## 3 Methods
### Data Basis  
Measurements are available for each of the different asset classes (Section 2) for the period from 2009 to 2020. On the one hand, each asset is regularly evaluated in time intervals specified for each asset class, and on the other hand, an evaluation is performed after an asset has received a replacement or a substantial maintenance measure. Consequently, the substantiality of a maintenance measure is defined by costs above a certain threshold. This means that the time interval between two quality measurements differs between assets of the same class, as well as for different classes. Furthermore, reactive and preventative maintenance measures are distinguished. Reactive maintenance measures are used to repair short-term failures and damage. Preventative measures, on the other hand, serve to prevent failures or damage before they occur. Table 3 shows an example extracted from the data records for two different assets (platform and elevator). In addition to the information on the asset, such as the individual identifier (asset number), the corresponding asset class, the year of construction, and the service life, information on its quality measurement is also required for the evaluation. The reason for the quality measurement and the result of the measurement are also relevant information, in addition to the date, which is used to calculate the age of the asset during the measurement. A total of 321,626 measurements from the years 2009-2020 are available for the asset classes considered.  
Figure 2: Historical development of the BAQ grade and target values from the LuFV [7; 19; 20].  
For evaluating available data on quality assessments due to maintenance, replacement measures, or routine, each class is considered separately, as the assets of different classes are diverse in their characteristics. This analysis aims to separate the assets' quality degradation over time and quality improvement as an effect of maintenance measures. In this process, the scores of the assets are analyzed before being converted into a grade.  
However, data are only available for a limited time period, which does not cover the service times of each asset class; thus, the history of the assets is partially unknown. Therefore, the impact of maintenance measures cannot be directly separated from age-based degradation in the data, since historical--hence, unknown--maintenance measures influence current asset quality. Consequently, available asset scores might be biased towards better quality than the quality that is explainable by the data.  
Instead of extracting net degradation data, maintenance measures and the circumstances under which these are performed are analyzed in detail. As only maintenance measures over a specific financial threshold are tracked, the scores of an asset can improve from one quality assessment to another without this improvement being explicitly related to a particular maintenance measure in the data.  
Therefore, in the approach described here, measurements declared as maintenance in the data are not the only ones considered maintenance measures, but all measurements that lead to an improvement in scores are considered as such.