## 5 Conclusions
In other words, with the selected features, the streaming algorithm may detect a changing behavior of the system and create a new cluster or wrongly assign the current point to another existing cluster. Hence, future research should be dedicated to the investigation of the system behavior under different operating conditions while one or more components are subject to degradation. In particular, future works should aim to construct system-level features whose values remain in a given range when the system works under the same operating condition even if its components are degrading. A promising tool to construct the features that respect this property is represented by Genetic Programming, which can also be used to find optimal component-level features and HIs from the same set of features extracted from raw signals [86]. The second limitation is related to real-time edge computing. In the described case study, 81 features are extracted from the signal in the time domain every second. This activity requires a computation time of less than 1 sec. Therefore, the latency of the algorithm is irrelevant. However, when signals need to be transformed in the frequency or time-frequency domain to reduce the noise or extract more meaningful features for diagnostics and prognostics (i.e., data pre-processing), the computation time could notably increase. Further research should address the construction of reliable, light-weight, and fast algorithms for data denoising and pre-processing. Finally, the described case study does not consider the application of trained diagnostic and prognostic models for real-time inference under different operating conditions. Hence, further research should address the integration of existing models with streaming analysis for anomaly and novelty detection. Moreover, how a diagnostic or prognostic model, which was trained on data under a given operating condition, can be modified to make accurate predictions under other operating conditions should also be investigated to improve the proposed methodology and address the stakeholders' requirements. Methodology, F.C. and M.B. ; supervision, A.R., M.G. and F.P. ; writing--original draft preparation, F.C. ; writing--review and editing, A.R. All authors have read and agreed to the published version of the manuscript. * _[PERSON] et al. (2021)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Multi-sensor edge computing architecture for identification of failures short-circuits in wind turbine generators. _Appl. Soft Comput._**2021**, _101_, 107053. [CrossRef]
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