## 4 Discussion  
The objective of this study was to study the performance of the transfer learning methodologies in an FDD problem in unfavourable conditions. Leveraging the information and knowledge from different HVAC installations to generate a model is the way to go to achieve the wide implementation of predictive or preventive maintenance systems for this kind of equipment. Generating the historical data necessary to produce a reliable fault diagnosis system has a high cost, not only monetary but also in terms of time since the collection of data has to cover the different operating conditions which are affected by the external weather conditions. However, directly transposing the methodologies and algorithms that have achieved great results for other fault diagnosis problems has an important flaw. The particularities of the field should be considered and HVAC installations are singular systems that are created on a case-by-case basis and despite manufacturers can sell pre-configured units, the control scheme or the thermal load and behaviour of the zones served cannot be defined beforehand. In other words, each HVAC system work in a unique installation and the similarity between them cannot be overlooked. This study demonstrates that a high fault diagnosis accuracy can be achieved by using a transfer learning approach, but not without adapting the methodologies to the HVAC FDD field. The results of the study show that, while the neural network architecture trained only using the source domain obtains an F-score of 0.55 in the diagnosis of target faults; when it is retrained with the target data instances, the accuracy does not improve. Instead, the F-score is reduced to 0.29 showing a degraded performance of the FDD method. Additionally, the application of popular transfer learning techniques shows F-scores that, in most cases, are below 0.60 and therefore cannot be considered reliable enough for a FDD system. Taken together, these findings suggest that contrary to what has been reported in current studies, transfer learning cannot be applied overlooking the particularities of the field and needs somehow a filtering mechanism to avoid the problem of negative transfer. This work has presented an innovative strategy to filter these samples by applying a pre-processing stage consisting of a filter that discards the dissimilar samples before proceeding with the model fine-tuning. This alleviates the number of samples needed for the model generation and at the same ensures the creation of a robust model. The results \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methodology** & **Precision** & **Recall** & **F-Score** \\ \hline DANN & 0.46 & 0.47 & 0.45 \\ Subspace alignment & 0.40 & 0.47 & 0.38 \\ Feature augmentation & 0.86 & 0.81 & 0.78 \\ CORAL & 0.17 & 0.14 & 0.07 \\ TCA & 0.52 & 0.65 & 0.55 \\ KLIEP & 0.51 & 0.35 & 0.30 \\ TrAdBaBoost & 0.74 & 0.73 & 0.73 \\ Proposed & 0.92 & 0.93 & 0.92 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of classification metrics for different transfer learning techniques. obtained when applying the filter to the target training samples show that the F-score of the final model can be increased from 0.29 to over 0.90 revealing the influence of the domain similarity. The data presented here emphasize the importance of the study of the similarity between the domains intended to use for transfer learning. In this regard, there are three important aspects to highlight of the presented methodology, which define the innovation introduced by the paper:  
* This study is not subject to the hypothesis of high similarity between the used domains, which is a common requirement in the published articles related to transfer learning in the field. This is the main advantage of the study compared with the current studies in the field,
* It provides an easily transferable scheme that does not require too much effort to be adapted and applied to a new installation without recorded historical data.