Tools

Introduction The Failure Modes, Effects and Diagnostic Analysis (FMEDA) is where an ISO 26262 safety case gets its key numbers. It looks at each component, its possible failure modes, the effect of each failure on the safety goal, and whether a diagnostic can detect it. From this table, we calculate SPFM, LFM, and PMHF. Yet for most teams, FMEDA is still a manually built spreadsheet. It can take weeks to create and is painful to update after a design change. The two hardest q

In a previous blog post, we have discussed how a fault tree is built, a top-down map of how a system can fail, starting from the worst outcome and branching down to root causes. A tree like that tells you how a system can fail and where its weak points are. But it leaves a crucial question unanswered: how likely is that failure, really? Is the top event a one-in-a-thousand risk or a one-in-a-billion one? Answering that is the job of quantitative fault tree analysis, and it's

While building SLIKWORKS, we used AI to automate much of the safety analysis work. Along the way, we encountered a number of challenges in applying LLMs and other ML models to this domain. These challenges are described in more detail in our SAE publication - AI-Powered FMEA in the Safety-Critical Industry. Below, we outline some of the key challenges we faced. Fig: AI tool challenges in mission-critical systems Hallucinations in LLMs Hallucinations in LLMs refer to instances

Why do we need modern tools for functional safety? Product development in safety-critical industries such as automotive, aerospace, and robotics is no longer confined to a single team or location. Engineers, developers, and safety experts often work across different countries, time zones, and organizations. As of early 2025, safety engineering work has shifted to one that is increasingly hybrid, where 52% of such employees are working in a hybrid/remote manner. To function e