Modern wind turbines operate in continuously transient conditions, with varying speed, torque, and power based on the stochastic nature of the wind resource. This variability affects not only the operational performance of the wind power system, but can also affect its integrity under service conditions. Condition monitoring continues to play an important role in achieving reliable and economic operation of wind turbines. This paper reviews the current advances in wind turbine condition monitoring, ranging from conventional condition monitoring and signal processing tools to machine-learning-based condition monitoring and usage of big data mining for predictive maintenance. A systematic review is presented of signal-based and data-driven modeling methodologies using intelligent and machine learning approaches, with the view to providing a critical evaluation of the recent developments in this area, and their applications in diagnosis, prognosis, health assessment, and predictive maintenance of wind turbines and farms.

Wind power is playing an increasingly significant role in daily life. However, wind farms are usually far away from cities especially for offshore wind farms, which brought inconvenience for maintenance. Two conventional maintenance strategies, namely corrective maintenance and preventive maintenance, cannot provide condition‐based maintenance to identify potential anomalies and predicts turbines' future operation trend. In this study, a model based data‐driven condition monitoring method is proposed for fault detection of the wind turbines (WTs) with SCADA data acquired from an operational wind farm. Due to the nature of the alarm signals, the alarm data can be used as an intermedium to link the normal data and fault data. First, KPCA is employed to select principal components (PCs) to retain the dominant information from the original dataset to reduce the computation load for further modelling. Then the selected PCs are processed for normal‐abnormal condition classification to extract those abnormal condition data that are classified further into false alarms and true alarms related to the faults. This two‐stage classification approach is implemented based on the KSVM algorithm. The results demonstrate that the two‐stage fault detection method can identify the normal, alarm and fault conditions of the WTs accurately and effectively.

In the continuous innovation of modern technology concept, remote sensing technology as an advanced and practical comprehensive detection technology has been widely used in many fields. Especially for environmental monitoring, the rational use of remote sensing image data analysis and processing platform can not only obtain valuable environmental information, but also provide effective management decisions for climate changeable natural disasters and other issues. Therefore, on the basis of understanding the design scheme of remote sensing image data analysis and processing platform system, this paper makes clear the positive role of remote sensing image processing technology in the development of environmental monitoring based on the application of the platform.