Stay Tuned! Analysing Hyperparameters of a Wide-Kernel Architecture for Industrial Faults

The performance of a deep learning model depends heavily on its architectural hyperparameters. However, there is often little guidance on how to tune those hyperparameters. This paper provides insights into how to tune the architectural hyperparameters of a wide-kernel convolutional model for industrial fault detection, by analysing a grid search over 12,960 possible combinations of hyperparameter settings on seven benchmark datasets of vibration time series. By aggregating the results on these seven datasets, we are able to generalise across multiple industrial fault detection settings. We find that, generally speaking, the number of filters in the later convolutional layers and the hyperparameters associated with the first layer are the most important. Additionally, we analyse the relationships between hyperparameters and develop this analysis into a ‘recommended sequence’ for how to tune them one-at-a-time.