Skip to main content
SpringerLink
Log in

Study on the construction and application of Cloudization Space Fault Tree

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

The Space Fault Tree (SFT) is a theoretical and technical framework proposed by the author in 2012. The SFT measures the reliability with fault probability, and analyzes the relationship between system reliability and influencing factors. The characteristic function (CF) of SFT is constructed by the relationship between the component fault probability and the influencing factors, and is the basis of the SFT. The system fault data is different from the general monitoring data, which has large discreteness, randomness and fuzziness, that is, uncertainty. The existing characteristic function is a continuous function with finite discontinuities. The function can be considered as a kernel function of the fault data, but it is difficult to express the uncertainty of fault data. To this end, using cloud model (CM) to transform the characteristic function, so that it has the ability to express the uncertainty of data, called the cloudization characteristic function (CCF). The cloudization SFT (CLSFT) is constructed by using the CCF, and enables the relevant theory and methods of SFT to express the data uncertainty, thus the expression of the fault data characteristics and rules are more accurate. Firstly, the construction process and rationality analysis of CLSFT are given. Secondly, the concepts of SFT are reconstructed by cloud model. Use these definitions and methods to analyze the fault data of a simple electrical component. The relationship is studied between the component fault probability and the using time and using temperature. The results reflect the discreteness, randomness and fuzziness of the fault data to a large extent. In summary, the paper provides the reference for analyzing and controlling the uncertainty of the fault data and reliability in practical application.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Abbreviations

Ex :

Digital features of cloud, expectation

En :

Digital features of cloud, entropy

He :

Digital features of cloud, hyper entropy

C(ex,en,he):

Cloud model

\(\mu _\mathrm{q}\) :

Certainty degree of concept

\(P_i^d (x)\) :

Characteristic function of basic event occurrence probability

i :

The ith component

\(d_{k}\) :

Influencing factor, \(d_k \in \{x_1 ,x_2 ,\ldots x_n \}\)

n :

Number of influencing factors

\(P_i (x_1 ,x_2 ,\ldots x_n )\) :

Basic event occurrence probability distribution

\(x_k \) :

Specific values of factor \(d_k \)

\(P_T (x_1 ,x_2 ,\ldots x_n )\) :

Top event(system) occurrence probability distribution

\(P_i^{d_k } \backslash P_T^{d_k } \) :

Basic event \(\backslash \) top event(system) occurrence probability distribution trend

\(I_g (i)\) :

Probability importance distribution

\(I_g^c (i)\) :

Criticality importance distribution

\(FI_i (x_1 ,x_2 ,\ldots x_n )\backslash \) :

Component \(\backslash \)system factor

   \(FI_T (x_1 ,x_2 ,\ldots x_n )\) :

distribution \(\backslash \) factor joint importance distribution

\(ZI_g (i)\) :

Component domain probability importance

\(ZI_g^c (i)\) :

Component domain criticality importance

\(P_{b}\) :

Domain boundary of component \(\backslash \) system

\(K_{j}(j\) = 1,2,..., r):

The jth set in r minimal cut set of fault tree

\(E_{i}/ E_{ii}\) :

ith/iith basic event in \(K_{j}\)\(Ex_k , He_k \) and \(En_k \): characteristic parameters of the cloud model obtained from reverse cloud model generator considering the influence of kth influencing factor on reliability data

subscript k :

Corresponding parameters of the kth factors \(P_{T\rightarrow d_k }^{Ex} (x_1 ,x_2 ,\ldots x_n ), P_{T\rightarrow d_k }^{En} (x_1 ,x_2 ,\ldots x_n ), P_{T\rightarrow d_k }^{He} (x_1 ,x_2 ,\ldots x_n )\) Three parameters for the uncertainty of system reliability data considering \(d_k \)

M and c :

The total number of cloud droplets

Q :

Threshold

\(\lambda _{\max } \) and \(\lambda _{\min } \) :

The maximum and minimum values in the differential fault probability distribution, respectively

\(\lambda \) :

Absolute value of deviation from 0 \(m_{En} , \quad m_{Ex} , \quad m_{He} \) Number of cloud droplets not in the \([-\lambda ,\lambda ]\). \(\delta _f , \quad \delta _d \) and \(\delta _r \) Fuzziness, discreteness and randomness of reliability data respectively

H:

Using humidity

C :

Using temperature

T :

Instance system

\(X_{1\sim 5}\) :

The five components in the instance system

\(C_{1}^{c}\) and \(C_{1}^{h}\) :

The cloud model under the influence of the using temperature c and using humidity h on the reliability of \(X_{1}\)

References

  1. Isoda, N., Kadohira, M., Sekiguchi, S., Schuppers, M., Stärk, K.D.C.: Review: evaluation of foot-and-mouth disease control using fault tree analysis. Transbound. Emerg. Dis. 62, 233–244 (2015)

