## 1 Introduction
Common signal-based condition monitoring approaches include filtering and classification techniques. Filtering methods can be applied in the time-, frequency-, and time-frequency domains. Wavelet analysis is used often as filtering method, which is time-frequency domain-based, particularly when vibration signals are examined for condition monitoring. In [2], this technique was used for drill wear feature extraction using force signals, and in [19], for condition monitoring of rotating machinery. Gearbox condition monitoring using wavelet filtering based on vibration signals was described in [3]. In [4], wavelet filtering was used to extract features for classifying the wear level of mining tools based on the mining power. Statistical features are used for feature extraction, e.g., root mean square, skewness, kurtosis, and standard deviation. In previous publications, statistical features were also used for condition monitoring of tool wear [2; 5] or of diesel engines [13]. More than one filtering method may also be used, as described in [6], where 25 features from the time and frequency domains were classified with a machine learning classification model. A review of sensor technologies, signal processing, and decision-making strategies was provided in [7], including filtering techniques such as principal component analysis (which is also used in [20]), the fast Fourier transform (FFT), and wavelet transform. After filtering (including feature extraction), the features must be evaluated and can be used for classification. In [7], classification methods (decision-making) such as neural networks (see also [3]), fuzzy-based methods, and hierarchical algorithms were described. Fuzzy-based methods often combine fuzzy logic with other common methods, such as fuzzy neural networks (used in [4]) or fuzzy c-means (used in [2]). The c-means algorithm is a clustering technique. Clustering is an unsupervised classification method for defining and distinguishing features before applying subsequent classification approaches, as discussed in [20]. The Support Vector Machine (SVM) is another common method and its use in condition monitoring and fault diagnosis was reviewed in [21], where it was concluded that SVM is developing from machine learning toward problem-oriented domains. For example, a SVM is used in [8] for wear degree classification of slurry pumps. Another method for monitoring slurry pumps was proposed in [9], where a moving-average wear degradation index is classified. Threshold monitoring using statistical features is a common classification approach, but the difficulty of defining the threshold setting has to be solved and implemented, therefore expert knowledge is often necessary. In [22], the problem of automatic threshold calculation was discussed. Probability distributions were used in [23; 24] to separate normal and abnormal behavior. In this approach, a threshold is defined based on the individual machine- and task-/load operating conditions. An optimal threshold can be found using the receiver operating characteristic (ROC) curve, as explained in [10]. The main problem when applying condition monitoring systems based on filtering and classification methods is identifying suitable task-related features for filtering as well as for classification approaches describing the application without knowledge of the real system state. Thus, the goal is to optimize the complete chain of measurement, filtering, and classification to obtain a reliable state classification based on measurements. Machine elements/parts are often designed for given lifetime for loads expected. To achieve realistic operation conditions and to match the identical wear conditions, it is necessary to control the mechanical and thermal conditions as well as the lubrication, and thus wear test runs are used for experiments. In this contribution, different approaches are compared based on their application to the common experimental system used.