Trustworthy Machine Learning Operations for Predictive Maintenance Solutions

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Published Jun 27, 2024
Kiavash Fathi Tobias Kleinert Hans Wernher van de Venn

Abstract

With the ever-growing capabilities of data acquisition and computational units in industry, development, and deployment of data-driven models (e.g., predictive maintenance solutions) have become more abundant. However, when not trained and maintained properly, these models can be counterproductive as their predictions are not correct, reliable, or interpretable. In addition, unlike conventional software, the issues with such models manifest themselves in reduced productivity and not in forms of traceable software error. Therefore, in this proposal we aim to use model evaluation measures introduced in trustworthy AI operations (TrustAIOps) to trigger re-evaluation of different parts of the data pipeline and the deployed data-driven model given machine learning operations (MLOps) requirements. We argue that by creating an ecosystem capable of monitoring different aspects of a data-driven solution by integrating and managing the implementation concepts in TrustAIOps and MLOps, it is possible to boost the performance of models given the constant changes induced by the specifications of Industry 4.0.

How to Cite

Fathi, K., Kleinert, T., & van de Venn, H. W. (2024). Trustworthy Machine Learning Operations for Predictive Maintenance Solutions. PHM Society European Conference, 8(1), 4. https://doi.org/10.36001/phme.2024.v8i1.3966
Abstract 233 | PDF Downloads 141

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Keywords

Trustworthy AI, Machine learning operations, Industry 4.0, Predictive maintenance

References
Ashmore, R., Calinescu, R., & Paterson, C. (2021). Assuring the machine learning lifecycle: Desiderata, methods, and challenges. ACM Computing Surveys (CSUR), 54(5), 1–39. Fathi, K., Ristin, M., Sadurski, M., Kleinert, T., & van de Venn, H. W. (2024). Detection of novel asset failures in predictive maintenance using classifier certainty. IEEE, 32nd Mediterranean Conference on Control and Automation (MED). Fathi, K., Sadurski, M., Kleinert, T., & van de Venn, H. W. (2023). Source component shift detection classification for improved remaining useful life estimation in alarm-based predictive maintenance. IEEE, 23rd International Conference on Control, Automation and Systems (ICCAS). Fathi, K., Stramaglia, M., Ristin, M., Sadurski, M., Kleinert, T., Sch¨onfelder, R., & van de Venn, H. W. (2024). Sustainability in semiconductor production via interpretable and reliable predictions. IFAC, 12th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes. Huyen, C. (2022). Designing machine learning systems. ”O’Reilly Media, Inc.”. Li, B., Qi, P., Liu, B., Di, S., Liu, J., Pei, J., . .. Zhou, B. (2023). Trustworthy ai: From principles to practices. ACM Computing Surveys, 55(9), 1–46.
Section
Doctoral Symposium