*3.4. HI Construction*

To intuitively evaluate the reliability of the detection results, a new HI construction method is also proposed based on the DTDA model. This method is simple and effective. Specifically, through feeding sequentially the online data batch into *Gy*(·), not only the detection results, but also the temporal features of the target bearing can be obtained. After detecting all the online data, the features of the whole degradation sequence can be obtained. Then, PCA is run to get the first principal component. After the smoothing operation, the obtained feature sequence is the HI of the target bearing. Since the feature extractor in the DTDA model can extract domain-invariant feature representation with strong discriminative ability, the HI constructed based on such a feature representation can be more sensitive to reflect various state changes of the target bearing. Certainly, the obtained HI can also verify the reliability of the online detection results.

#### **4. Experimental Results**

To verify the effectiveness of the proposed method, a set of comparative experiments is run on the IEEE PHM Challenge 2012 bearing dataset [34] in this section. The programming environment was Python 3.6 and MATLAB R2014. The experiments used the Windows operating system (OS) with an i5-7300 processor and 8 G memory.
