Explainable AI based Layerwise relevance propagation algorithm on green hydrogen BERT model for the predictive maintenance

Layer-wise Relevance Propagation (LRP) is one of the  explanation algorithms for interpreting complex, non-linear machine learning classifiers comprising deep neural networks, Fisher Vector models, and structured data models by restructuring  the prediction score backward through the network's architecture. The core idea is to attribute the model’s output, layer by layer, down to contributions of input components such as image pixels, tokens, features, or neurons. Developed to ensure interpretability, LRP decomposes a prediction into local, typically signed, contributions while maintaining a (generalized) conservation property at each layer. In this paper ,it is applied on the trained BERT model to ensure the predictability and trustworthiness through the green hydrogen sample datasets