Technology audit and production reserves

The object of research is a non-renewable system with a single sliding reservation. Such system consists of two main subsystems, one redundancy and two switching devices. While both main subsystems are operable, the spare subsystem is in an unloaded state. The redundancy system is designed to replace any major subsystem after its failure. Switching devices commute the main subsystems with a redundancy one. During the audit, it was revealed that the switching devices allow errors. In particular, a mistake of the first type, that is, they switch in advance, and a second type of error, that is, they pass the switching moment. This reduces the reliability of the system and leads to underutilization of the inherent resource.

An approach is proposed that quantitatively takes into account the influence of errors of the first and second type on the probability of failure-free operation of the system under study during its design. The approach consists of two stages. At the first stage, the reliability of the system is mathematically described by the dynamic failure tree. At the second stage, based on the failure tree, a Markov model is formed. Applying it, it is possible to calculate the probabilistic characteristics of the system.

The result is a mathematical relationship between the probability of trouble-free operation of the system and the parameters of the components of the system. In particular, the operating time to failure of the main and redundancy subsystems, as well as the parameters of switching devices that corresponds to errors of the first and second type. The form of presentation of the obtained results for the end user is a software product that automatically generates a family of graphs for reliability evaluation. Ignoring the errors of switching devices in the design of systems reduces their actual reliability, leads to underutilization of the reserve component resources, and also increases the probability of emergency situations.

Using a more accurate mathematical model makes it possible to monitor the errors of switching devices during the design of the system. The simulation results will be useful for selecting the parameters of the switching devices.

Stefanovych, T., Shcherbovskykh, S. (2017). Taking into account type I and II errors of switching device for system with 2-out-of-3 redundancy. Information extraction and processing, 45 (121), 56–62.

Zhang, P., Chan, K. W. (2012). Reliability Evaluation of Phasor Measurement Unit Using Monte Carlo Dynamic Fault Tree Method. IEEE Transactions on Smart Grid, 3 (3), 1235–1243. doi: http://doi.org/10.1109/tsg.2011.2180937

Shcherbovskykh, S.,, Lozynsky, O., Marushchak, Y. (2011). Failure Intensity determination for system with standby doubling. Przeglad Elektrotechniczny, 87 (5), 160–162.

Lin, Y.-H., Li, Y.-F., Zio, E. (2016). A Reliability Assessment Framework for Systems With Degradation Dependency by Combining Binary Decision Diagrams and Monte Carlo Simulation. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 46 (11), 1556–1564. doi: http://doi.org/10.1109/tsmc.2015.2500020

Shcherbovskykh, S., Spodyniuk, N., Stefanovych, T., Zhelykh, V., Shepitchak, V. (2016). Development of a reliability model to analyse the causes of a poultry module failure. Eastern-European Journal of Enterprise Technologies, 4 (3 (82)), 4–9. doi: http://doi.org/10.15587/1729-4061.2016.73354

Shcherbovskykh, S., Stefanovych, T. (2015). Reliability model developing for protective fittings taking into account load-sharing effect. Eastern-European Journal of Enterprise Technologies, 1 (3 (73)), 37–44. doi: http://doi.org/10.15587/1729-4061.2015.35951

Volk, M., Junges, S., Katoen, J.-P. (2018). Fast Dynamic Fault Tree Analysis by Model Checking Techniques. IEEE Transactions on Industrial Informatics, 14 (1), 370–379. doi: http://doi.org/10.1109/tii.2017.2710316

Yuchang, M. (2014.) A Multiple-Valued Decision-Diagram-Based Approach to Solve Dynamic Fault Trees. IEEE Transactions on Reliability, 63 (1), 81–93. doi: http://doi.org/10.1109/tr.2014.2299674

Zhou, Z., Zhang, Q. (2017) Model Event/Fault Trees With Dynamic Uncertain Causality Graph for Better Probabilistic Safety Assessment. IEEE Transactions on Reliability, 66 (1), 178–188. doi: http://doi.org/10.1109/tr.2017.2647845

Xing, L., Morrissette, B. A., Dugan, J. B. (2014). Combinatorial Reliability Analysis of Imperfect Coverage Systems Subject to Functional Dependence. IEEE Transactions on Reliability, 63 (1), 367–382. doi: http://doi.org/10.1109/tr.2014.2299431

