Probabilistic Models and Methods for Processing Data in “Smart” Monitoring System to Define Rational Preventive
4. Examples of implementation for processing data in “smart” monitoring system (with the demonstrations of Steps 1-5)
Example 1 for demonstration of Steps 1-5 implementation [15-18]. In 2016 the
“smart” remote monitoring system (RMS) was designed for the Joint-Stock Company “Siberian Coal Energy Company” (“SUEK” – www.suek.ru). Because of thousands of system elements and subsystems should be covered by monitoring, universal formal technologies for logic describing the processes of occurring and activating dangers, diagnostics and recovering the integrity of the values of parameters, maintained conditions for coal miners, normal operation of machinery, equipment and whole mine are established. The Technologies 1 and 2 above are met formal logic processes. It means the implementation of Step 1.
The implemented Step 2 is explained by the next propositions. Monitored parameters have been chosen for all valuable conditions, machinery and equipment. For each parameter the ranges of possible values of conditions are established: “Working range inside of norm”, “Out of working range, but inside of norm”, “Abnormality”, it may be interpreted by similarly light signals – "green", "yellow", "red" – see Figure 2. The condition “Abnormality” characterizes a threat to lose system integrity after danger influence (on logic level this range “Abnormality” may be interpreted as failure, fault,
unacceptable risk or quality, etc.). This construction allows to extract data for
probabilistic modeling: time between moments of the occurrences of dangers (potential threats), activation time of occurred dangers, recovery time.
Figure 2. The universal elementary ranges for traced parameters
All parameters are represented as the system elements. The conditions for coal miners, operating machinery and equipment are represented as complex subsystems integrated from serial elements (serial structure) and parallel subsystems for reservation (parallel structure). The whole mine is represented as complex system integrated from serial and parallel structures. Logic interpretation for serial structure from two elements is: the structure goes into a state of lost integrity when either 1st or 2nd element integrity is lost. Logic interpretation for parallel structure from two subsystems is: the structure goes into a state of lost integrity when both 1st and 2nd subsystems integrities are lost.
Step 3 has allowed to do the adaptation of proposed probabilistic models and methods (models “Black Box” for Technologies 1, 2 and the methods to generate new probabilistic models for complex structures, allowing prognostic researches on a level of PDF of time before a next abnormality) for implementing to parameters, valuable conditions, machinery and the equipment of the whole mine. Considering
consequences risk R(Treq) to lose integrity means probability to be though one time in
“red” range during period Treq – see Figures 1 and 2. PDF Ωoccur(t) of time between
neighboring occurrences of danger (from the “green” at the “yellow” range), PDF Ωactiv(t) of activation time of occurred danger (the time from the 1-st occurrence at the
“yellow” range to the 1-st occurrence at the “red” range) and PDF A(τ) of time
between operator’s error are approximated by exponential PDF – input for mean time see from Figure 2 and from the processing of statistics.
Step 4 has allowed to implement proposed probabilistic models and methods by the
software of “smart” RMS, to define acceptable risks. RMS is intended for a
possibility of prediction and the prevention of possible emergencies, minimization of a role of human factor regarding control and supervising functions. It may be reached on the basis of gathering and analytical processing in real time the information on controllable parameters of conditions, machinery and monitored equipment.
Step 5 has allowed to to estimate effects from a use of preventive measures in real time and to define rational preventive measures of supporting reliability and safety by
solving optimization problems with limits on acceptable risks. Effects are reached on
the basis of gathering and analytical processing in real time the information on controllable parameters of objects monitored – see Figure 3.
Figure 3. Example of implementation
The proposed probabilistic models and methods help to predict in real time the mean residual time before the next parameters abnormalities for two different cases: without any reaction of responsible staff and if obligatory adequate reaction is always.
Example 2 for “Black Box”. Let the mean time between neighboring occurrences of
danger (from the “green” at the “yellow” range) is 1 month, i.e. Toccur =1month, Tdiag
=Тerr.=0. The case “without any reaction” after parameter transition from “green” into
“yellow” range is characterized by input Tbetw =1year, and the input Tbetw=8hours
(about every shift) characterizes the case “for obligatory adequate reaction in real time”. The result of the prediction of the mean residual time before the next parameters abnormalities (see Figure 3) helps to define rational preventive measures of supporting safety in real time [16, 17].
Analytical results of RMS operation for responsible staff is transparent for all interested parties and adequate preventive reaction in time allows to increase a mean residual time before the next parameters abnormalities.
