# Explainable Artificial Intelligence Approach for Improving Head-Mounted Fault Display Systems
## 4 Conclusions  
This paper presented a proof of concept for using explainable artificial intelligence to enhance head-mounted fault display systems. To help operators understand the importance of their tasks, we provide information on the parameters causing problems and their criticality. We introduce a novel approach to evaluate the contribution of each feature to all possible subsets of other features. Additionally, the integrated diagnoser model enables early identification and localization of anomalies. By linking identified anomalies with manually recorded problems, we provide valuable insights to the maintenance manager.  
The prediction model shows satisfactory forecasting accuracy (around 7%) across various components but can be further improved by leveraging meteorological data and refining the model architecture and hyperparameters. The system effectively detects issues before they occur and operates efficiently without GPU support. It has also proven capable of detecting most reported issues well in advance, alerting relevant components, and running efficiently on lightweight models without needing GPU support.  
Future work could explore other sequence-to-sequence architectures, such as transformers, which could enhance prediction accuracy and reduce false alarms due to their effectiveness in current state-of-the-art tasks like speech recognition and language models. Additionally, the system could evolve from a decision-aid tool to a complete decision-maker. By conducting a more in-depth analysis of the relationship between anomalies and critical faults, the model could provide prescriptions and the actions required to maintain wind farm operations rather than merely serving as an alert system.  
Figure 14: (**a**) Detected warning view in generator component. (**b**) Warning anomaly view in the windfarm. (**c**) Critical anomaly view in the windfarm.  
**Author Contributions:** Conceptualization: A.B. and L.R. Methodology: L.R. Software: A.B. and L.C. Validation: L.R. Formal analysis: A.B., L.C. and L.R. Investigation: A.B. and L.C. Data curation: A.B. and L.R. Writing--original draft preparation: A.B., L.C. and L.R. Writing--review and editing: A.B. and L.R. Project administration: L.R. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This document is the result of the research in the framework of the XPM (eXplainable Predictive Maintenance) project funded by the French National Research Agency (ANR-21-CHR4-0003) under the CHIST-ERA program (CHIST-ERA-19-NAI-012).  
**Data Availability Statement:** In this paper we use open data available on: [[https://www.edp.com/en/innovation/open-data](https://www.edp.com/en/innovation/open-data)]([https://www.edp.com/en/innovation/open-data](https://www.edp.com/en/innovation/open-data)) (accessed on 25 March 2024).  
**Conflicts of Interest:** The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.