Zhu, P., Han, J., Liu, L., Lombardi, F. (2015). Stochastic Approach for the Analysis of Dynamic Fault Trees With Spare Gates Under Probabilistic Common Cause Failures. IEEE Transactions on Reliability, 64 (3), 878–892. doi: http://doi.org/10.1109/tr.2015.2419214

Zhu, P., Han, J., Liu, L., Zuo, M. J. (2014). A Stochastic Approach for the Analysis of Fault Trees With Priority AND Gates. IEEE Transactions on Reliability, 63 (2), 480–494. doi: http://doi.org/10.1109/tr.2014.2313796

Zhu, P., Guo, Y., Lombardi, F., Han, J. (2017). Approximate reliability of multi-state two-terminal networks by stochastic analysis. IET Networks, 6 (5), 116–124. doi: http://doi.org/10.1049/iet-net.2017.0033

Stefanovych T., Shcherbovskykh S. Taking into account type I and II errors of switching device for system with 2-out-of-3 redundancy // Information extraction and processing. 2017. Vol. 45, Issue 121. P. 56–62.

Zhang P., Chan K. W. Reliability Evaluation of Phasor Measurement Unit Using Monte Carlo Dynamic Fault Tree Method // IEEE Transactions on Smart Grid. 2012. Vol. 3, Issue 3. P. 1235–1243. doi: http://doi.org/10.1109/tsg.2011.2180937 

Shcherbovskykh S., Lozynsky O., Marushchak Y. Failure Intensity determination for system with standby doubling // Przeglad Elektrotechniczny. 2011. Vol. 87, Issue 5. P. 160–162.

Lin Y.-H., Li Y.-F., Zio E. A Reliability Assessment Framework for Systems With Degradation Dependency by Combining Binary Decision Diagrams and Monte Carlo Simulation // IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2016. Vol. 46, Issue 11. P. 1556–1564. doi: http://doi.org/10.1109/tsmc.2015.2500020 

Development of a reliability model to analyse the causes of a poultry module failure / Shcherbovskykh S. et. al. // Eastern-European Journal of Enterprise Technologies. 2016. Vol. 4, Issue 3 (82). P. 4–9. doi: http://doi.org/10.15587/1729-4061.2016.73354 

Shcherbovskykh S., Stefanovych T. Reliability model developing for protective fittings taking into account load-sharing effect // Eastern-European Journal of Enterprise Technologies. 2015. Vol. 1, Issue 3 (73). P. 37–44. doi: http://doi.org/10.15587/1729-4061.2015.35951 

Volk M., Junges S., Katoen J.-P. Fast Dynamic Fault Tree Analysis by Model Checking Techniques // IEEE Transactions on Industrial Informatics. 2018. Vol. 14, Issue 1. P. 370–379. doi: http://doi.org/10.1109/tii.2017.2710316 

Yuchang M. A Multiple-Valued Decision-Diagram-Based Approach to Solve Dynamic Fault Trees // IEEE Transactions on Reliability. 2014. Vol. 63, Issue 1. P. 81–93. doi: http://doi.org/10.1109/tr.2014.2299674 

Zhou Z., Zhang Q. Model Event/Fault Trees With Dynamic Uncertain Causality Graph for Better Probabilistic Safety Assessment // IEEE Transactions on Reliability. 2017. Vol. 66, Issue 1. P. 178–188. doi: http://doi.org/10.1109/tr.2017.2647845 

Xing L., Morrissette B. A., Dugan J. B. Combinatorial Reliability Analysis of Imperfect Coverage Systems Subject to Functional Dependence // IEEE Transactions on Reliability. 2014. Vol. 63, Issue 1. P. 367–382. doi: http://doi.org/10.1109/tr.2014.2299431 

Stochastic Approach for the Analysis of Dynamic Fault Trees With Spare Gates Under Probabilistic Common Cause Failures / Zhu P. et. al. // IEEE Transactions on Reliability. 2015. Vol. 64, Issue 3. P. 878–892. doi: http://doi.org/10.1109/tr.2015.2419214 

A Stochastic Approach for the Analysis of Fault Trees With Priority AND Gates / Zhu P. et. al. // IEEE Transactions on Reliability. 2014. Vol. 63, Issue 2. P. 480–494. doi: http://doi.org/10.1109/tr.2014.2313796 

Approximate reliability of multi-state two-terminal networks by stochastic analysis / Zhu P. et. al. // IET Networks. 2017. Vol. 6, Issue 5. P. 116–124. doi: http://doi.org/10.1049/iet-net.2017.0033