# _Separtial system innovation_
## 9 Results  
### General Statistics  
Figure 4a shows an evident prevalence of conference papers and proceedings: while this is quite typical of the computer science community, a comparison with the number of articles and conference papers shown in Figure 4b indicates that most of those conference and proceedings papers had been filtered away by our analysis, as their actual contents were using AI marginally or in a way that is not consistent with the criteria stated in the SLR (e.g., they were presenting projects in their starting phase, or they were documenting generic strategies). Figure 4a,b document also a substantial balance between open-access publications before the selection, while after the selection, the open-access publications represent 64% of the total.  
Figure 5a,b show the temporal cumulative distribution of papers before and after the selection. Starting from 2016, when the current concept of XAI was defined by DARPA [35], the distribution of selected papers seems to capture, as expected, a more recent interest due to two factors: the time needed to transfer ideas and methods from the community of  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Field** & **Description** \\ \hline Id & Unique identifier for each record \\ \hline Author & Authors of the paper \\ \hline Title & Title of the document \\ \hline Year & Year of publication \\ \hline Author Keywords & Keywords provided by the author \\ \hline Document Type & Type of the document \\ \hline Open Access & Open Access availability \\ \hline DOI & Digital Object Identifier \\ \hline \hline \end{tabular}
\end{table}
Table 1: Collected data from Scopus and WöS.  
Figure 3: PRISMA diagram of the selection process.  
computer science to the community of process engineering and the higher sensibility of the last to the degree of trust that a solution should exhibit before becoming a viable option.  
Figure 4: Distribution of document type for eligible and reviewed documents. (**a**) Eligible Documents. (**b**) Reviewed Documents.  
Figure 5: Cumulative distribution of document number for eligible and reviewed documents by the year. (**a**) Eligible Documents. (**b**) Reviewed Documents.  
Finally, a comparison was conducted between the keywords of the two sets before and after selection. The objective was to determine whether and how these keywords were used appropriately or if a trend effect influenced their usage, as well as to identify which keywords are considered more relevant by the authors of the most specialized works. The results, presented as word clouds in Figure 5(a),b, indicate a notable difference in keyword prominence.  
The eligible documents word cloud, representing the initial set of articles, shows the dominance of broad terms like Explainable AI, Artificial Intelligence and Machine Learning reflecting the general interest in XAI without necessarily focusing on its specific applications in process engineering. More specialized terms, such as system, decision making and trust are present but less prominent.  
In contrast, the second word cloud, which reflects the selected and reviewed articles, reveals a shift toward more process-engineering-specific keywords. Terms like Fault detection, diagnosis, defect, safety, and process indicate a stronger focus on industrial safety and fault management, which are critical to process engineering. Concepts such as optimization, prediction, and classification also gain prominence, demonstrating the technical emphasis of the selected works.  
This comparison shows a clear progression from a broader discussion of XAI to a more specialized focus on its practical applications in process engineering, particularly concerning system reliability and fault detection, underscoring the success and the necessity of the selection process in narrowing the literature to more relevant studies.