Hyperparameter Analysis of Wide-Kernel CNN Architectures in Industrial Fault Detection - An Exploratory Study

In recent years, industrial fault detection has become more data-driven due to advancements in automated data analysis using Deep Learning (DL). These techniques facilitate meaningful feature extraction, e.g., in time series data retrieved from sensors, which is typically of complex nature. This enables effective fault detection and prognostics, which increases efficiency and productivity of industrial equipment. However, the optimal settings for these DL architectures are generally use-case specific.