This survey explores applications of explainable artificial intelligence in manufacturing and
industrial cyber–physical systems. As technological advancements continue to integrate …

This research addresses a critical challenge in aerospace engineering: enhancing the
interpretability of machine learning models for predictive maintenance. By integrating …

Abstract The Rashômon Effect, applied in Explainable Machine Learning, refers to the
disagreement between the explanations provided by various attribution explainers and to …

In recent years, the integration of Artificial Intelligence (AI) into Intelligent Fault Diagnosis
(IFD) through multi-source signal fusion has advanced significantly. However, the inherent …

Due to their performance in this field, Long-Short-Term Memory Neural Network (LSTM)
approaches are often used to predict the remaining useful life (RUL). However, their …

Bearings are pivotal components of rotating machines where any defects could propagate
and trigger systematic failures. Once faults are detected, accurately predicting remaining …

Deep learning approaches have recently been extensively explored for the prognostics of
industrial assets. However, they still suffer from a lack of interpretability, which hinders their …

This research addresses a critical challenge in aerospace engineering: enhancing the
interpretability of machine learning models for predictive maintenance. By integrating …