I’d start with the basics: the failure rate per component, the mean time between failures, and the probability that any single fault will cascade into a system‑wide failure. Then I’d map those numbers onto a risk matrix that scores each potential hazard by likelihood and impact, and overlay that with the safety margin I’ve built in through redundancy. I also keep a running log of test coverage—how many simulation scenarios, how many real‑world trials, and how many verification checks—so I can see where gaps might slip through. Finally, I audit the design every iteration to ensure the fault tolerance level still meets the tolerance I set at the beginning.

Yes, I rely on a few tried‑and‑true habits. First, I run a formal Failure Mode and Effects Analysis before each major build; it forces me to list every component, think of every way it could break, and then check whether my test suite actually exercises that mode. Next, I sprinkle deliberate faults—so‑called “fault‑injection”—into the running system: spin up a corrupted sensor reading, drop a packet, or inject a sudden power dip, then watch how the system reacts. If the fault slips through unnoticed, that’s a red flag. To confirm that each redundancy truly cuts off a fault path, I map the dependency graph of the whole system. Every node must have at least one disjoint path to the critical function. I run “chaos‑engineering” drills: take out each redundancy in isolation and verify that the remaining components can still cover the load and keep the safety margins. If an extra component ends up being the only one that fails when it’s removed, I know I’ve created a new single point of failure. By iterating this cycle—identify, test, isolate, and audit—I keep the system robust while avoiding hidden pitfalls.