Engineering structures and infrastructure continue to be used despite approaching or having reached their design lifetime. While contact-based measurement techniques are challenging to implement at a large scale and provide information at discrete locations only, non-contact methods are more user-friendly and offer accurate, robust, and continuous spatial information to quantify the structural conditions of the targeted systems. Advancements in optical sensors and image-processing algorithms increased the applicability of image-based, non-contact techniques such as photogrammetry, infrared thermography, and laser imaging for structural health monitoring (SHM). Additionally, with the incorporation of artificial intelligence (AI) algorithms, the assessment process is expedited and made more efficient. This paper summarizes the efforts made in the last five years to leverage AI-aided, non-contact sensing techniques for applications in SHM with emphasis on image-based methods. Future directions to advance AI-aided image-based sensing techniques for SHM of engineering structures are also discussed.

The improvements in wind energy infrastructure have been a constant process throughout many decades. There are new advancements in technology that can further contribute toward the prognostics and health management (PHM) in this industry. These advancements are driven by the need to fully explore the impact of uncertainty, quality and quantity of data, physics-based machine learning (PBML), and digital twin (DT). All these aspects need to be taken into consideration to perform an effective PHM of wind energy infrastructure. To address these aspects, four research questions were formulated. What is the role of uncertainty in machine learning (ML) in diagnostics and prognostics? What is the role of data augmentation and quality of data for ML? What is the role of PBML? What is the role of the DT in diagnostics and prognostics? The methodology used was Preferred Reporting Items for Systematic Review and Meta-Analysis. A total of 143 records, from the last five years, were analyzed. Each of the four questions was answered by discussion of literature, definitions, critical aspects, benefits and challenges, the role of aspect in PHM of wind energy infrastructure systems, and conclusion.

Wind turbines suffer from mass imbalance due to manufacturing, installation, and severe climatic conditions. Condition monitoring systems are essential to reduce costs in the wind energy sector. Many attempts were made to improve the detection of faults at an early stage to plan predictive maintenance strategies, but effective methods have not yet been developed. Artificial intelligence has a huge potential in the wind turbine industry. However, several shortcomings related to the datasets still need to be overcome. Thus, the research question developed for this paper was “Can data augmentation and fusion techniques enhance the mass imbalance diagnostics methods applied to wind turbines using deep learning algorithms?” The specific aims developed were: (i) to perform sensitivity analysis on classification based on how many samples/sample frequencies are required for stabilized results; (ii) to classify the imbalance levels using Gramian angular summation field and Gramian angular difference field and compare against data fusion; and (iii) to classify the imbalance levels using data fusion for augmented data. Convolutional neural network (CNN) techniques were employed to detect rotor mass imbalance for a multiclass problem using the estimated rotor speed as an input variable. A 1.5-MW turbine model was considered and a database was built using the software turbsim, fast, and simulink. The model was tested under different wind speeds and turbulence intensities. The data augmentation and fusion techniques used along with CNN techniques showed improvement in the classification and hence the diagnostics of wind turbines.

This paper investigates the use of convolutional neural networks (CNN) to automatically detect aerodynamic imbalances in horizontal axis wind turbines (WTs). The database is assembled with low-frequency acquisition rates, similar to those obtained using supervisory control and data acquisition (SCADA) systems. The methodology considers imbalances caused by pitch errors only, which might occur due to installation faults or pitch control errors. The measured raw data is initially processed using traditional statistical techniques. Next, the Gramian Angular Field (GAF) method is used to transform the statistical data into images, and then, a CNN is trained to identify aerodynamic rotor imbalance. The proposed methodology is evaluated under numerical simulations of a 1.5 MW wind turbine, and the accuracy and feasibility of the method are demonstrated. The paper demonstrates that it is possible to detect an aerodynamic imbalance in wind turbine rotors from statistics descriptors of nacelle IMU translational accelerations and wind speeds, considering a sampling frequency of above 0.05 Hz, and using an artificial intelligence technique.