SafetyLens: Visual Data Analysis of Functional Safety of Vehicles

Modern automobiles have evolved from just being mechanical machines to having full-fledged electronics systems that enhance vehicle dynamics and driver experience. However, these complex hardware and software systems, if not properly designed, can experience failures that can compromise the safety of the vehicle, its occupants, and the surrounding environment. For example, a system to activate the brakes to avoid a collision saves lives when it functions properly, but could lead to tragic outcomes if the brakes were applied in a way that's inconsistent with the design. Broadly speaking, the analysis performed to minimize such risks falls into a systems engineering domain called Functional Safety. In this paper, we present SafetyLens, a visual data analysis tool to assist engineers and analysts in analyzing automotive Functional Safety datasets. SafetyLens combines techniques including network exploration and visual comparison to help analysts perform domain-specific tasks. This paper presents the design study with domain experts that resulted in the design guidelines, the tool, and user feedback.

Fuzzy-logic-based safety verification framework for nuclear power plants

This article presents a practical implementation of a safety verification framework for nuclear power plants (NPPs) based on fuzzy logic where hazard scenarios are identified in view of safety and control limits in different plant process values. Risk is estimated quantitatively and compared with safety limits in real time so that safety verification can be achieved. Fuzzy logic is used to define safety rules that map hazard condition with required safety protection in view of risk estimate. Case studies are analyzed from NPP to realize the proposed real-time safety verification framework. An automated system is developed to demonstrate the safety limit for different hazard scenarios.