**4. Discussions**

1. Comparing the diagnosis results of the proposed hybrid intelligent method with those of individual classifiers, we find that the testing accuracies of the hybrid intelligent method (100% for data set A, 90.83% for data set B and 91.33% for data set C in case 1; 100% in case 2) increase by 0, 18.61%, 17.72% and 16.61% compared with the average accuracies of the six individual classifiers. In addition, although the highest classification accuracy (100%) is obtained by all the classifiers for data set A in case 1, the classification error of the hybrid intelligent method is least among all classifiers. Thus, the proposed hybrid intelligent method is superior to the individual classifiers in the light of the classification accuracies.

2. All the above comparisons prove that the proposed hybrid intelligent method obtains significant improvements in fault diagnosis accuracy compared to the individual classifiers. It reliably recognizes both incipient faults and compound faults of rolling element bearings. The success obtained by the hybrid intelligent method may be attributed to the following three points. 1) Extracting both time- and frequency-domain features better reflects the machinery health conditions. 2) Selecting the sensitive features reflecting the fault characteristics avoids interference of other fault-unrelated features. 3) Combining multiple intelligent classifiers based on fuzzy inference system raises diagnosis accuracy.

3. The problems studied in this chapter cover single fault diagnosis, incipient fault diagnosis and compound fault diagnosis, and therefore they are typical cases of machinery fault diagnosis. The satisfactory experiment results demonstrate the effectiveness and generalization ability of the hybrid intelligent method. Although the proposed method is applied to fault diagnosis of the rolling element bearings successfully, it may also be employed to fault diagnosis of other rotating machinery.
