## 2 Related Work and Background
### Fault Detection and Condition Monitoring  
Within CBM, fault detection and condition monitoring are common approaches for rotating industrial equipment where faults regularly occur [18]. In the past, this was achieved using physics-based models, which require background knowledge on the underlying processes. These models hardly adapt to changing circumstances and increments in the amount of data and variables [19]. Innovations in data-driven analytics and the advances in Industrial Internet of Things (IOT) have altered the area of fault detection and condition monitoring towards a more intelligent approach [4; 20]. These methods allow for automated data processing without prior understanding of technical elements of industrial machinery, while easily adaptable to changing operation conditions. Because of the increased availability of large-scale time series datasets and better processing capacity, the usage of deep learning applications has grown in popularity. These time series are recorded by sensors, which are increasingly being used for fault detection and condition monitoring. When elements of equipment decay over time, for example, the analog metrics of the machine will not immediately reflect this; however, increased power usage (motor current), vibrations or temperature of machine elements monitored with internal and external technologies, such as sensors, might indicate that the underlying parts need to be replaced [4; 9; 10]. These signals derived from sensors can be convertedinto numerical time series data for subsequent study. However, for reliable fault detection, considerable efforts in feature extraction is typically required. Traditional methods for extracting representative features to classify signals include time-domain analysis (e.g., statistical measures such as mean and standard deviation), frequency-domain analysis (e.g., Fourier transformations [7], see Figure 1) and time-frequency domain analysis, (e.g., wavelet transformations [21; 22]). As one could expect, the quantity of features derived from the different domains results in a high-dimensional dataset. Therefore, features are picked [23] and techniques are frequently used to decrease the dimensionality of these features, such as principal component analysis (PCA) [24; 25] or linear discriminant analysis (LDA) [26]. Furthermore, [27], for example, utilized information entropy to preprocess the original time series data. Before the final dataset can be supplied to a classifier, the essential preprocessing steps typically take a substantial amount of time and high-level knowledge in signal processing and data processing--regarding standard (non-deep learning) machine learning approaches. Additionally, the feature extraction process is influenced by the type of data gathered from a particular machine or sensor, for instance, vibration sensors require different preprocessing steps than analog sensors. Within fault detection and condition monitoring, many different classifiers are researched, including k-nearest neighbors (K-NN) [28; 29], support vector machines (SVM) [21; 30; 31; 32], artificial neural networks [33; 34] and interpretable machine learning methods such as random forests (RF) [35]. The performance of these techniques varies a lot depending on the data quality, thoroughness of the feature extraction process and complexity of the classification task; therefore, it is often difficult to find the right classifier for the task at hand. In other words, there is not one particular machine learning classifier that is most capable of distinguishing different fault conditions. As a result, a comparison between classifiers is deemed necessary for every fault detection task to find the most optimal model. This work concentrates on deep learning applications, and more specifically on the use of one-dimensional CNNs in the context of fault detection utilizing time series data, i.e., multivariate signal data.