1-D Residual Convolutional Neural Network coupled with Data Augmentation and Regularization for the ICPHM 2023 Data Challenge

To avoid these Industrial machines are subjected to heavy stress drawbacks, we propose a residual Convolutive Neural conditions and are therefore susceptible to defects and Network (CNN) that stands out for its small number of resulting malfunctions. Even small defects can already parameters and additonally employ data augmentation have significant consequences as they can cause additional techniques to further mitigate the effect of a possible costs by, e.g., delaying industrial production over-fitting for the ICPHM 2023 Data Challenge on through unexpected downtime, or can even put human Industrial Systems' Health Monitoring using Vibration lives in danger, e.g., resulting from bearing failure in Signal Analysis [10].