    Article  Google Scholar 

  2. Zytoon, M.A., El-Shazly, A.H., Noweir, M.H., Al-Zahrani, A.A.: Quantitative safety analysis of a laboratory-scale bioreactor for hydrogen sulfide biotreatment using fault tree analysis. Process Saf. Prog. 32, 376–386 (2013)

    Article  Google Scholar 

  3. Flage, R., Baraldi, P., Zio, E., Aven, T.: Probability and possibility-based representations of uncertainty in fault tree analysis. Risk Anal. 33, 121–133 (2013)

    Article  Google Scholar 

  4. Pedroni, N., Zio, E.: Uncertainty analysis in fault tree models with dependent basic events. Risk Anal. 33, 1146–1173 (2013)

    Article  Google Scholar 

  5. Wang, D., Zhang, Y., Jia, X., Jiang, P., Guo, B.: Handling uncertainties in fault tree analysis by a hybrid probabilistic-possibilistic framework. Qual. Reliab. Eng. Int. 32, 1137–1148 (2016)

    Article  Google Scholar 

  6. Ge, D., Li, D., Chou, Q., Zhang, R., Yang, Y.: Quantification of highly coupled dynamic faulttree using IRVPM and SBDD. Qual. Reliab. Eng. Int. 32, 139–151 (2016)

    Article  Google Scholar 

  7. Cui, Y., Shi, J., Wang, Z.: Analog circuits fault diagnosis using multi-valued Fisher’s fuzzy decision tree (MFFDT). Int. J. Circuit Theory Appl. 44, 240–260 (2016)

    Article  Google Scholar 

  8. Ekaette, E., Lee, R.C., Cooke, D.L., Iftody, S., Craighead, P.: Probabilistic fault tree analysis of a radiation treatment system. Risk Anal. 27, 1395–1410 (2007)

    Article  Google Scholar 

  9. Yousfi Steiner, N., Hissel, D., Moçotéguy, P., Candusso, D., Marra, D., Pianese, C., Sorrentino, M.: Application of fault tree analysis to fuel cell diagnosis. Fuel Cells 12, 302–309 (2012)

    Article  Google Scholar 

  10. Ge, D., Yang, Y.: Reliability analysis of non-repairable systems modeled by dynamic fault trees with priority AND gates. Appl. Stoch. Models Bus. Ind. 31, 809–822 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  11. Merle, G., Roussel, J.-M., Lesage, J.-J., Perchet, V., Vayatis, N.: Quantitative analysis of dynamic fault trees based on the coupling of structure functions and Monte Carlo simulation. Qual. Reliab. Eng. Int. 32, 7–18 (2016)

    Article  Google Scholar 

  12. Yevkin, O.: An efficient approximate markov chain method in dynamic fault tree analysis. Qual. Reliab. Eng. Int. 32, 1509–1520 (2016)

    Article  Google Scholar 

  13. Merle, G., Roussel, J.-M., Lesage, J.-J.: Quantitative analysis of dynamic fault trees based on the structure function. Qual. Reliab. Eng. Int. 30, 143–156 (2014)

    Article  Google Scholar 

  14. Zhang, X.-P., Wang, J., Ming-liang, H.U.: Application of FTA in safety assessment of row piles of excavation engineering. Chin. J. Geotech. Eng. 33, 960–965 (2011)

    Google Scholar 

  15. Liu, X., Qi, H., Wang, X., et al.: Reliability analysis and evaluation of differential system based on low load strengthening model. Qual. Reliab. Eng. Int. 32(2), 647–662 (2016)

    Article  Google Scholar 

  16. Zhen, H., Mahadevan, S.: Accelerated life testing (ALT) design based on computational reliability analysis. Qual. Reliab. Eng. Int. 32(7), 2217–2232 (2016)

    Article  Google Scholar 

  17. Xiaojian Yi, R., Dhillon, B.S., Shi, J., et al.: eliability analysis method on repairable system with standby structure based on goal oriented methodology. Qual. Reliab. Eng. Int. 32(7), 2505–2517 (2016)

    Article  Google Scholar 

  18. You, D., Pham, H.: Reliability analysis of the CNC system based on field failure data in operating environments. Qual. Reliab. Eng. Int. 32(5), 1955–1963 (2016)

    Article  Google Scholar 

  19. Chen, T., Zheng, S., Luo, H., et al.: Reliability analysis of multiple causes of failure in presence of independent competing risks. Qual. Reliab. Eng. Int. 32(2), 363–372 (2016)

    Article  Google Scholar 

  20. Liu, Y., Naiwei, L., Yin, X.: A hybrid method for structural system reliability-based design optimization and its application to trusses. Qual. Reliab. Eng. Int. 32(2), 595–608 (2016)

