Industrial fault detection has become more data-driven due to advancements in automated data analysis using deep learning. Such methods make it possible to extract useful features, e. g., from time series data retrieved from sensors, which is typically of complex nature. This allows for effective fault detection and prognostics that boost the efficiency and productivity of industrial equipment. This work explores the influence of a variety of architectural hyperparameters on the performance of one-dimensional convolutional neural networks (CNN). Using a multi-method approach, this paper focuses specifically on wide-kernel CNN models for industrial fault detection, that have proven to perform well for tasks such as classifying vibration signals retrieved from sensors. By varying hyperparameters such as the kernel size, stride and number of filters, an extensive hyperparameter space search was conducted; to identify optimal settings, we collected a total of 12,960 different combinations on three datasets into a model hyperparameter dataset, with their respective performance on the underlying fault detection task. Afterwards, this dataset was explored with follow-up analysis including statistical, feature, pattern and hyperparameter impact analysis. We find that although performance varies substantially depending on hyperparameter choices, there is no single simple strategy to optimise performance across the three datasets. However, an optimal setting in terms of performance can be found in the number of filters used in the later layers of the architecture for all datasets. Furthermore, hyperparameter importance differs across and within the datasets, and we found nonlinear relationships between hyperparameter settings and performance. Our analysis highlights key considerations when applying a wide-kernel CNN architecture to new data within the field of industrial fault detection. This supports practitioners who wish to apply and train state-of-the-art convolutional learning methods to apply to similar fault detection settings, e. g., vibration data arising from new combinations of sensors and/or machinery in the context of bearing faults.
