## 3 Methods, Application, and Result Discussion
### Proposed Approach
The proposed work delves deeper into details than previous works, yielding outstanding results despite the increased complexity. This is why a numerical comparison between the results obtained in this paper and in previous ones cannot be performed and would not be fair, as our work and previous works follow different approaches and reach different objectives and conclusions. ## 4 Conclusions

This work supports the hypothesis that diagnosis and prognosis using data-driven methods in highly dynamic systems, in particular safety-critical systems, must take into account three main challenges: data drift, complexity, and unavailability. This work, therefore, addresses this hypothesis by introducing a learning model called CSIDTL that involves adaptive learning, deep learning, and transfer learning to address these challenges, respectively. The model is constructed to involve incremental selective learning and collaborative transfer learning in different learning rounds to address these challenges in a regressive manner. Compared with traditional deep learning models such as LSTM and, in general, compared with previous work that mainly tackles data complexity issues, the model achieves better results, whose stability is demonstrated using cross-validation. This confirms, to a large extent, the need for such a mix of learning tools and domains to perform such a complex process. In terms of future prospects and opportunities and concerning feature engineering, further algorithms and tools for detecting and removing outliers/noise should be discussed to further enhance data quality, and suggesting tools for assessing such quality instead of data visualization would be a great advantage. Further, from the perspective of learning systems, this work uses a single-layer LSTM model to realize the CSIDTL process, so the introduction of a deeper architecture and adaptive learning variants, such as bidirectional LSTM and gated recurrent units, with a different learning philosophy such as ensemble learning, in addition to additional generative modeling tools, could enable the three data-related challenges to be tackled at a higher level. Not applicable. All the materials required to reproduce the findings of this study are available at [[https://zenodo.org/record/8385839](https://zenodo.org/record/8385839)]([https://zenodo.org/record/8385839](https://zenodo.org/record/8385839)) (accessed on 27 August 2023). The authors would like to thank the Dynamic Research and Identification Group (DIRG) of Politecnico di Torino and the authors of the introductory article ([PERSON]; [PERSON]; [PERSON]; [PERSON]) for making their dataset publicly available, which proved very important and useful for conducting the proposed CSIDTL experiments. The authors declare no conflict of interest. ## References

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