When a monitoring system fails, the first diagnosis can arrive too quickly: the sensor is bad, the modem is offline, the battery is too small.
I sometimes ask a stranger question instead.
If the system were healthy and I wanted to create exactly this symptom, what would I have to break?
Reason through mechanisms
The question changes the work from naming a component to reproducing a pattern. What failure could make an abnormal value appear only after a technician left? What could preserve stored history while live visibility disappeared? What behaviour could make a winter energy problem survive additional solar capacity?
Each answer is a hypothesis, not proof. The next step is to seek a discriminating observation — something that would be different if one mechanism rather than another were responsible.
Failing successfully
Success is not manufacturing a fault in a live environmental system. It is constructing a testable failure model carefully enough that the evidence can challenge it.
The method belongs across the lifecycle because causes do not respect component boundaries.