*2.4. System Design*

In order to solve the above problems, this paper proposed an FD method based on CCA and JITL. We mainly used the CCA component to preprocess and normalize data, transform high-dimensional data into low-dimensional variable covariance subspaces and maximize the correlation between the most important top latent dimensions. SVD was applied to decompose the covariance matrix of input and output into two separate singular subspaces and keep the original distribution trends of variable correlations. Subspace mapping procedures projects input and output matrices back to the singular subspaces only with the most important relations and generates two groups of variables, Px and Py, after dimension reduction which eliminates the noise, that is, the residual subspaces. Then, JITL was used to calculate the Euclidean similarity of the query sample and the training data, respectively, and selected a sample subset for online testing regarding distances between them. During the experiments, the data sets of Px and Py were equally divided as a training data set and a testing data set. Finally, the FD model formulated statistics to define thresholds of fault signals and performed to detect signal faults in the testing samples. The workflows of the model are shown in Figure 2.

**Figure 2.** Flowchart of the proposed CCA-JITL FD model.
