Analysis of Crack Dimensions During Crack Propagation Using Neural Network

Machine learning and artificial intelligence have emerged as the most advanced technologies in today's society for solving issues as well as assessing and forecasting occurrences. The use of AI and ML in various organizations is to capitalize on the benefits of vast amounts of data based on scientific approaches, notably machine learning, which may identify patterns of decision-making and minimize the need for human intervention. The purpose of this research is to develop a suitable neural network model, which is a component of AI and ML, to assess and forecast crack propagation in a bearing with a seeded crack. The bearing was continually run for many hours, and data were retrieved at time intervals that might be utilized to forecast crack growth. The variables RMS, crest factor, SNR, skewness, kurtosis, and Shannon entropy are collected from the continuously running bearing and utilized as input parameters, with total crack area and crack width regarded as output parameters. Finally, utilizing several methodologies of the neural network tool in MATLAB, a realistic ANN model was trained to predict the crack area and crack width.