## 5 Related Work  
Previous work has already explored PTing for FDD (8) by applying expert knowledge rules for assessment of measured data. For more detailed pinpointing of faults, [PERSON] applied a model-based FDD method comparing simulation results to measured data. In an effort that relates the concepts of FDD and PTing, [PERSON] et al. (2016) focused on reciprocating chillers, assuming very detailed knowledge of the particular chiller for their method. Various FDD approaches that utilize historical data are also related to PTing but often the tested thresholds are not based on building certificates. One example is the approach for real-time detection of anomalies presented by [PERSON] et al. (2017), which is based on comparison of real and predicted consumption by applying the two-sigma rule. These approaches use highly detailed models for predicting the expected behavior of a particular component, while BuMPeT is flexible in this sense, and able to work with various kinds of models, regardless of the complexity of their design and usage. FDD approaches that integrate Building Information Models (BIMs) are also relevant to our method. Existing BIMs, however, only cover a subset of the relevant relationships that Brick is able to represent ([PERSON] et al., 2018). [PERSON] et al. (2018) present a method for using building information models for semi-automatic constructing models for model-based FDD. However, a large proportion of the model parameters are still set by manual surveys or calibrated from historical data. [PERSON] et al. (2018) present a method that from a building information model generates relevant PT cases. However, for detailed fault diagnosis they apply a model-based approach without a direct coupling to the PTs. Another building information model enabled approach has been described by [PERSON] et al. (2018). This approach integrates a simulation module, a module for static and dynamic information as well as a separate FDD module. While it presents good experimental results, the effort to prepare the information infrastructure necessary for the FDD process is greater than the effort the implementation of BuMPeT would take. Moreover, the modules are not connected. In comparison, BuMPeT builds on the more descriptive building metadata models. It provides a direct link between PTs and the procedures for narrowing down faults within a building. Furthermore, the results in this paper document the benefits of the method when applied to a real case building. Application to a new building is only conditioned by the availability of the building's metadata model. The case study in ([PERSON] et al., 2018) has been applied to the HVAC system only, without further information on how to extend the methodology onto other subsystems. One limitation of the proposed system is that it \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**X** & **A** & **B** & **C** & **D** & **E** & **Elim. Area** \\ \hline NA & F & NA & P & P & NA & 19\% \\ NA & F & NA & P & F & NA & 9\% \\ NA & F & NA & F & P & NA & 9\% \\ NA & NA & F & NA & NA & P & 20\% \\ F & P & F & P & P & P & 74\% \\ F & F & P & P & P & P & 45\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Evaluation of Location of underperforming area strategy. depends on human input, i.e., occupants are expected to submit tickets upon experiencing deteriorated performance of the HVAC. This is a shortcoming as faults or underperformance may emerge long before the occupants experience them and report them. The development of this approach seems to be relatable to a single BIM within the case study and lacks applicability to other BIMs without an extensive integration period. Contrary to this, our approach BuMPeT is based on PTing that utilizes building instrumentation, and thus requires no user input. Furthermore, it is easily extendable to other buildings and their respective BIMs. The ability for the PT framework to automatically discover and instantiate the applicable PTs from a metadata model supports its flexibility and portability [30]. [PERSON] et al.