XAI4Wind: A Multimodal Knowledge Graph Database for Explainable Decision Support in Operations & Maintenance of Wind Turbines
University of Hull（赫尔大学）
Joyjit Chatterjee, Nina Dethlefs
备注：
Updated version of knowledge graph resource paper - updates include additions to the Appendix on more properties in the knowledge graph, corrected typos/grammatical errors etc
Condition-based monitoring (CBM) has been widely utilised in the wind industry for monitoring operational inconsistencies and failures in turbines, with techniques ranging from signal processing and vibration analysis to artificial intelligence (AI) models using Supervisory Control & Acquisition (SCADA) data. However, existing studies do not present a concrete basis to facilitate explainable decision support in operations and maintenance (O&M), particularly for automated decision support through recommendation of appropriate maintenance action reports corresponding to failures predicted by CBM techniques. Knowledge graph databases (KGs) model a collection of domain-specific information and have played an intrinsic role for real-world decision support in domains such as healthcare and finance, but have seen very limited attention in the wind industry. We propose XAI4Wind, a multimodal knowledge graph for explainable decision support in real-world operational turbines and demonstrate through experiments several use-cases of the proposed KG towards O&M planning through interactive query and reasoning and providing novel insights using graph data science algorithms. The proposed KG combines multimodal knowledge like SCADA parameters and alarms with natural language maintenance actions, images etc. By integrating our KG with an Explainable AI model for anomaly prediction, we show that it can provide effective human-intelligible O&M strategies for predicted operational inconsistencies in various turbine sub-components. This can help instil better trust and confidence in conventionally black-box AI models. We make our KG publicly available and envisage that it can serve as the building ground for providing autonomous decision support in the wind industry.
Wind energy has emerged as a highly promising source of renewable energy in recent times. However, wind turbines regularly suffer from operational inconsistencies, leading to significant costs and challenges in operations and maintenance (O&M). Condition-based monitoring (CBM) and performance assessment/analysis of turbines are vital aspects for ensuring efficient O&M planning and cost minimisation. Data-driven decision making techniques have witnessed rapid evolution in the wind industry for such O&M tasks during the last decade, from applying signal processing methods in early 2010 to artifi…
University of Navarra - Tecnun（纳瓦拉大学特克南工程学院）；NEM Solutions（NEM解决方案公司）
A failure detection system is the first step towards predictive maintenance strategies. A popular data-driven method to detect incipient failures and anomalies is the training of normal behaviour models by applying a machine learning technique like feed-forward neural networks (FFNN) or extreme learning machines (ELM). However, the performance of any of these modelling techniques can be deteriorated by the unexpected rise of non-stationarities in the dynamic environment in which industrial assets operate. This unpredictable statistical change in the measured variable is known as concept drift.…
Predictive Maintenance for Ultrafiltration Membranes Using Explainable Similarity-Based Prognostics
Jheronimus Academy of Data Science, Eindhoven University of Technology, Eindhoven, The Netherlands（杰赫罗尼穆斯数据科学学院，埃因霍温理工大学，埃因霍温，荷兰）；School of Industrial Engineering, Eindhoven University of Technology（工业工程学院，埃因霍温理工大学）
University of Bradford（布拉德福德大学）；University of Hull（赫尔大学）；University of Sheffield（谢菲尔德大学）
The offshore wind energy is increasingly becoming an attractive source of energy due to having lower environmental impact. Effective operation and maintenance that ensures the maximum availability of the energy generation process using offshore facilities and minimal production cost are two key factors to improve the competitiveness of this energy source over other traditional sources of energy. Condition monitoring systems are widely used for health management of offshore wind farms to have improved operation and maintenance. Reliability of the wind farms are increasingly being evaluated to a…
