Advancing predictive maintenance: a deep learning approach to sensor and event-log data fusion
Abstract
Purpose
This study aims to introduce an innovative approach to predictive maintenance by integrating time-series sensor data with event logs, leveraging the synergistic potential of deep learning models. The primary goal is to enhance the accuracy of equipment failure predictions, thereby minimizing operational downtime.
Design/methodology/approach
The methodology uses a dual-model architecture, combining the patch time series transformer (PatchTST) model for analyzing time-series sensor data and bidirectional encoder representations from transformers for processing textual event log data. Two distinct fusion strategies, namely, early and late fusion, are explored to integrate these data sources effectively. The early fusion approach merges data at the initial stages of processing, while late fusion combines model outputs toward the end. This research conducts thorough experiments using real-world data from wind turbines to validate the approach.
Findings
The results demonstrate a significant improvement in fault prediction accuracy, with early fusion strategies outperforming traditional methods by 2.6% to 16.9%. Late fusion strategies, while more stable, underscore the benefit of integrating diverse data types for predictive maintenance. The study provides empirical evidence of the superiority of the fusion-based methodology over singular data source approaches.
Originality/value
This research is distinguished by its novel fusion-based approach to predictive maintenance, marking a departure from conventional single-source data analysis methods. By incorporating both time-series sensor data and textual event logs, the study unveils a comprehensive and effective strategy for fault prediction, paving the way for future advancements in the field.
Keywords
Acknowledgements
The authors would like to thank China Scholarship Council for financial support. Additionally, the authors extend the gratitude to Prof. Kean Aw for his contributions and insights that significantly enhanced the quality of this paper.
Conflicts of interest: the authors declare that they have no conflicts of interest.
Citation
Liu, Z. and Hui, J. (2024), "Advancing predictive maintenance: a deep learning approach to sensor and event-log data fusion", Sensor Review, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/SR-03-2024-0183
Publisher
:Emerald Publishing Limited
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