Algorithms for Determining the Probability of Risks of Accidents in Tunnels Based on the Characteristics of the Noise of Noisy Signals

The paper covers creating the algorithms for calculating the probability of various types of defects in tunnels, the development of which can lead to accidents. Tunnels are an important and complex part of the transport and communication system, through which heavy traffic is carried out. Determining the probability of defects in the latent period of their initiation in individual sections of tunnels is an important problem. The formation of defects is accompanied by the appearance of noise that distorts the useful signals coming from sensors and measuring instruments installed to control the stability of the tunnel and the reliability of its structures. Traditionally measuring instruments register noisy signals, and the technical condition of the tunnels is assessed on the basis of the values of their characteristics. It is shown in the paper that the more reliable indicators of fixing the onset of dangerous changes in the latent period of initiation are the characteristics of the noise, which cannot be extracted from the noisy signal. It is noted that the probability with which the noise takes on admissible and critical values is an indicator of changes in the technical condition of tunnels. Algorithms have been developed for calculating the probabilities of the noise values getting in the given intervals. These probabilities are stored as reference sets for the initiation of tunnel defects. After the training has been carried out, the values of the probabilities with which the noise takes on the given values at different time instants are matched to the type of defect and one of the possible technical states: serviceable, operational, partially operational, inoperable; pre-emergency; emergency, etc. It is also shown that the differences in the probabilities with which the noise takes on the same values at different times are indicators of the dynamics of changes in the malfunction in the tunnels. A database ofinformative attributes of the intensity of the development of failures is also created in the paper. For this database, the indicators of the dynamics of the development of a defect are determined, such as insignificant, slow, significant, intensive.

1. Aliev T. Noise Control of the Beginning and Development Dynamics of Accidents. Springer, 2019, 201 p.

2. Shvidkyi V. Ya., Gres A. A. Checkup of deformations when tunneling under the runways-in-use of Sheremetyevo airport, Transport construction, 2019, no. 3, pp. 23—25 (in Russian).

3. Ma kovsk ij L. V. , K ravchen ko V. V. Prospects for creation of the underground expressway system in the largest cities and megalopolises, Transport construction, 2018, no. 1, pp. 11—14 (in Russian).

4. Shevchenko A. A., Kobetskiy A. D., Boev A. O.Experience in application of automated systems for monitoring of metro tunnels, Transport construction,2019, no. 2, pp. 26—28 (in Russian).

5. Noorossana R., Saghaei A., Amiri A. Statistical Analysis of Profile Monitoring, Wiley, New York, 2012, 332 p., available at: https://www.wiley.com/en-us/Statistical+Analysis+of+Profile+Monitoring-p-9781118071977.

6. Aliluev S. V., Bolshakov A. A., Popov A. N., Teterin D. P. Methods and Algorithms for Control and Diagnostics of the Steering Gear of the Autonomous Underwater Vehicles, Mekhatronika, Avtomatizatsiya, Upravlenie, 2017, vol. 18, no. 4, pp. 264—269 (in Russian).

7. Bosh lya kov A. A. , Kova lev V. V. , Rubt sov V. I. Automated Fault Diagnostics in the Scanners of the Optical-Location Stations, Mekhatronika, Avtomatizatsiya, Upravlenie, 2017, vol. 18, no. 3,pp. 180—185 (in Russian).

8. Weihong (Grace) Guo, Jionghua (Judy) Jin, S. Jack Hu.Profile Monitoring and Fault Diagnosis Via Sensor Fusionfor Ultrasonic Welding, Journal of Manufacturing Science and Engineering, 2019, vol. 141, issue 8, pp. 081001-1-81001-13, available at: https://doi: 10.1115/1.4043731.

9. Yaser Zerehsaz, Chenhui Shao, Jionghua Jin.Tool wear monitoring in ultrasonic welding using high-order decomposition, Journal of Intelligent Manufacturing,2019, vol. 30, no. 2, pp. 657—669, available at: https://doi:10.1007/s10845-016-1272-4.

10. Kim J., Huang Q., Shi J., Chang T.-S. Online Multichannel Forging Tonnage Monitoring and Fault Pattern Discrimination Using Principal Curve, ASME Journal of Manufacturing Science and Engineering, 2006, vol. 128, no. 4, pp. 944—950, available at: https://doi.org/10.1115/1.2193552.

11. Amiri A., Zou C., Doroudyan M. H. Monitoring Correlated Profile and Multivariate Quality Characteristics, Quality and Reliability Engineering International, 2013, vol. 30, no. 1, pp. 133—142, available at: https:// doi.org/10.1002/qre.1483.

12. Xiaoli Li, Shen Dong, Zhejun Yuan.Discrete wavelet transform for tool breakage monitoring, International Journal of Machine Tools and Manufacture, 1999, vol. 39, no. 12, pp. 1935—1944, available at: https://doi: 10.1016/S0890-6955(99)00021-8.

13. Jian Guo, Zhaojun Li, Jionghua Jin. System Reliability Assessment with Multilevel Information Using the Bayesian Melding Method, Reliability Engineering & System Safety, 2018, vol. 170, pp. 1—268, available at: https://doi: 10.1016/j.ress.2017.09.020.

14. Weihong Guo, Chenhui Shao, Tae Hyung Kim, S. Jack Hu, Jionghua Jin, J. Patrick Spicer, Hui Wang.Online process monitoring with near-zero misdetection for ultrasonic welding of lithium-ion batteries: An integration of univariate and multivariate methods, Journal of Manufacturing Systems, 2016, vol. 38, no. 1, pp. 141—150, available at: https://doi: 10.1016/j.jmsy.2016.01.001.

15. Aliev T. A., Musaeva N. F., Gazizade B. I. Technologies for Monitoring the Dynamics of Damage Development in Drilling Rigs Using High-Order Moments of the Noise, Mechatronics, automation, control, 2020, vol. 21, no. 4, pp. 213—223 (in Russian).

16. Aliev T. A., Musaeva N. F., Suleymanova M. T. Algorıthms for constructıng the confıdence ınterval for the mathematcal expectatıon of the noıse and theır applıcatıon ın the control of the dynamıcs of accıdent development. Mechatronics, automation, control, 2020, vol. 21, no. 9, pp. 521—529 (in Russian).

17. Aliev T. A., Musaeva N. F. Technologies for Early Monitoring of Technical Objects Using the Estimates of Noise Distribution Density, Journal of Automation and Information Sciences, 2019, vol. 51, no. 9, pp. 12—23.

18. Aliev T. A., Musaeva N. F., Suleymanova M. T., Gazizade B. I. Analytic representation of the density function of normal distribution of noise, Journal of Automation and Information Sciences, 2015, vol. 47(8), no. 4, pp. 24—40.

19. Ventsel Y. S., Ovcharov L. A. The Theory of Random Processes and Its Engineering Applications, Moscow, KNORUS, 2013, 448 p. (in Russian).

20. Guidelinesfor the technical diagnosis of road tunnels, available at: https://www.normacs.ru/Doclist/doc/7UE.html.