Hybrid Autoencoder-Based Framework for Early Fault Detection in Wind Turbines
Department of Computer Science and Engineering, Alliance University（计算机科学与工程系，联盟大学）；College of Engineering and IT, University of Dubai（工程与信息技术学院，迪拜大学）；Shiv Nadar University（Shiv Nadar大学）
Predicting Wind Turbine Power Using Machine Learning and Weather Forecasts
University of Nottingham（诺丁汉大学）；University of Granada（格拉纳达大学）；DaSCI Andalusian Institute in Data Science and Computational Intelligence（安达卢西亚数据科学与计算智能研究所（DaSCI））；Intelligent Plant（智能工厂公司）；University of Leeds（利兹大学）
Halmstad University（哈尔姆斯塔德大学）；RISE Research Institutes of Sweden（瑞典RISE研究院）；INESC TEC（葡萄牙系统与计算机工程研究所（TEC））；NOS Comunicações S.A.（NOS通信股份有限公司）；University of Porto（波尔图大学）；Jagiellonian University（雅盖隆大学）
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Adani Institute of Infrastructure Engineering（阿达尼基础设施工程学院）
Wind turbine is a complex machine with its rotating and non-rotating equipment being sensitive to faults. Due to increased wear and tear, the maintenance aspect of a wind turbine is of critical importance. Unexpected failure of wind turbine components can lead to increased O\&M costs which ultimately reduces effective power capture of a wind farm. Fault detection in wind turbines is often supplemented with SCADA data available from wind farm operators in the form of time-series format with a 10-minute sample interval. Moreover, time-series analysis and data representation has become a powerful…
Prediction of wind turbines power with physics-informed neural networks and evidential uncertainty quantification
University of Granada（格拉纳达大学）；Research Centre for Information and Communication Technologies (CITIC-UGR)（信息与通信技术研究中心（CITIC-UGR））；TECNALIA（泰克纳利亚研究中心）；Imperial College London（帝国理工学院）
With an increasing emphasis on driving down the costs of Operations and Maintenance (O&M) in the Offshore Wind (OSW) sector, comes the requirement to explore new methodology and applications of Deep Learning (DL) to the domain. Condition-based monitoring (CBM) has been at the forefront of recent research developing alarm-based systems and data-driven decision making. This paper provides a brief insight into the research being conducted in this area, with a specific focus on alarm sequence modelling and the associated challenges faced in its implementation. The paper proposes a novel idea to pr…
Thermal and RGB Images Work Better Together in Wind Turbine Damage Detection
Khmelnytskyi National University（赫梅利尼茨基国立大学）；West Ukrainian National University（西乌克兰国立大学）；Kazimierz Pulaski University of Technology and Humanities in Radom（拉多姆卡齐米日·普瓦斯基科技与人文大学）
Wind energy is expected to be one of the leading ways to achieve the goals of the Paris Agreement but it in turn heavily depends on effective management of its operations and maintenance (O&M) costs. Blade failures account for one-third of all O&M costs thus making accurate detection of blade damages, especially cracks, very important for sustained operations and cost savings. Traditionally, damage inspection has been a completely manual process thus making it subjective, error-prone, and time-consuming. Hence in this work, we bring more objectivity, scalability, and repeatability in our damag…
University of Liege（列日大学）；The Pennsylvania State University（宾夕法尼亚州立大学）；Aalborg University（奥尔堡大学）
Civil and maritime engineering systems, among others, from bridges to offshore platforms and wind turbines, must be efficiently managed as they are exposed to deterioration mechanisms throughout their operational life, such as fatigue or corrosion. Identifying optimal inspection and maintenance policies demands the solution of a complex sequential decision-making problem under uncertainty, with the main objective of efficiently controlling the risk associated with structural failures. Addressing this complexity, risk-based inspection planning methodologies, supported often by dynamic Bayesian …
Explainable Fuzzy GNNs for Leak Detection in Water Distribution Networks
Jheronimus Academy of Data Science, Eindhoven University of Technology, Eindhoven, The Netherlands University of Bari Aldi Moro, Bari, Italy IHE Institute for Water Education, Delft, The Netherlands University of Cartagena, Cartagena, Spain School of Industrial Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
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