## 4 Discussion and Conclusions  
The results presented in the previous section led us to draw several considerations. First, the computational time for both training and online condition monitoring is low. In particular, in the online monitoring, the inference by the classification models is only made when anomalies are detected. Indeed, a streaming labelling of nominal data might not be necessary as, in many industrial situations, nominal data are much easier to collect. Therefore, the lack of nominal data labelling allows reducing the computational time of online monitoring, which is less than 7 s in the first scenario and less than 3 s in the second scenario. In addition, when a novel behavior is detected, the classification model can be re-trained only adding the samples corresponding to the fault conditions to the already available dataset. In this way, it is not necessary to keep in memory all nominal samples, which represents an advantage for edge applications, in which the storage memory is limited. Second, the high values of precision and recall obtained on the test set demonstrate the high generalization ability of the selected model for fault diagnosis. Indeed, the datasets used for the training and the online monitoring were different in most cases. For each condition, except for fault 2, datasets were collected on different days. For instance, D1 and D7 were both collected in the nominal condition. However, only D1 was used in the training phase. Third, results demonstrate that the classification accuracy is not affected by transients. Indeed, most datasets contain the warm-up phase of the machinery. However, thanks to the feature selection steps and, in particular, to the RFE technique that uses a classification model to identify the best feature subset, the increasing trend of some extracted features that can be seen at the beginning of each dataset (see Figure 5) does not influence the classification accuracy. Avoiding the pre-processing step on the available datasets is an essential aspect in real applications, where many transients could be present. Finally, an evident positive aspect is the minimization of false alarms. First, using kitNET for anomaly detection, the false positive rate is equal to 0%. Second, the use of a classifier after the anomaly detection has a double function. On the one hand, it allows classifying the observations in one of the known fault classes. Hence, it makes possible the diagnosis of the detected anomaly. On the other hand, it allows classifying the detected anomalies in the nominal class if the anomaly detector made an error. Another important consideration is related to the feature selection phase. In particular, two different feature subsets are selected in the two scenarios, i.e., peak, peak-to-peak, mean, and skewness in the first scenario, and peak, peak-to-peak, and skewness in the second scenario. Hence, in the hypothesis that fault 2 is unknown, i.e., the training is based only on datasets corresponding to the nominal condition, and fault 1, only three features would be selected. Nevertheless, the anomaly corresponding to the occurrence of fault 2 is detected. On the contrary, the extracted features are not able to recognize fault 3. However, as suggested by the company's technical office, fault 3 can only be seen by monitoring another kind of parameter with another type of sensor. The last consideration is related to the anomaly threshold \(Th_{a}\) and the classification threshold \(Th_{c}\), which the user in the present paper manually sets. The anomaly threshold can be considered an inherent parameter of kitNET and, as such, could be tuned through several optimization techniques. A more critical parameter is the classification threshold, as it affects the ability to detect novelties. In general, it should be higher when models are trained with few classes. However, a limitation of the proposed methodology lies in the absence of a generalizable way to set this parameter. We can conclude that the methodology is easy, fast, and accurate.