This paper extends previous work in predictive maintenance that implement a self-adaptive evolutionary strategy ( \(SA-ES\) ) for feature selection in multi-label anomaly detection tasks. We incorporate Shapley Additive exPlanations (SHAP), an explainable artificial intelligence (XAI) method, to evaluate the relevance and classification performance of features selected by the \(SA-ES\) , and compare their effectiveness against those with the highest absolute Shapley values. A comparative analysis is performed using three multi-label classifiers on a public predictive maintenance dataset, with evaluation conducted through \(5-fold\) cross-validation. Our findings validate the efficiency of the \(SA-ES\) in reducing feature dimensions and minimizing Hamming Loss. Additionally, we present insightful visualizations and interpretations for multi-label anomaly classification, facilitating the application of predictive maintenance in real-world industrial scenarios.