## 5 Summary and Conclusions  
Supervision, monitoring, and diagnosis of technical systems are essential features of SHM-systems and assumed as key components of new automation systems (for production machines and equipment). For practical realization often approaches easy to implement but also providing qualitative or quantitative meaningful statements are required. Applied online realizable approaches are often related to threshold monitoring or to the detection of features representing damages, etc. This contribution presents four approaches providing continuous information about the system status based on measurements. In addition, the problem of missing objective (or analytical) statements about the end of useful lifetime is not solved (due to missing theory), but new ideas for continuous evaluation of the health status are provided, which can be easily adapt to arbitrary given systems for which individual thresholds have to be defined. In this contribution, we propose four new methods for signal-based data evaluation to be applied to health monitoring. Designed properties are compared with respect to monitoring wear state. The first method (AE analysis) uses specific wear characteristics based on AE signals using piezoelectric ceramics. The wear phases can be distinguished using STFT and the accumulation of the emitted acoustic energy. This deep insight into the physical nature of the AE facilitates a complex diagnosis. The second method (diagnosis-based data filtering) is used to generate characteristic values (the arithmetic mean from the hydraulic pressure) to evaluate the surface state. The accumulated \begin{table}
\begin{tabular}{|p{85.4 pt}|p{85.4 pt}|p{85.4 pt}|p{85.4 pt}|p{85.4 pt}|} \hline
**Method** & **Acoustic Emission analysis (approach A)** & **Diagnosis-oriented data filtering** & **Condition evaluation based on a classification analysis analysis approach (approach B)** & **Data-based classification approach** \\ \hline
**Error for sensor application** & - & - & - & - \\ \hline
**Error for filtering** & + & 0 & 0 & 0 \\ \hline
**Cuts and complexity of technical equipment** & + & - & - & - \\ \hline
**Real-time capability** & - & + & + & + \\ \hline
**Usage complexity (user)** & + & 0 & - & - \\ \hline
**Usage complexity (developers)** & 0 & - & - & ++ \\ \hline
**Capability for reliable fault detection** & 0 & + & + & + \\ \hline
**Capability for reliable diagnosis** & **++** & 0 & 0 & 0 \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison of the Four Methods Evaluating the Performance of Different Criteria; - - : very low, - : low, 0 : neutral, + : high, ++ : very high. damage increments and the slope of the related curve allow conclusions to be made about the wear stages and the actual level of damage. An evaluation of the machine state can be made using threshold-based classification. The same filtering technique (diagnosis-oriented data filtering) is used in the third approach (condition evaluation based on a sensitivity matrix). A sensitivity matrix is constructed to consider the absolute level of wear and the actual changes in this level. Using this sensitivity matrix, the machine state can be evaluated as noncritical, alarming, or critical. Using the fourth method (data-based classification approach), a fuzzy-based classification algorithm analyzes the expertly classified data and generates a fuzzy-based model. The states are classified automatically. The first three methods allow a simple integration and implementation into the operation level, while the fourth method is characterized by the capacity to classify different states automatically after training and therefore represents the class of supervised (machine learning approaches) to be applied for monitoring. Based on these methods, the relationship can be determined between the physical failure modes and the calculated characteristics. Thus, the automatic classification of the changes that occur stochastically during wear processes is possible.