YAP: Tool Support for Deriving Safety Controllers from Hazard Analysis and Risk Assessments

TL;DR

This paper presents YAP, a research tool that supports deriving safety controllers from hazard analysis and risk assessments by modeling, generating, and verifying stochastic models for optimal controller selection.

Contribution

It introduces a workflow and tool support for safety controller derivation from hazard analysis, integrating risk modeling, stochastic model generation, and verification.

Findings

Successful application to a collaborative robot example

Effective derivation of safety controllers from hazard data

Integration of risk modeling with stochastic model checking

Abstract

Safety controllers are system or software components responsible for handling risk in many machine applications. This tool paper describes a use case and a workflow for YAP, a research tool for risk modelling and discrete-event safety controller design. The goal of this use case is to derive a safety controller from hazard analysis and risk assessment, to define a design space for this controller, and to select a verified optimal controller instance from this design space. We represent this design space as a stochastic model and use YAP for risk modelling and generation of parts of this stochastic model. For the controller verification and selection step, we use a stochastic model checker. The approach is illustrated by an example of a collaborative robot operated in a manufacturing work cell.