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

Pseudocode 1

The pseudocode of degradation detection for historical datasets.

The degradation detection for historical datasets.

Step1. Initialization. L represents the moving detection window length, w represents the window width of ALWS, and let L = L1,w = w1.

Step2. According formula(3), calculate the SD sequence([SD₁,SD₂,...,SD_N]) of full cycles.

Step3. Calculate the ALWS sequence of full cycles.

For i in (w,N):

sd = [SD_i-w +1,SD_i-w +1,...,SD_i]

According the formula (), calculate a(i) = ALWS(sd)

End for

Get the sequence [a₁, a₂,..., a_N-w +1]. Add m-1 zero before it and get the new ALWS sequence [A₁, A₂,...,A_m-1,a₁,...a_N-w +1], where A₁ = ... = A_w-1 =0.

Step4. Using Z to represent the number of nonzeros in the detection window sequence and [T_low,T_up] represents the time interval when the obvious degradation point.

For j in (1, N-L), do:

Get the detection window sequence: [A_j,...,A_j+L]

Count the number Z of nonzero in the detection sequence: Z = is_not_zero([A_j,...,A_j+L])

If Z more than int (0.5*L)

T_low = j

T_up = j + L1

break

Else

continue

End if

End for

Step5. For each engine in the historic database, according to the above step 1-4, the degradation interval is determined, and the original historical database D0 can be divided into two datasets, D1 in the health stage and D2 in the obvious degradation stage.