Explainable Artificial Intelligence Approach for Improving Head-Mounted Fault Display Systems

Abdelaziz Bouzidi, Lala Rajaoarisoa () and Luka Claeys
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Abdelaziz Bouzidi: IMT Nord Europe, Center for Digital Systems, University of Lille, F-59000 Lille, France
Lala Rajaoarisoa: IMT Nord Europe, Center for Digital Systems, University of Lille, F-59000 Lille, France
Luka Claeys: IMT Nord Europe, Center for Digital Systems, University of Lille, F-59000 Lille, France

Future Internet, 2024, vol. 16, issue 8, 1-20

Abstract: To fully harness the potential of wind turbine systems and meet high power demands while maintaining top-notch power quality, wind farm managers run their systems 24 h a day/7 days a week. However, due to the system’s large size and the complex interactions of its many components operating at high power, frequent critical failures occur. As a result, it has become increasingly important to implement predictive maintenance to ensure the continued performance of these systems. This paper introduces an innovative approach to developing a head-mounted fault display system that integrates predictive capabilities, including deep learning long short-term memory neural networks model integration, with anomaly explanations for efficient predictive maintenance tasks. Then, a 3D virtual model, created from sampled and recorded data coupled with the deep neural diagnoser model, is designed. To generate a transparent and understandable explanation of the anomaly, we propose a novel methodology to identify a possible subset of characteristic variables for accurately describing the behavior of a group of components. Depending on the presence and risk level of an anomaly, the parameter concerned is displayed in a piece of specific information. The system then provides human operators with quick, accurate insights into anomalies and their potential causes, enabling them to take appropriate action. By applying this methodology to a wind farm dataset provided by Energias De Portugal, we aim to support maintenance managers in making informed decisions about inspection, replacement, and repair tasks.

Keywords: failure explanation; failure detection; predictive maintenance; virtual reality; head-mounted system; wind turbines (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
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