    Article  Google Scholar 

  21. Garmabaki, A.H.S., Ahmadi, A., Mahmood, Y.A., et al.: Reliability modelling of multiple repairable units. Qual. Reliab. Eng. Int. 32(7), 2329–2343 (2016)

    Article  Google Scholar 

  22. Mendes, A.A., Ribeiro, J.L.D.: An accessible way to establish reliability and expected time-to-failure for cold standby redundant systems subject to periodic inspections. Qual. Reliab. Eng. Int. 32(5), 1663–1675 (2016)

    Article  Google Scholar 

  23. Peng, K., Huang, C.: Stochastic modelling and simulation approaches to analysing enhanced fault tolerance on service-based software systems. Softw. Test. Verif. Reliab. 26(4), 276–293 (2016)

    Article  Google Scholar 

  24. Jin, X., Gan, Y., Wenbin, J., et al.: Research on wind turbine safety analysis: failure analysis, reliability analysis, and risk assessment. Environ. Prog. Sustain. Energy 35(6), 1848–1861 (2016)

    Article  Google Scholar 

  25. Zarei, E., Mohammadfam, I., Aliabadi, M.M., et al.: Efficiency of control room operators based on human reliability analysis and dynamic decision-making style in the process industry. Process Saf. Prog. 35(2), 192–199 (2016)

    Article  Google Scholar 

  26. Zhang, E., Chen, Q.: Multi-objective particle swarm optimization for uncertain reliability optimization problems. Control and Decision (2013). http://www.cnki.net/kcms/detail/21.1124.TP.20150611.0806.001.html

  27. Gao, Y., Wang, Y., Deyi, M.: Reliability analysis of pilot emergency operations based on uncertainty theory and CREAM. China Saf. Sci. J. 23(10), 56–62 (2013)

    Google Scholar 

  28. Mao, S., Shi, Y., Sun, T.: Determination of minimum sample size for pareto product in reliability tests. J. Mech. Strength 35(3), 274–277 (2013)

    Google Scholar 

  29. Deyi, L., Yi, D.: The Uncertainty of Artificial Intelligence. National Defense Industry Press, Beijing (2005)

    MATH  Google Scholar 

  30. Li, D., Liu, C., Gan, W.: A new cognitive model: cloud model. Int. J. Intell. Syst. 24, 357–375 (2009)

    Article  MATH  Google Scholar 

  31. Hwang, K., Li, D.: Trusted cloud computing with secure resources and data coloring. IEEE Internet Comput. 14–22 (2010)

  32. Meng, X., Zhang, G., Kang, J., et al: A new subjective trust model based on cloud model. In: 2008 IEEE International Conference on Networking, Sensing and Control, pp. 1125–1130 (2008)

  33. Jian, L., Mingwu, W., Peng, X., et al.: Classification of stability of surrounding rock using cloud model. Chin. J. Geotech. Eng. 36(1), 83–87 (2014)

    Google Scholar 

  34. Liao, R., Bian, J., Yang, L., et al.: Cloud model-based failure mode and effects analysis for prioritization of failures of power transformer in risk assessment. Int. Trans. Electr. Energy Syst. 23, 1172–1190 (2012)

    Article  Google Scholar 

  35. Zhang, X., Zhao, L., Zang, J., et al.: Hybrid MATLAB and LabVIEW with T-S cloud inference neural network to realize a flatness intelligent control system. Steel Res. Int. 85, 1639–1652 (2014)

    Article  Google Scholar 

  36. Chenxi, W., Lou, Y., Lou, P., et al.: DG location and capacity optimization considering several objectives with cloud theory adapted GA. Int. Trans. Electr. Energy Syst. 24, 1076–1088 (2013)

    Google Scholar 

  37. Li, D., Wang, S., Yuan, H., et al.: Software and applications of spatial data mining. Wiley Interdiscip. Rev. 6, 84–114 (2016)

    Google Scholar 

  38. Li, D., Liu, Y., Zhang, H., et al.: Cloud computing beyond turing machines. In: 2011 IEEE International Conference on Cloud Computing and Intelligence Systems, pp. 373–381 (2011)

  39. Huang, L., Zhang, H., Chen, G., et al.: From turing machine intelligence to collective intelligence. In: 2012 IEEE 2nd International Conference on Cloud Computing and Intelligence Systems, vol. 3, pp. 1171–1177 (2012)

  40. Gao, H., Jiang, J., Zhang, L., et al.: Cloud model: detect unsupervised communities in social tagging networks. In: International Conference on Information Science and Cloud Computing Companion, pp. 317–323 (2013)

  41. Li, H., Zhang, G., Li, D., et al.: Computation on attribute importance of classification based on cloud model. In: 2008 International Conference on Computational Intelligence for Modelling Control & Automation, pp. 879–883

