LEEANDSEO
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Dataset
Preprocessing
Split dataset
Training dataset
Test dataset
Standardization
Standardization
Second derivative
Second derivative
Moving class label
Moving class label
Time Window Processing
Time Window Processing
1NN
LOO-CV
Calculate median, standard deviation
Calculate median, standard deviation
Calculate median, standard deviation
Calculate median, standard deviation
Binary feature matrix )
Binary feature matrix )
binary
binary
Relative Distance ( )
Relative Distance ( )
Relative Distance ( )
Relative Distance ( )
Numerical feature matrix )
Numerical feature matrix )
CL-LNN
RD-LNN
Numerical value
Numerical value
Decision tree
Support Vector Machine
Trained Model
Trained Model
Input Test data
Input Test data
Early failure detection
Early failure detection
Training Model
FIGURE 1 Flow chart of the proposed method for early failure detection
system condition for each time point of measurement (ie, c t = 0 for normal, and c t = 1 for break). This sensor information is preprocessed to implement the classification algorithms. First, the entire data needs to be split into training and test dataset before data standardization is conducted for each variable since the test dataset should be unknown during the modeling. We divide the whole dataset into 90% for training and 10% for test dataset to do the experiments in Section 4 to apply the proposed algorithms in this article. Therefore, the training dataset is standardized first, and then the mean and SD from the training dataset is applied to the standardization of the test dataset. For implementing standardization, each measurement is scaled by subtracting the corresponding mean and then being divided by the SD so that the mean becomes 0 and the SD 1, as follows.
s t , j − mean ( s j ) std ( s j ) ,
j = 1 , … , p ,
(2)
s t , j ←
where the notation ← indicates that the variable in the left-hand side is replaced with the new variable of the right-hand side, mean( s j ) and std( s j ) are the mean and SD, respectively, of the original measurement data from the j th sensor. Stan- dardization is implemented to scale the data with mean 0 and SD 1 which usually gives better performance on the algorithm. The derivative then is applied to sense sudden changes in the sensor signals. The derivative in the time series is the difference between all neighboring points in one dimension. That is, s ′ t , j = | s t , j − s t − 1 , j | , s ′′ t , j = | s ′ t , j − s ′ t − 1 , j | , j = 1 , … , p , (3)
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