Two-Stage Degradation Assessment and Prediction Method for Aircraft Engine Based on Data Fusion

Pseudocode 2

The pseudocode of degradation detection for new observations.

The degradation detection for new observations

Step1. Initialization, if K >100, L = L1 and w = w1; else L = w = w2. And score sequence S = [ ]

Step2. Based on the health stage datasets D1, calculating the ALWS(D1) sequence, and taking the maximum value max(ALWS(D1)) as the threshold e_upper.

Step3. Calculating ALWS(X) sequence A and adding w-1 zeros before it. Getting the new sequence A: [A₁,A₂,A_w-1,A_w,...,A_K] with length K, and A₁ = A₂ = ... = A_w-1 =0.

Step4. Updating sequence A([A₁,A₂,A_w-1,A_w,...,A_K]).

For a in A, do:

If a less than or equal to e_upper:

a =0.

Else:

Continue

End if

End for

Step5. Degradation assessment for the observation.

For i = L,2,..,K, do:

Get detecting window [A_i-L +1,...,A_i]

Count the number S_i of zeros in [A_i-L +1,...,A_i]

Add new element into the score sequence S, S=S + S_i

End for

If max(S) more than int (0.5L):

This observation is in the degradation stage.

Else:

This observation is in the health stage.