ABSTRACT
This chapter deals with the concepts of using artificial intelligence (AI) for image processing (IP) to evaluate wear patterns and wear processes in real time. AI-based solutions may offer real-time feedback on equipment health, eliminate downtime, increase performance, and decrease maintenance costs, making them invaluable in a wide range of sectors where an accurate evaluation of wear patterns and mechanisms is critical. Convolutional neural networks, unsupervised machine learning, and generative adversarial networks are all examples of AI systems that can process vast datasets, detect tiny changes in wear patterns, and automatically identify wear processes. AI-based IP methods are already rather advanced, and their importance in wear analysis is only expected to grow. AI-based IP algorithms are useful for evaluating the attributes of solid particles, such as their size, shape, symmetry, and density. To plot 3D surface plots, 2D plots for roughness tests, patterns, wear processes, porosity, etc., IP methods are useful. Coated and uncoated wind turbine blades’ material surfaces and wear rates may be quantified with the use of hyperspectral imaging technologies.
