From Interoperability to Explainability: Semantic Data Models for XAI in Industrial Prognostics and Health Management
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Abstract
Semantic data models play a key role in enabling interoperability in industrial plants, and their importance has grown further with the rapid adoption of industrial artificial intelligence (AI). As AI applications become increasingly prevalent, most of them rely on data-driven approaches and exhibit a predominantly black-box nature, making their output difficult to interpret and explain. To address this limitation, this work investigates the integration of semantic data models with AI applications to enhance the interpretability of their results.
In this study, the OPC UA ISA-95 companion specification is utilized as the semantic data model, while a random forest–based AI model is applied for predictive maintenance in a manufacturing shop floor context. To support explainable AI (XAI), the results are evaluated in five dimensions: faithfulness, stability, sparsity, redundancy, and calibration. Furthermore, a comparative analysis is conducted under three scenarios: (i) using a flat tabular data set with feature engineering, (ii) incorporating the semantic model, and (iii) augmenting the semantic model with the flat tabular data set. Preliminary findings indicate that the integration of semantic data models enhances the explainability of black-box AI models.
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XAI, SHAP, PHM, Semantic Data, Industrial Edge, Siemens, Explainability, Predictive Maintenance
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