###### Abstract  
With the developments in improved computation power and the vast amount of (automatic) data collection, industry has become more data-driven. These data-driven approaches for monitoring processes and machinery require different modeling methods focusing on automated learning and deployment. In this context, deep learning provides possibilities for industrial diagnostics to achieve improved performance and efficiency. These deep learning applications can be used to automatically extract features during training, eliminating time-consuming feature engineering and prior understanding of sophisticated (signal) processing techniques. This paper extends on previous work, introducing one-dimensional (1D) CNN architectures that utilize an adaptive wide-kernel layer to improve classification of multivariate signals, e.g., time series classification in fault detection and condition monitoring context. We used multiple prominent benchmark datasets for rolling bearing fault detection to determine the performance of the proposed wide-kernel CNN architectures in different settings. For example, distinctive experimental conditions were tested with deviating amounts of training data. We shed light on the performance of these models compared to traditional machine learning applications and explain different approaches to handle multivariate signals with deep learning. Our proposed models show promising results for classifying different fault conditions of rolling bearing elements and their respective machine condition, while using a fairly straightforward 1D CNN architecture with minimal data preprocessing. Thus, using a 1D CNN with an adaptive wide-kernel layer seems well-suited for fault detection and condition monitoring. In addition, this paper clearly indicates the high potential performance of deep learning compared to traditional machine learning, particularly in complex multivariate and multi-class classification tasks. fault detection; condition monitoring; multivariate signals; time series analysis; deep learning; industrial application +
Footnote †: This paper is a substantially adapted and extended revision of our paper “An Improved Wide-Kernel CNN for Classifying Multivariate Signals in Fault Diagnosis” published in the 2020 IEEE International Conference on Data Mining Workshops Proceedings (ICDMW), Sorrento, Italy, 17–20 November 2020.  
such approaches and provides specific enhancements for accurately distinguishing signals in fault detection and condition monitoring applications. Regarding data analysis, in particular industrial applications with heavy machinery can benefit from a better understanding of the underlying processes and the state of the equipment in order to adapt their maintenance strategies. Previous research has shown that depending on the type or size of the machine, rolling bearing element defects account for at least 40% of broken machinery in industrial applications [5; 6]. Such parts are used in rotating mechanisms of the machine. Due to frictions when the machine rotates, these bearing elements degrade over time. Traditionally, the condition of a rolling bearing element would be approximated based on historical data of breakdowns. Nowadays, the condition of a bearing element can be measured by mounting sensors on selected sections of the machine to record vibrations [7] or by measuring motor currents of the electrical engine that drives these elements [8]. Especially vibrations have proved to be effective for displaying the underlying state of bearings [9]. As one important prerequisite for an effective analysis, raw signals need to be denoised and preprocessed before analysis--using complex and time-consuming signal processing techniques to retrieve usable information [7; 10]. As a result, the general focus has been shifted towards deep learning algorithms that are able to process raw data and can automatically construct features by recognizing patterns in the input data [9].