  42. Tao, Z., Jun, H., He, W., et al.: Modeling user’s preference in folksonomy for personalized search. In: 2011 International Conference on Cloud and Service Computing, pp. 55–59 (2011)

  43. He, R., Zhang, G., Niu, J., et al: Clouding algorithm: a novel multi-population evolution model and its applying to global numerical optimization. In: Third International Conference on Natural Computation (ICNC 2007), pp. 123–128 (2007)

  44. Qin, K., Wu, F., Xu, K., et al: Image segmentation based on cloud concept analysis. In: 2010 6th International Conference on Wireless Communications Networking and Mobile Computing (WiCOM), pp. 1–4 (2010)

  45. Hesong, L, Guangwei, Z., Deyi, L., et al.: Knowledge discovery of classification is given based on cloud modeland genetic algorithm. In: 2008 International Conference on Computer Science and Software Engineering, pp. 358–363

  46. Qin, K., Xu, K., Du, Y., et al.: An image segmentation approach based on histogram analysis utilizing cloud model. In: 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery, vol. 2, pp. 524–528 (2010)

  47. Cui, T.: The Construction of Space Fault Tree Theory and Research, p. 6. Liaoning Technical University, Fuxin (2015)

    Google Scholar 

  48. Cui, T., Ma, Y.: Research on multi-dimensional space fault tree construction and application. China Saf. Sci. J. 23(4), 32–37 (2013)

    Google Scholar 

  49. Cui, T., Ma, Y.: Definition and understand on size set domain and cut set domain based on multi-dimensional space fault tree. China Saf. Sci. J. 24(4), 27–32 (2014)

    Google Scholar 

  50. Cui, T., Ma, Y.: The definition and cognition of the factor importance distribution in Continuous Space Fault Tree. China Saf. Sci. J. 25(3), 24–28 (2015)

    Google Scholar 

  51. Cui, T., Ma, Y.: Research on the maintenance method of system reliability based on multi-dimensional space fault tree. J. Syst. Sci. Math. Sci. 34(6), 682–692 (2014)

    MATH  Google Scholar 

  52. Cui, T., Ma, Y.: The method research on decision criterion discovery of system reliability. Syst. Eng. 35(12), 3210–3216 (2015)

    Google Scholar 

  53. Li, S., Cui, T., Ma, Y.: System reliability assessment method based on space fault tree. J. Saf. Sci. Technol. 11(6), 86–92 (2015)

    Google Scholar 

  54. Cui, T., Ma, Y.: Reliability assessment method based on space fault tree. Fuzzy Syst. Math. 29(5), 173–182 (2015)

    MATH  Google Scholar 

  55. Cui, T., Ma, Y.: Discrete space fault tree construction and application research. J. Syst. Sci. Math. Sci. 36(10), 1753–1761 (2016)

    MATH  Google Scholar 

  56. Cui, T., Yun-dong, M.A.: Discrete space fault tree construction and failure probability space distribution determine. Syst. Eng. 36(4), 1081–1088 (2016)

    Google Scholar 

  57. Cui, T., Li, S., Ma, Y., et al.: Research on trend of failure probability in DSFT based on ANN derivation. Appl. Res. Comput. (2017)

  58. Cui, T.J., Li, S.S.: Study on the relationship between system reliability and influencing factors under big data and multi-factors. Cluster Comput. (2017). https://doi.org/10.1007/s10586-017-1278-5

  59. Cui, T.J., Wang, P.Z., Li, S.S.: The function structure analysis theory based on the factor space and space fault tree. Cluster Comput. 20(2), 1387–1398 (2017)

    Article  Google Scholar 

Download references

Acknowledgements

The author wishes to thank all his friends for their valuable critics, comments and assistances on this paper. This study was partially supported by the grants (Grant Nos. 51704141, 51474121, 51674127) from the Natural Science Foundation of China.

Author information

Authors and Affiliations

  1. College of Safety Science and Engineering, Liaoning Technical University, Fuxin, 123000, China

    Sha-Sha Li, Tie-Jun Cui & Jian Liu

  2. Key Laboratory of Mine Thermodynamic Disasters and Control of Ministry of Education, Fuxin, 123000, China

    Sha-Sha Li, Tie-Jun Cui & Jian Liu

  3. Tunnel & Underground Structure Engineering Center of Liaoning, Dalian Jiaotong University, Dalian, 116028, China

    Tie-Jun Cui

Authors
  1. Sha-Sha Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tie-Jun Cui
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jian Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tie-Jun Cui.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, SS., Cui, TJ. & Liu, J. Study on the construction and application of Cloudization Space Fault Tree. Cluster Comput 22 (Suppl 3), 5613–5633 (2019). https://doi.org/10.1007/s10586-017-1398-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-017-1398-y

Keywords

Search

Navigation