XAI4Wind: A Multimodal Knowledge Graph Database for Explainable Decision
  Support in Operations & Maintenance of Wind Turbines
  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.
