## 4 Experimental Assessment
### DL-Based Diagnostic Assessments
Figure 9: Training history for (**a**) \(CNN64\), (**b**) \(CNN64\_64\), (**c**) \(CNN64\_Dense100\), (**d**) FCN, (**e**) DNN64, (**f**) DNN128\(\_64\), and (**g**) DNN100\(\_\)150\(\_\)50. For each subfigure, the eight images respectively represent the training histories for the eight different input/source data channels in the order: \(SP_{R}\), \(SP_{C}\), \(FR_{R}\), \(SP_{C}\), \(SP_{F}RR_{R}\), \(SP_{F}\), \(SP_{R}\), \(SP_{C}\), \(FR_{R}\). Overall, the respective test accuracies of the models on the different input/source data channels are quite high; especially for the FCN whose architecture is the deepest of them all. A deeper analysis of the findings in Table 2 reveals a 4.6% and 7.5% improved diagnostic accuracy by the 1D-CNN and MLP models, respectively, between the use of the raw vibration signals and their critical segments. On the downside, the poor predictive accuracy of the more shallow models is shown by the DNN64's low test accuracies (less than 88%) on all the input/source data channels. Amidst the predictive accuracies of the models across the rows, the positive impact of the proposed spectral isolation technique can be observed in the higher test accuracy between the use of raw signals and their critical segment counterparts; for instance, between \(SP_{R}\) and \(SP_{C}\), \(FR_{R}\) and \(FR_{C}\), and \(SP_{FR_{C}}\) and \(SP_{FR_{C}}\). Combining vibration measurements from the spindle can occur in four possible ways, which include: \(SP_{-}FRR\), \(SP_{-}FR_{C}\), \(SP_{-}FR_{C}\), and \(SP_{C-}FR_{R}\). These different combinations produce different diagnostic accuracies, as shown in Table 2. However, a significant observation is that for the respective DL models, using the critical segments from both vibration sources (frame and spindle--\(SP_{-}FR_{R}\)) produces better diagnostic accuracies in comparison with the other spindle-frame combinations--\(SP_{-}FR_{R}\), \(SP_{-}FR_{C}\), and \(SP_{C-}FR_{R}\). Nonetheless, the test accuracies from each Of the DL models were highest when only spindle vibrations were used (raw and critical segments). Interestingly, between the 1D-CNN and MLP variants presented, it can be observed that the 1D-CNN has superior classification efficiencies even with a shallow architecture, such as the \(CNN64\). This is attributed to the filters that scan through the signals for discriminative features, and with the \(Global\)\(AveragePooling1D\) integrated in their architecture, the 1D-CNN models are better able to prioritize the maximum value for feature values of a feature matrix and uses it to create a down-sampled (pooled) feature matrix. This strongly provides valid support for choosing the 1D-CNN models over the MLP models. Although the test accuracy provides a global perspective for performance evaluation of the models, it may be more important to assess their class-specific (the different health stages of the wheels) performance to better ascertain their probability for false/positive predictions. Generally, the confusion matrix provides a reliable avenue for such assessments and was employed. For each of the input/source data channels, the confusion matrix of the models was computed, and, overall, there were 56 different outputs, most of which returned a perfect prediction for the different classes, while a few imperfect predictions were also returned. Because it would be futile to present all the 56 matrices in this study (in addition to the numerous similarities in the models' outputs), we have provided, in Figure 10, each model's best and worst confusion matrix outputs and their corresponding inferences that can be drawn from them.