Example 3 (for complex structure). Let’s analyse a fragment of the main gas
pipeline Bovanenkovo-Ukhta (more than 1200 km) by probabilistic modelling of natural and technogenic processes. It constructed over an earth surface. Sub- fragments between compressor stations (9 stations - Bajdaratsky, Jarynsky, Gagaratsky, Vorkuta, Usinsk, Intinsky, Syninsky, Chikshinsky, Maloperansky) are allocated. There are serial subsystems and every subsystem has parallel structure of elements (pipeline) - see Figure 4, 5.
Figure 4. The analyzed fragment of the main
gas pipeline
Figure 5. The serial-parallel structure for
modelling processes
About 75-90% from the pipelines are under natural threats, including ice drift (threats for constructions). It is required to estimate risk to lose integrity (quality of operation) of fragment Bovanenkovo-Ukhta in 2023-2043.
The solving of a problem is the next [15-16]. According to estimations of experts, in 20-30 years there will be considerable changes of climatic conditions which will cause rise in temperature of frozen thicknesses, increase in depth seasonal thawing and, as consequence, decrease in stability and bearing ability of the bases for a gas pipeline and other engineering constructions. Technical characteristics of elements between compressor stations are considered as identical, except for the first subfragment (between stations Bajdaratsky and Jarynsky) which is underwater transition (reservation by 4 elements-pipelines) – see Figure 4. Initial data for modelling have been generated depending on conditions of concrete sites and specificity of a territorial arrangement of a line.
Results of modelling processes have shown, that risk to lose integrity (quality of operation) for 20 prognostic years during the period 2023-2043 is equal to 0.6 – 0.8. In comparison with other precedents these figures speak about expediency of undertaking of preventive measures, and also about the necessity of working out the Plan of emergencies liquidation. If period between system controls will be reduced from 6 to 3 months the risk to lose integrity in 2023-2043 is nearby 0.16 – 0.44. It is twice more low, rather than for an existing mode of maintenance and repair. On the basis of these results the following recommendations are scientifically proved:
- to establish a risk level to lose integrity (quality of operation) 0,38 within 10 years
of operation as unacceptable (on the base of «precedent principle»);
- to pass to the quarterly control of a condition of system after 10 years of operation (i.e. since 2024);
- to use annual planning of maintenance measures service on the basis of modeling
processes for rational risk management in acceptable limits.
Example 4. What about the possible pragmatic effects? (Step 5)
The Complex (as a part of global system) of risks predictions for techno-genic safety support on the objects of oil & gas distribution has been awarded by Award of the Government of the Russian Federation in the field of a science and technics for 2014. The created peripheral posts are equipped additionally by the means of Complex to feel vibration, a fire, the flooding, unauthorized access, hurricane, and also the intellectual means of adequate reaction, capable to recognize, identify and predict a development of extreme situations. The applications of Complex for 200 objects in several regions of Russia during the period 2009-2014 have already provided economy about 8,5 Billions of Roubles. The economy is reached at the expense of effective implementation of the functions of risks prediction and processes optimization [2, 4, 14-16].
Conclusion
The universal approach, applicable in different areas for processing data in “smart” monitoring systems and based on the original probabilistic models, is proposed. The approach includes the next 5 Steps:
- Step 1 - to define universal formal technologies for logic describing the processes of occurring and activating dangers, diagnostics and recovering system integrity, considering the possibilities of periodic control and monitoring;
- Step 2 - to define universal elementary ranges for the traced parameters (from reliability or safety point of view), monitored conditions and interpretation of events, allowing analytical data processing by probabilistic modeling;
- Step 3 - to develop probabilistic models for two Technologies, which can be used for “Black Box”, and the methods to generate new probabilistic models for complex structures, allowing prognostic researches on a level of the probability destribution function (PDF) of time before a next abnormality for one element, subsystem, system; - Step 4 - to implement the proposed probabilistic models and methods of step 3 for
processing data in “smart” monitoring system, to define acceptable risks;
- Step 5 - to estimate effects from a use of preventive measures in real time and to define rational preventive measures of supporting reliability and safety by solving
optimization problems with limits on acceptable risks.
Implementation of the approach for processing data in “smart” monitoring system allows to define rational preventive measures of supporting reliability and safety. They proposed models and methods are applicable to be used in real time and also for short- and long-term planning.
The efficiency of approach is demonstrated by the examples of implementation in the for the Joint-Stock Company “Siberian Coal Energy Company”, for a fragment of the main gas pipeline Bovanenkovo-Ukhta, for the Complex of risks predictions for techno-genic safety support on the objects of oil & gas distribution (applications of which for 200 objects in several regions of Russia during the period 2009-2014 have provided the economy about 8,5 Billions of Roubles).
References
[1] Kostogryzov, A., Nistratov A., Zubarev, I., Stepanov, P., Grigoriev, L. (2015)
About accuracy of risks prediction and importance of increasing adequacy of used probabilistic models. Journal of Polish Safety and Reliability Association.
