# Explainable Artificial Intelligence Approach for Improving Head-Mounted Fault Display Systems
## 2 Explicative Head-Mounted Fault Display System Design
### Fault Diagnoser Model Computation
#### 2.3.3 Selecting Reliable Sensor Data  
Let us recall that the methodology aims to assist maintenance managers and operators by simplifying and targeting their tasks to implement the appropriate maintenance strategy for specific issues. Then, to develop an effective maintenance decision support system using models like LSTM as the diagnoser, it should be transparent and comprehensible to generate explanations and provide specific inferences about the failure (including its severity and complexity) that one can validate against the domain knowledge. In this context, minimizing the input variables to be monitored is essential, as each variable can explain the event in multiple ways. Therefore, it is necessary to identify a possible subset of characteristic variables so that, for N features, the explanation of anomalies is the most plausible allowing the analysis of some statistical measurements associated with the components represented by each feature. This could simplify a targeted maintenance strategy. Figure 4: Diagnoser model architecture. Figure 5: Standard simplified diagram of an LSTM cell. The aim at this stage is to scrutinize the variables to identify those most relevant for accurately describing the behavior of a group of components. In practice, within a system with multiple components, each component typically has dedicated sensors monitoring its state. However, in wind energy systems with high levels of interconnections, a sensor designed to monitor one component may also provide valuable data for observing another component. Figure 6 illustrates an example of cross-correlation between a group of features and components. It can be observed that the behavior of each component is defined by a few key measurements, represented by different sensors \(M_{i}\). The objective is to select the key sensors that determine the dynamics of the global system as illustrated by the circles with red borders in the figure. Let us note that this procedure is performed offline, so the relevant features are selected ahead of time, before real-time implementation. Furthermore, the variable selection process is carried out in two stages. First, we analyze each component separately to determine the most relevant feature using a standard Particle Swarm Optimization (PSO) algorithm. In the second stage, we apply a global optimization approach that considers the interactions among various components. The approach uses simple displacement rules to iteratively refine the number of features, progressively converging towards an optimal solution. Each iteration updates the variables based on their current velocity, the best solution found so far, and the best solutions in their neighbouring. Thus, one can ensure the identification of the most critical variables for describing the overall system behavior, while also managing time complexity and limiting the number of sensors considered. Therefore, to effectively characterize the behavior of a group of components, we suggest iteratively identifying the optimal correlated features and influencing parameters of the entire system by examining the relationship between its component and feature. So, let us consider the subsequent clustering function \(g:x_{t}\to 2^{x_{t}}\), containing measures from \(m\) sensors, defined by:  
\[g(x_{t})=\{x_{t}^{m}|x_{t}^{m}\}. \tag{4}\]  
Then, we can compute the component-related clustering and sensor-origin clustering \(\eta:x_{t}\to C\times 2^{x_{t}}\) by the following relation:  
\[\eta(x_{t}^{m})=\bigcup_{s}\left[\Omega\Big{(}x_{t}^{m}\Big{)},g\Big{(}x_{t}^{ m}\Big{)}\right] \tag{5}\]  
with \(\Omega(x_{t}^{m})\) assigning the sub-vector state \(x_{t}^{m}\) to the appropriate component class such that:  
\[\arg\max\mathcal{P}(x_{t}^{m},t)=\phi\Bigg{[}\eta(x_{t}^{m}),\eta(x_{t}^{n}) \Bigg{]},\text{with }m\
eq n. \tag{6}\]  
Figure 6: Relationship example between a group of features vs. a group of components.