DSF Core: Integrated Decision Support for Optimal Scheduling of Lifetime Extension Strategies for Industrial Equipment

. 2023 Jan 25;23(3):1332. doi: 10.3390/s23031332

Algorithm 2 Algorithm for main optimization for decision variables

θ

1:
initialize $σ$ as the optimal one found during pre-optimization
2:
for every KPI time series (each corresponding to a component—strategy type combination) and failure probability time series (each corresponding to a component—failure type combination) do
3:
compute the $100 (1 - n r) %$ percentiles of its instant values observed during the computation of g for the optimal pre-optimization solution, with resolution r [ $n \in N, 1 - n r \in [0, 1]$ ]
4:
end for
5:
while no termination criterion (maximum epochs reached or g not improved within 1 epoch) applies do
6:
for every decision variable do
7:
if the decision variable corresponds to KPI threshold or failure probability threshold then
8:
compute g for the closest lower and higher values for this threshold corresponding to respective percentiles if not computed with same arguments yet
9:
else(decision variable corresponds to corrective strategy “actuator”)
10:
compute g for the other value of this binary “actuator” if not computed with same arguments yet
11:
if the value of g is better than the optimal found so far then
12:
update optimal $θ$ and optimal g value
13:
update KPI and probability percentiles based on new optimal solution, keeping also the initial thresholds as candidate values
14:
end if
15:
end if
16:
end for
17:
$epoch \leftarrow epoch + 1$
18:
end while