Past performance is not an indicator of future performance, especially for rare events. Past performance would not have indicated (at least not to those involved at the time) what would happen at Bhopal, Texas City, or any other accident you can think of. So, what is the definition of a safe plant? Some have responded, “One that hasn’t had an accident.” Unfortunately, such thinking is flawed. Similarly, what’s the definition of a safe driver? One that hasn’t had an accident? If you were a passenger in a vehicle, yet you observe the driver texting, speeding, being aggressive, and you know they are a bit under the influence, would you be reassured if they told you they’ve never had an accident? It should obvious to everyone that a safe driver is one who follows the rules and laws, doesn’t drive under the influence, doesn’t texting, wears a safety belt, keeps their car in good condition, etc. Doing so does not guarantee there will never be an accident, but it does lower the probability. The same applies to a safe process plant (i.e., following all the rules, standards and regulations effectively). And we can model the impact of this!
Yet it’s easy for functional safety engineers to focus instead on math, hardware calculations, and the selection of 3rd party certified devices. The frequentist based statistical calculations result in extremely small numbers that cannot be proven. However, the prior belief probability can be updated with even subjective information. Doing so can change the answer orders of magnitude. The key takeaway is that the focus of functional safety should be on effectively following all the steps in the ISA/IEC 61511 safety lifecycle and the requirements of the OSHA PSM regulation, not the math or certification of devices. Both documents were essentially written in blood through lessons learned the hard way by many organizations.
To learn more about the use of Bayesian networks in functional safety, read the full paper here.
Blog part 1: The use of Bayesian Networks in Functional Safety