Zaidi SSH (2008) Time-frequency analysis for efficient fault diagnosis and failure prognosis for interior permanent-magnet AC motors. IEEE Trans Ind Electron 55(12):4191–4199

    Article  Google Scholar 

  • Syed WA, Perinpanayagam S, Samie M, Jennions I (2016) A novel intermittent fault detection algorithm and health monitoring for electronic interconnections. IEEE Trans Compon Packag Manuf Technol 6(3):400–406

    Article  Google Scholar 

  • Vasan ASS, Long B, Pecht M (2012) Diagnostics and prognostics method for analog electronic circuits. IEEE Trans Ind Electron 60(11):5277–5291

    Article  Google Scholar 

  • Wang Y, Wei Z, Yang J (2018) Feature trend extraction and adaptive density peaks search for intelligent fault diagnosis of machines. IEEE Trans Ind Inf 15(1):105–115

    Article  Google Scholar 

  • Xiao C, Yu M, Zhang B, Wang H, Jiang C (2020) Discrete component prognosis for hybrid systems under intermittent faults. IEEE Trans Autom Sci Eng

  • Yang M (2022) Research on vehicle automatic driving target perception technology based on improved MSRPN algorithm. J Comput Cogn Eng

  • Yang J, Xie G, Yang Y, Zhang Y, Liu W (2019a) Deep model integrated with data correlation analysis for multiple intermittent faults diagnosis. ISA Trans 95:306–319

  • Yang K, Wang G, Dong Y, Zhang Q, Sang L (2019b) Early chatter identification based on an optimized variational mode decomposition. Mech Syst Signal Process 115:238–254

  • Yang Y, Song X (2022) Research on face intelligent perception technology integrating deep learning under different illumination intensities. J Comput Cogn Eng 32–36

  • Zanardelli WG, Strangas EG, Aviyente S (2007) Identification of intermittent electrical and mechanical faults in permanent-magnet ac drives based on time-frequency analysis. IEEE Trans Ind Appl 43(4):971–980

    Article  Google Scholar 

  • Zhang J, Christofides PD, He X, Wu Z, Zhao Y, Zhou D (2020) Robust detection of intermittent sensor faults in stochastic LTV systems. Neurocomputing 388:181–187

    Article  Google Scholar 

  • Zhou D, Shi J, He X et al (2014a) Review of intermittent fault diagnosis techniques for dynamic systems. Acta Automatica Sinica 40(2):161–171

  • Zhou H, Liu GJ, Qiu J, Lv KH (2014b) Intermittent fault diagnosis under extreme vibration environment based on EMD and neural network. In: Key engineering materials, vol 584. Trans Tech Publ, pp 97–101

  • Zhou D, Zhao Y, Wang Z, He X, Gao M (2020) Review on diagnosis techniques for intermittent faults in dynamic systems. IEEE Trans Ind Electron 67(3):2337–2347

    Article  Google Scholar 

Download references

Acknowledgements

We thank the reviewers for their helpful suggestions.

Funding

This work was supported by the National Natural Science Foundation of China (Grant No. U2034209 and Grant No. 61633005) and the Graduate Scientific Research and Innovation Foundation of Chongqing, China (Grant No. CYS20070).

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Authors and Affiliations

  1. State Key Laboratory of Power Transmission Equipment and System Security and New Technology, Chongqing University, Chongqing, 400044, China

    Jianfeng Qu, Xiaoyu Fang, Yi Chai & Jinzhuo Liu

  2. College of Automation, Chongqing University, Chongqing, 400044, China

    Jianfeng Qu, Xiaoyu Fang, Yi Chai & Jinzhuo Liu

  3. College of Control Science and Engineering, Shandong University, Shandong, 250061, China

    Qiu Tang

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  1. Jianfeng Qu
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  2. Xiaoyu Fang
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  3. Yi Chai
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  4. Qiu Tang
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  5. Jinzhuo Liu
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Correspondence to Jianfeng Qu.

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Qu, J., Fang, X., Chai, Y. et al. An intermittent fault diagnosis method of analog circuits based on variational modal decomposition and adaptive dynamic density peak clustering. Soft Comput 26, 8603–8615 (2022). https://doi.org/10.1007/s00500-022-07226-1

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