## 1 Introduction  
State-Of-Health evaluations and assessments of the actual wear state are important advanced features of modern condition monitoring systems [1; 2; 3; 4; 5; 6; 7; 8; 9]. Evaluations of the hazard rate/probability of failure (load-dependent) and the actual remaining lifetime are goals in the development of advanced methods and implementations [6; 10]. In [11] a new approach is developed to define the useful lifetime of components and systems using only operation data in combination with the information of the end of lifetime (of systems failed used for training of the model used for test). This approach works only for stationary operation conditions assuming stochastically wear behavior. Approaches for ensuring the functionality of systems throughout their lifetime are related to Fault Detection and Isolation and/or maintenance-related aspects, such as Condition-Based Maintenance, machine monitoring, Structural Health Monitoring, or related paradigms. These approaches have been developed during the last two decades mainly by combining signal- or model-based diagnostic approaches that detect and partially evaluate changes during operation. Most of these approaches, e.g., [1; 12; 13], aim to detect changes in systems occurring caused by operation, aging, or both, which lead to changes in measurements. The focus is given to fault detection in the sense of the detection of changes, to fault isolation meaning the establishment of detected changes to system parts or components as well as diagnosis as the method to conclude to underlying physical causes or effects. The goal of these methods is the integration of automatic supervision and maintenance planning in operating systems. Practical requirements such as the transferability to different systems are of increasing interest. Three key issues that need to be addressed are: (i) processing the data measurements, which are typically problem-specific and acquired in real-time; (ii) interpreting the measurement data in terms of the machine's condition (filtering); and (iii) extracting task- and/or application-related knowledge from measurements and data (for classification). In this contribution, different assessment approaches are introduced, which are partially combined and compared [14; 15; 16]. This survey introduces wear analysis and classification methods. For the first time in this contribution a detailed comparison considering different properties of the approaches is added. Specific effects related to wear include changes in the tribological condition of surface (denoted as surface condition in the sequel) due to the movements of surfaces with material contacts. The wear mechanisms as described in [17] are defined in this context as adhesion, abrasion, surface fatigue, and tribochemical reaction. Depending on the type of tribological load, different wear mechanisms occur. Several mechanisms exist for sliding wear, so describing the wear process may become more difficult due to various wear mechanisms occurring simultaneously. Typical phenomena include wear debris and the transfer of material attributable to cold welding (adhesion), scratches due to hard particles and cutting processes (abrasion), cracks resulting from fatigue and loss of robustness because of cyclic loading (surface fatigue), and existing reaction particles generated by chemical reactions between the materials and lubricants (tribochemical reaction). To measure wear effects directly, geometric dimensions such as changes in the length, surface, or volume are often considered, which are described in [18] for measuring the wear of brake friction materials as an illustrative example. The aim is to find indirect characteristics for describing the wear process. Another problem is related to the dependency of the wear process on different properties and interactions with the material used. Several signal-based methods may be used to identify connections between variables measured during the process and the wear process itself.