Session: 14-03-01: Reliability and Safety in Industrial Automation Systems

Paper Number: 69998

Start Time: Monday, 06:30 PM

69998 - Demonstration of a Limited Scope Probabilistic Risk Assessment for Autonomous Warehouse Robots With OpenPRA 

For all safety-critical domains, starting with the nuclear industry and expanding into space, industrial automation, autonomous systems, transportation, medical, energy, and many more, probabilistic risk assessment (PRA) is an indispensable technology. To evaluate the risk [1], dependability [2], and resilience [3] characteristics of complex systems, PRA uses widely adopted methods. These include classical event trees, fault trees, Markov chains, Bayesian networks, and their numerous combinations and extensions. To analyze challenging failure scenarios of modern, intelligent, autonomous, and highly dynamic Cyber-Physical Systems, the integration of multiple PRA methods is needed. 

This paper presents a new PRA approach based on classical Event Tree Analysis (ETA) and Fault Tree Analysis (FTA) and provides the technical description of a new open-source software platform called OpenPRA and its associated open model exchange format. The OpenPRA framework aims to integrate the multiple PRA methods and tools into a universal, easy-to-use, and highly customizable environment. In particular, we introduce the modules for ETA and FTA. Each module consists of a solver and a public application programming interface (API). Both modules are based on theC++  Boost Graph Library. Also, an XML/JSON reader that provides the main functionality of the OpenPRA model exchange format is presented. Based on these modules, an algorithm was developed that parses and solves combined models calling the FTA and ETA solvers.

Besides, this paper describes a representative case study from the autonomous system domain, focusing on autonomous warehouse robots. We investigated a mission typical for an autonomous warehouse robot. For the selected mission, an event tree model was developed, which contains the individual states of the mission (e.g., charging, drive) as sequences of events. The failure probability of the events is modeled by fault tree models (e.g., loss of driving capabilities). This results in a combined model that demonstrates the functionality of the PRA. Using this case study, the obtained results are demonstrated and evaluated against existing legacy PRA tools, such as SAPHIRE, CAFTA, RiskSpectrum, or RISKMAN.

References:

[1] S. Kaplan and B. J. Garrick, “On The Quantitative Definition of Risk,” Risk Analysis, vol. 1, no. 1, pp. 11–27, 1981, doi: 10.1111/j.1539-6924.1981.tb01350.x.

[2] Avižienis, Algirdas, Jean-Claude Laprie, and Brian Randell. Dependability and its threats: a taxonomy. Building the Information Society. Springer, Boston, MA, 2004. 91-120.

[3] M. A. Diaconeasa, A. Mosleh, A. Morozov, and A. T. Tai, “Model-Based Resilience Assessment Framework for Autonomous Systems,” presented at the ASME 2019 International Mechanical Engineering Congress and Exposition, Nov. 2019, doi: 10.1115/IMECE2019-12288.

Presenting Author: Philipp Grimmeisen University of Stuttgart

Authors:

Philipp Grimmeisen University of Stuttgart
Artur Karimov Ufa State Aviation Technical University
Mihai A. Diaconeasa North Carolina State University
Andrey Morozov University of Stuttgart