## 1 Introduction  
In the present application, the tapered roller bearings are installed in a two-stage planetary gearbox in port vehicles. Since the bearings are exposed to a highly stressful environment, the bearings can evolve the wearing phenomena. Particularly in the port application, unpredictable machine failures must be avoided, as this results in high costs. Therefore, the condition monitoring of the bearings is necessary to detect damage at an early stage. To implement this, acoustic signals are evaluated in this contribution. There is already a large number of publications regarding the condition monitoring of bearings. The most important results are introduced briefly. In general, two diverging approaches can be distinguished, namely signal processing methods and intelligent methods [1, 2]. In general, the signals in the time domain, frequency domain or time-frequency domain can be used for signal-processing methods. Typical methods to analyze the signals are statistical indicators, the detection of characteristic fault frequencies for each component or occurring sidebands and spectrum observations [3, 4]. In addition to common methods like Fourier transformation, multiple decomposition methods like empirical mode decomposition or variational mode decomposition, have been developed [5]. The advantage of these methods compared to Fourier transformation is that they are applicable to non-stationary signals, which are frequently occurring in gearboxes. Therefore, the vibration signals are decomposed into mono-component modes. A new method for detecting bearing damage in this field is feature mode decomposition (FMD) [6]. Based on the use of a finite-impulse response filter, the signals are preprocessed. Afterwards, a mode selection based on a combined argument of correlated kurtosis and the correlation coefficient is performed. FMD can successfully detect individual damages and, in contrast to previous decomposition methods, is also capable of extracting the modes in the case of combined damages. The database of all presented signal processing methods can be used as an input for a variant of intelligent methods, like support vector machines or neural networks [7]. Based on the literature, the presented methods are not applicable to every use case. Most of the publications concern laboratory investigations on bearings. Investigations of bearings integrated in a gearbox are rare. Planetary gearboxes are used in rough and variable conditions. Therefore, the evaluation of fault frequencies based on the fast Fourier transformation is not possible since it is designed for stationary signals [8]. Furthermore, planetary gearboxes, and, therefore, bearings in planetary gearboxes, show additional challenges like the superposition of vibration events, nonlinear transmission paths, low-speed components and noise. Also, most investigations are based on localized faults, like local pittings. Investigations on distributed wearing phenomena are rare. This paper presents an investigation of localized and distributed defects. The investigations on the tapered roller bearings are being developed in the compound system of the two-stage planetary gearbox. In addition, this paper investigates the influence of unknown damage and is not limited to the detection of damage classes which the network has already used in the training process. For this, a convolutional neural network is developed, which takes spectral data as input. The first investigation addresses the class granularity. It is analyzed how many classes the classifier can reliably differentiate. Therefore, a binary classifier differentiating between two classes, healthy and damaged, is first developed. Afterwards, a classifier differentiating between three classes, healthy, localized and distributed damage, is used. Finally, a classifier differentiating between seven classes, which show different severities of the presented subclasses, is designed and evaluated. The second investigation addresses the impact of different speed rotations.