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Explainable fault prediction using learning fuzzy cognitive maps

Mansouri, Taha; Vadera, Sunil

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Abstract

IoT sensors capture different aspects of the environment and generate high throughput data streams. Besides capturing these data streams and reporting the monitoring information, there is significant potential for adopting deep learning to identify valuable insights for predictive preventive maintenance. One specific class of applications involves using Long Short-Term Memory Networks (LSTMs) to predict faults happening in the near future. However, despite their remarkable performance, LSTMs can be very opaque. This paper deals with this issue by applying Learning Fuzzy Cognitive Maps (LFCMs) for developing simplified auxiliary models that can provide greater transparency. An LSTM model for predicting faults of industrial bearings based on readings from vibration sensors is developed to evaluate the idea. An LFCM is then used to imitate the performance of the baseline LSTM model. Through static and dynamic analyses, we demonstrate that LFCM can highlight (i) which members in a sequence of readings contribute to the prediction result and (ii) which values could be controlled to prevent possible faults. Moreover, we compare LFCM with state-of-the-art methods reported in the literature, including decision trees and SHAP values. The experiments show that LFCM offers some advantages over these methods. Moreover, LFCM, by conducting a what-if analysis, could provide more information about the black-box model. To the best of our knowledge, this is the first time LFCMs have been used to simplify a deep learning model to offer greater explainability.

Citation

Mansouri, T., & Vadera, S. (2023). Explainable fault prediction using learning fuzzy cognitive maps. Expert Systems, 40(8), https://doi.org/10.1111/exsy.13316

Journal Article Type Article
Acceptance Date Apr 14, 2023
Online Publication Date Apr 26, 2023
Publication Date Apr 26, 2023
Deposit Date May 3, 2023
Publicly Available Date May 3, 2023
Journal Expert Systems
Print ISSN 0266-4720
Electronic ISSN 1468-0394
Publisher Wiley
Volume 40
Issue 8
DOI https://doi.org/10.1111/exsy.13316
Keywords Artificial Intelligence, Computational Theory and Mathematics, Theoretical Computer Science, Control and Systems Engineering
Publisher URL https://doi.org/10.1111/exsy.13316

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