## 1 Introduction  
For a durability test bench, reliability and availability are the most important factors for profitable operation. In addition to traditional maintenance, in which systems are operated until they fail or are replaced after a defined service interval, new methods have been developed through the widespread use of artificial intelligence. Predictive maintenance (PdM) can detect failures of both the test bench and the device under test (DUT) in good time, thus predicting abnormalities in the overall system or a component [1]. The question of which systems and components to monitor is inherent in the choice of PdM. While preventive maintenance (PvM) defines fixed replacement intervals for worn parts, it is difficult to prevent sporadic abnormalities. With no known damage to the test bench, it makes sense to shift the focus to monitoring the DUT. Endurance runs on a test bench are carried out until the DUT fails. To derive predictive measures, an assessment of the condition of the DUT is required. Due to the great attention being paid to PdM, significant progress is being made in terms of implementation [2] and research [3]. Current efforts aim at reducing the cost of PdM systems in industries [4] such as the automotive industry [5] and improving their accuracy through new models and algorithms. This is where the need for big data and real-time models becomes clear [6; 7]. PdM methods based on artificial intelligence (AI) are becoming increasingly prevalent and focus on boosting performance and efficiency in PdM [8]. Depending on the problem, the application area, and the available data, machine learning (ML) solves grouping, regression, and classification tasks or is used for anomaly detection. Deep learning (DL) algorithms are used for similar tasks. For optimization, an appropriate algorithm must be selected [9]. Both ML and DL are compared [10] and used particularly frequently in complex and dynamic industrial environments [11]. AI-based techniques, including ML and DL, have been shown in the automotive and manufacturing industries to enhance performance and accuracy in PdM using fault diagnosis [12]. For this, ref. [13] uses intelligent sensors in a smart factory, while [14] uses real-time data from IoT sensors. In addition to optimizing the models and algorithms, it is important to obtain correct features from the input data. Especially for ML, the extracted features play a major role in performance. Some research is therefore aimed at optimizing feature extraction [15]. Although PdM methods can be used almost anywhere, the specific application is important for choosing the right models and algorithms. As an ML method, a support vector machine (SVM) was used in [16] for the classification of a vibration signal of a passenger car. With good results, it was also by far the most frequently used method in a recent research article [17]. For unknown damage cases on an engine with little training data, ref. [18] used a one-class support vector machine (OSVM) to detect abnormalities. Early damage patterns using a vibration signal were recognized in [19; 20] with the help of deep learning (DL). In [21], DL was used to monitor the condition of an engine using 21 sensors. In contrast, using only a torque signal, a special type of recurrent neural network (RNN) known as a long-short-term memory (LSTM) network was used to identify faults in a tapping process [22]. With [23] it became clear that SVM and RNN are valid methods for the desired results on a test bench, but it remains unclear how they perform under conditions of constraints and without a controlled test group. In this work, two different AI-based PdM methods were implemented on a rear axle test bench with only limited data available. The main target was to predict damage for highly varying tests to both the test bench and the DUT using a single torque signal and a comparison of different approaches for this purpose. Due to the lack of big data, a condition-based approach was implemented to detect any abnormalities at an early stage.