Summer Safety and Reliability Seminars, Volume 6, Numbers 2, 2015, pp. 71-
80.
[2] Kostogryzov A., Nistratov G. (2004) Standardization, mathematical modelling,
rational management and certification in the field of system and software engineering" (80 standards, 100 mathematical models, 35 software tools, more than 50 practical examples). Armament. Policy. Conversion, Moscow.
[3] Zio E. (2006) An Introduction to the Basics of Reliability and Risk Analysis.
World Scientific.
[4] Kostogryzov, A. and Stepanov, P. (2008) Innovative management of quality
and risks in systems life cycle. Armament. Policy. Conversion, Moscow.
[5] Kolowrocki, K., Soszynska-Budny, J. (2011) Reliability and Safety of Complex
Technical Systems and Processes. Springer-Verlag London Limited.
[6] Eid, M. and Rosato, V. (2016) Critical Infrastructure Disruption Scenarios
Analyses via Simulation. Managing the Complexity of Critical Infrastructures.
A Modelling and Simulation Approach, SpringerOpen, pp. 43-62.
[7] Di Pietro, A., Liberto, C., Flourentzou, N., Kyriakides, E., Pothof , I. and
Valenti, G. (2016) Physical Simulators of Critical Infrastructures. Managing the Complexity of Critical Infrastructures. A Modelling and Simulation Approach, SpringerOpen, pp. 63-84.
[8] Huiskamp, W. and van den Berg, T. (2016) Federated Simulations. Managing
the Complexity of Critical Infrastructures. A Modelling and Simulation Approach, SpringerOpen, pp. 109-138.
[9] Kostogryzov, A., Nistratov, G., Kleshchev, N. (2006) Mathematical Models
and Software Tools to Support an Assessment of Standard System Processes.
Proceedings of the 6th International SPICE Conference on Process Assessment
and Improvement, Luxembourg, pp. 63-68
[10] Kostogryzov, A., Nistratov, G. (2006) Mathematical Models and Software Tools for Analyzing System Quality and Risks according to standard requirements. Proceedings of the 6th International scientific school "Modelling
and Analysis of safety and risk in complex systems" (MASR – 2006), Saint
Petersburg, Russia, July 4 - 8, pp. 155-163.
[11] Grigoriev, L.I., Kershenbaum, V. and Kostogryzov, A. (2010) System
foundations of the management of competitiveness in oil and gas complex.
National Institute of oil and gas, Moscow.
[12] Kostogryzov A., Krylov V., Nistratov A., Nistratov G., Popov V., Stepanov P. (2011) Mathematical models and applicable technologies to forecast, analyze and optimize quality and risks for complex systems, Proceedings of the 1st
International Conference on Transportation Information and Safety（ICTIS),
[13] Kostogryzov, A., Nistratov, A., Nistratov, G. (2012) Applicable Technologies to Forecast, Analyze and Optimize Reliability and Risks for Complex Systems
// Proceedings of the 6th International Summer Safety and Reliability Seminar,
September 2012, Poland, V.3, No.1, pp. 1-14.
[14] Kostogryzov, A., Nistratov, G. and Nistratov, A. (2012) Some Applicable
Methods to Analyze and Optimize System Processes in Quality Management.
Total Quality Management and Six Sigma, InTech, pp. 127-196.
[15] Abrosimov, N., Kostogryzov, A., Mahutov, N. at al. (2015) Security of Russia.
Legal, Social&Economic and Scientific&Engineering Aspects. The Scientific Foundations of Technogenic Safety. Under the editorship of N. Mahutov N.A. –
Znanie, Moscow.
[16] Artemyev, V., Kostogryzov, A., Rudenko, Ju., Kurpatov, O., Nistratov, G., Nistratov, A. (2017) Probabilistic methods of estimating the mean residual time before the next parameters abnormalities for monitored critical systems. Proceedings of the 2nd International Conference on System Reliability
and Safety (ICSRS), Milan, Italy, December 20-22, 2017,pp. 368-373
[17] Artemyev, V., Rudenko, Ju., Nistratov, G. (2018) Probabilistic methods and
technologies of risks prediction and rationale of preventive measures by using “smart systems”. Applications to coal branch for increasing Industrial safety of enterprises. Probabilistic modeling in system engineering. InTechOpen, 2018,
pp.23-51.
[18] Kershenbaum, V., Grigoriev, L., Kanygin, P., Nistratov, A. (2018) Probabilistic
modeling processes for oil and gas systems. Probabilistic modeling in system
engineering. InTechOpen, 2018, pp. 55-79.
[19] Feller, W. (1971) An Introduction to Probability Theory and Its Applications, Vol. II, Willy.
[20] Klimov, G. (1983) Probability theory and mathematical statistics, Мoscow