Evaluating the Monitoring System Train subjects on EPS, monitoring system Train subjects on EPS, monitoring system Vary presence/absence of causal navigation,

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Evaluating the Monitoring System Train subjects on EPS, monitoring system Train subjects on EPS, monitoring system Vary presence/absence of causal navigation, model hierarchy Vary presence/absence of causal navigation, model hierarchy Measure elapsed time, degree of success Measure elapsed time, degree of success Hypotheses: as monitoring capabilities grow, Hypotheses: as monitoring capabilities grow, - Time for fault detection, isolation, and diagnosis will decrease - Probability of correct diagnosis, isolation & detection will increase - Average degree of success per subject will increase Correct diagnosis =3, correct isolation = 2, correct detection = 1 Correct diagnosis =3, correct isolation = 2, correct detection = 1

No hierarchy No hierarchy Variable faults Variable faults Graph predicted/observed Graph predicted/observed Select variables by system Select variables by system + Hierarchical models + Model navigation + Causal navigation + Process fault display Three Monitoring System Designs PowerGeneration.supply PowerStorage.supply SecondaryPower.demand SecondaryPower.Ba1.soc SecondaryPower.Ba1.i SecondaryPower.Ba1.v

For each time step: For all exogenous variables: Estimated(v) = Measured(v), Ambiguity(v)=nil For all other variables: Estimated(v) = 0, Ambiguity(v) = nil, For all processes: Ambiguity(p) = {p} Push all exogenous processes and variables on OPEN Execute a queue and process strategy on OPEN nodes, N; if N is a process if all its inputs have been seen Push Successors(N) onto OPEN if N is active then for each variable in its successors Estimated(v) = N(inputs(N)) + Estimated(v) Ambiguity(v)= union(Ambiguity(v), Ambiguity(N)) else Push(N, bottom(OPEN)) Fault Isolation in a Causal Graph

if N is a variable if all its input processes have been seen Push Successors(n) onto OPEN if Measurable(N) and Out_of_Bounds(Estimated(n), Measured(n)) FAULT_REGION(N) = Ambiguity(n) if Measurable(N) Estimated(N) = Measured(N) Ambiguity(N) = nil /* The ambiguity stops here */ For each process p in Successors(N) Ambiguity(p) = union(Ambiguity(p), Ambiguity(N)) else Push(n, bottom(OPEN)) END Queue and Process Fault Isolation in a Causal Graph (contd)

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