Showing papers by "Stephen Ekwaro-Osire published in 2022"

Prognostics and Health Management of Wind Energy Infrastructure Systems

Abstract: The improvements in wind energy infrastructure have been a constant process throughout many decades. There are new advancements in technology that can further contribute towards 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 (PRISMA). 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.

Fabrication, Mechanics, and Reliability Analysis for Three-Dimensional Printed Lattice Designs

Abstract: The use of three-dimensional (3D) printing for lattice structures has led to advances in diverse applications benefitting from mechanically efficient designs. Three-dimensional printed lattices are often used to carry loads, however, printing defects and inconsistencies potentially hinder performance. Here, we investigate the design, fabrication, mechanics, and reliability of lattices with repeating cubic unit cells using probabilistic analysis. Lattices were designed with 500 μm diameter beams and unit cell lengths from 0.8 mm to 1.6 mm. Designs were printed with stereolithography and had average beam diameters from 509 μm to 622 μm, thereby demonstrating a deviation from design intentions. Mechanical experiments were conducted and demonstrated an exponential increase in yield stress for lattice relative density that facilitated probabilistic failure analysis. Sensitivity analysis demonstrated lattice mechanics were most sensitive to fluctuations for beam diameter (74%) and second to lattice yield stress (8%) for lattices with 1.6 mm unit cells, while lattices with smaller 1.0 mm unit cells were most sensitive to yield stress (48%) and second to beam diameter (43%). The methodological framework is generalizable to further 3D printed lattice systems, and findings provide new insights linking design, fabrication, mechanics, and reliability for improved system design that is crucial for engineers to consider as 3D printing becomes more widely adopted.

Mass Imbalance Diagnostics in Wind Turbines Using Deep Learning With Data Augmentation

Abstract: Abstract 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.

Wind Turbine Rotor aerodynamic imbalance detection using CNN

Abstract: 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.