*2.5.4 Agile fault classification with IGWO-SVM model*

For the confidence evaluation, the training sample and the test sample are constructed in the same weight. In the stage of fault classification, the tent chaotic mapping, nonlinear convergence factor and dynamic inertia weight are applied to improve GWO performances. Then, the training set will be utilized to train the SVM model. Through agile parameter optimization by IGWO, the best SVM parameter for the optimal model can be achieved.

Different from the extensively used fitness evaluated with the training set, a novel SVM classification accuracy focusing on the test set is selected to satisfy the agile diagnosis, as depicted by

$$\Gamma = \frac{1}{m} \sum\_{n=1}^{m} \left( \frac{l\_m}{l\_n} \times \mathbf{100\%} \right) \tag{12}$$

where *m* is the number of fault class in the test set, *lm* is the number of correctly identified faults in the nth test set, and *ln* is the total number of samples. The optimal parameters will be utilized to build the agile fault diagnosis model.
