Multimodal ML Strategies for Wind Turbine Condition Monitoring in Heterogeneous IoT Data Environments

Addressing the pressing need for efficient wind turbine monitoring in the sustainable energy sector, this paper begins with an extensive literature review focused on condition monitoring techniques specific to wind turbines. This foundational review uncovers significant gaps, particularly in managing diverse and voluminous data streams that are characteristic of wind turbine operations. The core objective of this research is to thoroughly analyze the unique characteristics of heterogeneous data environments in wind turbine monitoring, tackling challenges like data diversity, volume, and reliability, where their effectiveness in interpreting complex data is scrutinized. This analysis provides critical insights into the applicability of these models in practical monitoring situations. Further, the research broadens its scope to assess the implications of these findings within the Artificial Intelligence of Things (AIoT) domain. It highlights the potential of AI and IoT integration in revolutionizing wind turbine monitoring, leading to smarter, more resilient renewable energy systems. The study sets a foundation for future advancements in AIoT, especially in enhancing the efficiency and intelligence of renewable energy infrastructures. It paves the way for the development of more sophisticated AI-driven tools for energy management, envisioning a future where renewable energy systems are managed with greater efficiency and intelligence.