## 2 Related Work and Background
### Deep Learning  
In general, deep learning methods offer strong processing and learning on complicated data. For instance, automatic feature generation and refinement techniques, such as for complicated classification problems, may typically leverage connections in the data in order to retrieve valuable information from the data into redefined structures. Using complicated multivariate signal data for fault detection and condition monitoring is an example of utilizing these complex data structures. Overall, there has been a lot of interest in utilizing neural networks for such complicated classification problems during the previous decade. Initially, the multi-layer perceptron (MLP) was used, in which all network layers are fully linked [36]; however, because of the significant increase in calculation time, the depth of these networks is restricted. Thus, in the past years, more advanced neural network architectures were developed to accommodate for this. The creation of recurrent neural networks (RNN), such as long short-term memory (LSTM) networks, has yielded promising results since they are able to account for time dependencies and therefore can handle time series data and signals very well [37; 38]. However, because of memorizing long-term time dependencies, RNNs use vast amounts of memory (RAM). So, these models are less suited for long sequence data due to increased training times. This is especially the case for signal data from sensors, which is often sampled at a high frequency consisting of many data points. To tackle the training issue of RNNs, combined models utilizing autoencoders as feature extractors were created [39]. These models enhance computation but also increase the model's complexity and decrease its interpretability. Deep learning algorithms have been used to detect faults and monitor machine conditions many times. The MLP [40] was one of the earliest deep learning applications in fault detection and condition monitoring. Later, RNNs [37] and CNNs [4; 20; 41; 42] became more common in fault detection, where they have exhibited significant performance increases. Further, CNN approaches combined with data transformations, e.g., spectrograms, have been proposed several times [3; 43]. Ref. [44] was successful in the creation of a 1D CNN that is able to handle raw signals by integrating automated feature extraction with time series classification. These 1D CNNs can also withstand noise effectively and can be trained with small amounts of data [45]. Overview--Convolutional Neural Networks  
A convolutional neural network (CNN), in general, is a regularized MLP that specializes in processing two-dimensional inputs such as picture pixels and color channels. CNNs have previously proven to be effective in computer vision tasks including image classification and video identification [46; 47]. The main advantages of a CNN, compared with a traditional neural network, such as an MLP, is the use of local receptive fields, weight-sharing and sub-sampling. Especially, the weight-sharing significantly reduces memory requirements and therefore improves algorithmic efficiency [48]. Commonly, a convolutional layer consists of three phases. The first layer performs a number of convolutions, followed by the second phase that consists of an activation function. Afterwards, a pooling function is applied [48]. Before employing a CNN on one-dimensional data, e.g., time series, the data has to be converted using signal processing techniques into a two-dimensional representation in the time-frequency spectrum or using wavelet transforms [22; 49; 50]. For example, one-dimensional signals can be transformed in two-dimensional spectrograms, which in turn can be fed as an image to the CNN. This approach is not able to process raw signals directly, thus contradicting the advantages of employing deep learning applications over standard machine learning approaches. The one-dimensional (1D) CNN was created to tackle this challenge by integrating automated feature extraction for time series classification tasks [44].