## 3 Data Processing
### Classifier  
CNNs are used in many applications, which can be summarized mainly into three categories [15]. The first category is classification. Examples for classification are mainly medical image analysis and real-time target tracking. CNNs are also used for object detection, which is the second category. Object detection includes object localization and object classification. It is often used in medical context, for example, in radiology with X-ray images to detect fractured bones or in the case of tumor detection [16]. Lastly, the third main application is segmentation. In addition, CNNs can be used as a preprocessing step for feature extraction [17]. CNNs provide many advantages. They offer to extract features from the raw data with no need for preprocessing steps [17]. Furthermore, they are precise and result in high accuracy [18]. They have a high ability to work with variable or complex data, outperform other intelligent methods, are computationally efficient and only need few parameters [19]. Therefore, the application to noisy acoustic signals in the port environment is promising for the realization of a reliable classifier. Generally, a CNN is a multi-layer neural network and used for image data [17]. CNNs use discrete convolution to extract information from the image data. The general structure of CNNs is presented shortly. CNNs basically consist of an input layer, hidden layers and an output layer. The hidden layers are convolutional, pooling or fully connected layers. First, the images are fed to the network using the input layer. Afterwards, a combination of multiple convolutional and pooling layers is used to extract features. One convolutional layer is built by a set of multiple kernels [15]. Each kernel has weights, which are windowwise convolved with the input array. This creates features and also reduces the data size [20]. The key parameters are the size and the number of kernels [16], whereas pooling layers are only used to reduce the feature size, while retaining the relevant information. They reduce the dimensionalities and downscale the matrix. Pooling layers, in addition, improve the net transferability to unknown data [18]. After multiple convolutional and pooling layers, the two-dimensional feature space is flattened to one dimensionality. This is performed through a fully connected layer and is required for the classification. At least one layer is necessary but multiple are possible. The last layer has the same size as the defined classes. In each fully connected layer, each input is connected and weighted to the next output. Lastly, a function of activation is needed, which normalizes the output to class probabilities. CNNs have successfully been used in bearing condition monitoring [2]. [PERSON] et al., for example, used an adaptive deep CNN to differ between four bearing health states [19]. [PERSON] et al. used adaptive overlapping convolutional neural networks to increase the performance by using the one-dimensional raw vibration signal [21]. This method reached high accuracy for a public bearing dataset with ten health conditions, while using only 5 % of the data for training. In addition, the results were verified with a second dataset of a different bearing investigation. For the presented application, a CNN is developed with tensorflow and tested for differentiating into the presented bearing damage cases. The architecture is shown in Figure 3. In addition to the layer configuration, the two-dimensional kernel size of each layer is provided. Also, the number of features is shown below each convolutional layer. Figure 2: Spectrograms of three damage classes for 2700 rpm, which are used as input for the convolutional neural network (CNN). The first convolutional layer restructures the three-dimensional input, which is comprised of the RGB values of the spectrograms. The layer is combined with a relu activation layer and a max pooling layer.