# Explainable Artificial Intelligence Approach for Improving Head-Mounted Fault Display Systems  
[PERSON]  
IMT Nord Europe, Center for Digital Systems, University of Lille, F-59000 Lille, France  
Correspondence: [EMAIL_ADDRESS]  
[PERSON]  
into the diagnostic model to predict future conditions (normal or abnormal). However, for maintenance to be genuinely effective, greater emphasis must be placed on fault management. Simply enhancing the maintenance program is insufficient for today's advanced physical systems. In this context, one approach is to equip the supervision system with a mechanism for interpreting the encountered problems. The main challenge is to detect the occurrence of an anomaly and provide a plausible cause or explanation, allowing human operators to determine the appropriate response quickly [11]. This is where explainable artificial intelligence (XAI) becomes valuable, promoting a shift towards contextual, human-centered, understandable, and reliable explanations [12]. Initially, Explainable AI (XAI) concentrated on developing methods to make black-box models more understandable, like deep neural networks. This encompasses a range of model-agnostic techniques such as Anchor [13], LIME [14], LORE [15], LUX [16], and SHAP [17]. Additionally, there are model-specific approaches that utilize gradient-based deviations from the reconstruction of input features [18] or the internal features of particular machine learning algorithms. An example is GradCam [19], specifically designed for deep neural networks. Alongside these research works, another emerging trend emphasizes the development of efficient glass-box models [20], which are inherently interpretable. Examples include explainable boosting machines [21] and, more recently, prototype deep neural networks. Regardless of the underlying techniques the XAI methods aim to explain the model's decisions by describing the relationship between the model input and output. This description can be presented in various forms [22], such as feature importance, feature impact, decision rules, prototypes, and examples. Providing these explanations builds end-user trust in the model and enhances understanding, which can be used to model debugging, data debiasing, etc., contributing to boosting the model performance. In this paper, we propose enhancing transparency, confidence, and intelligibility in decision automation systems by leveraging the interpretability of AI models. In other words, the paper introduces a methodology to design a head-mounted fault display system that combines predictive capabilities (through deep learning model integration) with anomaly explanations (focusing on model transparency and comprehensibility) to enhance predictive maintenance tasks. In essence, it presents a methodology to design an efficient system that interactively presents identified faults through an explicative Head Mounted Fault Display (xHMFD) system. This system is developed from sampled and recorded data generated by the physical system and data generated by a deep neural diagnoser model. The integrated solution will go beyond detecting the required anomaly. As such, it will also provide insight into the unusual behavior of the wind turbine for each component, as well as their criticality. This allows the maintenance manager to optimally adapt their maintenance program to the observed anomaly. However, it is important to note that over the past few decades, head-mounted display systems have been extensively utilized to monitor complex systems, demonstrating their effectiveness [23; 24]. [PERSON] et al. [25] extended the use of these systems beyond monitoring to support maintenance tasks. In their approach, videos, images, and 3D animations assist technicians during maintenance operations, enabling less specialized personnel to perform these tasks. While these applications are effective, augmented reality (AR) may not always be suitable, as it requires technicians to physically travel to wind farms, which can be both costly and time-consuming.