The article "Алгоритмы определения вероятности рисков аварий в тоннелях по характеристикам помехи зашумленных сигналов" (“Algorithms for determining the probability of risks of accidents in tunnels based on the characteristics of the noise of noisy signals”) by Academician Telman Aliyev, Advisor of ANAS, Professor Naila Musayeva, DSc (Eng), Head of the Laboratory of Identification of Stochastic Processes, and Narmin Matanat Suleymanova, dissertationist of the Institute, has been published in the prestigious Scopus-indexed journal Mekhatronika, Avtomatizatsiya, Upravlenie.
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. It is shown 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 of informative 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.