Functional and operational risk as a criterion for assessing the durability of an autonomous energy system

The article presents new models and methods for estimating the residual service life of an autonomous energy system, using the functional operational risk criterion (FOR). The purpose of the article is to demonstrate a new method of durability evaluation using the fuzzy logic and soft computing framework. Durability in the article is understood as a complex property directly adjacent to the complex property of system resilience, as understood in the Western practice of assessing and ensuring the reliability of technical systems. Due to the lack of reliable homogeneous statistics on system equipment failures and recoveries, triangular fuzzy estimates of failure and recovery intensities are used as fuzzy functions of time based on incomplete data and expert estimates. The FOR in the model is the possibility for the system availability ratio to be below the standard level. An example of the evaluation of the FOR and the residual service life of a redundant cold supply system of a special facility is considered. The transition from the paradigm of structural reliability to the paradigm of functional reliability based on the continuous degradation of the technological parameters of an autonomous energy system is considered. In this case, the FOR can no longer be evaluated by the criterion of a sudden failure, nor is it possible to build a Markov’s chain on discrete states of the technical system. Assuming this, it is appropriate to predict the defi ning functional parameters of a technical system as fuzzy functions of a general form and to estimate the residual service life of the technical system as a fuzzy random variable. Then the FOR is estimated as the possibility for the residual life of the technical system to be below its warranty period, as determined by the supplier of the equipment.

1. Cherkesov G. N., Nedosekin A. O. Evaluation of the survivability of complex structures with reusable impacts of high accuracy (part 1). Nadejnost = Dependability 2016; (2): 3–15. (In Russ.)

2. Christopher W. Zobel and Lara Khansa Quantifying Cyberinfrastructure Resilience against Multi-Event, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061-0235.

3. Nedosekin A. O., Abdoulaeva Z. I. Assessment of industrial and economic risks of the enterprise. St. Petersburg: Polytechnic University Publishing House 2016;: 107 (In Russ.)

4. Smirnov A. V., Nedosekin A. O., Makarenko D. P., Aleksandrov S. V. Prediction of residual life of equipment of technical systems of special objects of the Ministry of Defense of the Russian Federation. Reports: Actual issues of the development of systems of autonomous power supply facilities of the Ministry of Defense of the Russian Federation. St. Petersburg: Polytechnic University Publishing House 2017;: 116–122. (In Russ.)

5. Nedosekin A. O., Makarenko D. P. Abdoulaeva Z. I. Fuzzyprobabilistic model for risk assessment of responsible technical systems. Informatsia i kosmos = Information and space 2018; (1): 92– 99. (In Russ.)

6. Nedosekin A. O., Makarenko D. P., Abdoulaeva Z. I., Kozlovsky A. N. The main ways of modeling uncertainty in complex systems. Informatizatsia i sviaz = Informatization and Communication 2017; (4): 157–160. (In Russ.)

7. Abdoulaeva Z. I., Nedosekin A. O. Strategic analysis of innovation risks. St. Petersburg: Ed. SPbGPU 2013;: 145 (In Russ.)

8. Zobel C. W., Khansa L. Quantifying Cyberinfrastructure Resilience against Multi-Event Attacks. Decision Sciences, Volume 43 Number 4, August 2012;: 687–710.

9. Cherkesov G. N. Reliability of hardware and software systems. St.Petersburg: Peter 2005;: 478. (In Russ.)