From Interoperability to Explainability: Semantic Data Models for XAI in Industrial Prognostics and Health Management
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
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.
How to Cite
##plugins.themes.bootstrap3.article.details##
XAI, SHAP, PHM, Semantic Data, Industrial Edge, Siemens, Explainability, Predictive Maintenance
M., & Kim, B. (2018). Sanity checks for saliency
maps. In Advances in neural information processing
systems (neurips) (Vol. 31).
Alvarez-Melis, D., & Jaakkola, T. S. (2018). On the robustness
of interpretability methods. arXiv preprint
arXiv:1806.08049.
Bhatt, U., Weller, A., & Moura, J. M. F. (2020). Evaluating
and aggregating feature-based model explanations. In
Proceedings of the 29th international joint conference
on artificial intelligence (ijcai).
Elgendy, S. (2018). Pump sensor data — timeseries
analysis. Kaggle dataset and analysis
notebook. Retrieved from https://
www.kaggle.com/code/shawkyelgendy/
pump-sensor-data-timeseriesanalysis
(Accessed 2026-06-13)
Guo, C., Pleiss, G., Sun, Y., & Weinberger, K. Q. (2017).
On calibration of modern neural networks. In Proceedings
of the 34th international conference on machine
learning (icml) (pp. 1321–1330).
Hedstr¨om, A., Weber, L., Krakowczyk, D., Bareeva, D.,
Motzkus, F., Samek, W., . . . H¨ohne, M. M.-C. (2023).
Quantus: An explainable AI toolkit for responsible
evaluation of neural network explanations and beyond.
Journal of Machine Learning Research, 24, 1–11.
Hooker, S., Erhan, D., Kindermans, P.-J., & Kim, B. (2019).
A benchmark for interpretability methods in deep neural
networks. In Advances in neural information processing
systems (neurips) (Vol. 32).
International Electrotechnical Commission. (2013). IEC
62264: Enterprise-Control System Integration.
International Electrotechnical Commission. (2020). IEC
62541: OPC Unified Architecture.
Lei, Y., Li, N., Guo, L., Li, N., Yan, T., & Lin, J. (2018). Machinery
health prognostics: A systematic review from
data acquisition to RUL prediction. Mechanical Systems
and Signal Processing, 104, 799–834.
Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin,
J. M., Nair, B., . . . Lee, S.-I. (2020). From local explanations
to global understanding with explainable AI
for trees. Nature Machine Intelligence, 2(1), 56–67.
Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to
interpreting model predictions. In Advances in neural
information processing systems (neurips) (Vol. 30, pp.
4765–4774).
Mazzetti, M., et al. (2020). OPC UA and Industry 4.0: Enabling
interoperability for IIoT data integration. IEEE
Industrial Electronics Magazine.
Nor, A. K. M., Pedapati, S. R., Muhammad, M., & Leiva, V.
(2021). Overview of explainable artificial intelligence
for prognostic and health management of industrial assets
based on preferred reporting items for systematic
reviews and meta-analyses. Sensors, 21(23), 8020.
OPC Foundation. (2022). OPC 10030: ISA-95 Common Object
Model Companion Specification, v4.2. Retrieved
from https://reference.opcfoundation
.org/specs/OPC-10030/4.2
Petsiuk, V., Das, A., & Saenko, K. (2018). RISE: Randomized
input sampling for explanation of black-box models.
In Proceedings of the british machine vision conference
(bmvc).
Profanter, S., Tekat, A., Dorofeev, K., Rickert, M., & Knoll,
A. (2019). OPC UA versus ROS, DDS, and MQTT:
Performance evaluation of Industry 4.0 protocols. In
Proceedings of the ieee international conference on industrial
technology (icit) (pp. 955–962).
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). “Why
Should I Trust You?”: Explaining the Predictions of
Any Classifier. In Proceedings of the 22nd acm sigkdd
international conference on knowledge discovery and
data mining (pp. 1135–1144).
Ribeiro, M. T., Singh, S., & Guestrin, C. (2018). Anchors:
High-precision model-agnostic explanations. In Proceedings
of the aaai conference on artificial intelligence
(pp. 1527–1535).
Samek, W., Binder, A., Montavon, G., Lapuschkin, S., &
M¨uller, K.-R. (2017). Evaluating the visualization
of what a deep neural network has learned. IEEE
Transactions on Neural Networks and Learning Systems,
28(11), 2660–2673.
Schleipen, M., Gilani, S.-S., Bischoff, T., & Pfrommer, J.
(2016). OPC UA & Industrie 4.0: Enabling technology
with high diversity and variability. Procedia CIRP, 57,
315–320.
Serradilla, O., Zugasti, E., Rodriguez, J., & Zurutuza,
U. (2022). Deep learning models for predictive
maintenance: A survey, comparison, challenges and
prospects. Applied Intelligence, 52, 10934–10964.
Siemens AG. (2024). IIH Semantics: Semantic data modelling
on the industrial edge. Product documentation.
Siemens AG. (2025). SIMATIC Industrial Information Hub (IIH). SIMATIC Industrial Edge product
page. Retrieved from https://www.siemens
.com/en-us/products/simatic-apps/
industrial-information-hub/
Slack, D., Hilgard, S., Jia, E., Singh, S., & Lakkaraju, H.
(2020). Fooling LIME and SHAP: Adversarial attacks
on post-hoc explanation methods. In Proceedings of the
aaai/acm conference on ai, ethics, and society (aies)
(pp. 180–186).
Todkar, A., Sarkar, M., & Solanki, J. (2025, October). Semantic
framework for IT-OT integration in industrial
environments. In Annual conference of the phm society
(Vol. 17). doi: 10.36001/phmconf.2025.v17i1.4551
Todkar, A., Sarkar, M., Solanki, J., & Tylka, J. (2025). Approaches
to automatic discovery and modeling of industrial
assets for IT/OT integration. In Proceedings of
the 2025 ieee 21st international conference on automation
science and engineering (case) (pp. 2341–2346).
doi: 10.1109/CASE58245.2025.11164146
Vollert, S., Atzmueller, M., & Theissler, A. (2021). Interpretable
machine learning: A brief survey from the predictive
maintenance perspective. In Proceedings of the
ieee international conference on emerging technologies
and factory automation (etfa).
Yeh, C.-K., Hsieh, C.-Y., Suggala, A., Inouye, D. I., &
Ravikumar, P. K. (2019). On the (in)fidelity and sensitivity
of explanations. In Advances in neural information
processing systems (neurips) (Vol. 32).
Zhang, W., Yang, D., & Wang, H. (2019). Data-driven methods
for predictive maintenance of industrial equipment:
A survey. IEEE Systems Journal, 13(3), 2213–2227.
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.