**4.2. Results and discussion**

In order to evaluate the proposed feature-level fusion model, we apply it to bearing fault diagnosis. And data of this bearing fault diagnosis task are also take from a lab of the Case Western Reserve University website [3]. In this work, three experiments over three data sets are conducted as Table 11 shows. Those data are collected under various operating loads from motor driven end and fan end accelerometers.


**Table 11.** Description of three data sets

Each data set covers four different operating conditions and four different loads (0, 1, 2 and 3 hp). And each class of data sets has 160 data samples which are divided into two equal halves, one for training and the other for testing. The task of data set A is to identify different type of faults, while the experiment over data set B is carried out to further investigate the diagnosis performance of developing faults when the fusion model is trained by incipient faulty samples. And the experiment over data set C is to test the diagnosis performance of incipient faults when the fusion model is trained by the serious faulty samples.

Table 12 gives the results of these three experiments. From Table 12, we can see that the new feature-level fusion model using GEP can get stable, good diagnosis performance. And it is clear that testing performance is higher than training performance in the experiment on data set C. That is to say, when the new feature-level fusion model is trained by the serious faulty samples, it can easily identify incipient faults.


**Table 12.** Fault diagnosis performance using feature-fusion model

In order to observe the performance change when the new feature-fusion model uses multiple source information instead of single source information, the new method is used to test bearing fault diagnosis performance with single sensor source. Table 13 gives the performance comparison result between more than one sensor (here using two sensors) and single sensor. From Table 13, we can see multi-sensor testing performance is greatly higher than the single sensor application using the new feature-level fusion model.


**Table 13.** Performance comparison between multi-sensor and single sensor
