## 6 Limitations and Future Work  
The conducted research is, of course, not free from limitations resulting from the adopted methodology. First and foremost, it was not possible to obtain data on malfunctions from operational elevators, as this would have been very time-consuming--properly designed systems experience malfunctions very rarely. Additionally, deliberately inducing malfunctions in a functioning device could lead to a reduction in its service life. However, it is worth emphasizing that the developed model of the elevator cabin door drive system is a faithful replica of real devices manufactured by a company specializing in elevator production, with which the authors have collaborated. The prototype system was built using components provided through this collaboration. Moreover, the methods for simulating faults developed for research purposes were consulted with specialists in the field of cabin door drives. It should also be noted that although the exact values of diagnostic parameters, such as motor current or torque, may vary depending on the specific design of the device, for failures such as mechanical drive blockage, the symptoms will be nearly identical to those in a functioning system. In the case of failures such as a broken drive belt, the signals at the exact moment of the fault were not captured; however, the general symptoms of a broken belt are the same and enable easy detection of the malfunction and quick notification of the device maintenance personnel. From the perspective of rapid notification, it does not matter whether the maintenance department receives information about the malfunction one second or one minute after it occurs. It should also be kept in mind that the mechanical design of cabin door drives \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Method** & \
\begin{tabular}{c} **Number of Door** \\ **States Taken** \\ **into Account** \\ \end{tabular} & **Data Reduction** &
\begin{tabular}{c} **Number of** \\ **ML Methods** \\ **Compared** \\ \end{tabular} & **Effectiveness** \\ \hline
[34] & Multi-agent System & 7 & no & NA & ND \\ \hline
[35] & Machine learning & 4 &
\begin{tabular}{c} sliding window with sampling \\ from 40 ms to 1 s \\ \end{tabular} & 4 & F1 score \(\sim\)90\% \\ \hline
[36] & Machine learning & 4 &
\begin{tabular}{c} sliding window with sampling \\ from 10 ms to 1 s \\ \end{tabular} & 3 & F1 score \(\sim\)100\% \\ \hline
[37] & Machine learning & 5 & no & 1 & ND \\ \hline
[38] & Machine learning & 3 & no & 3 & F1 score \(\sim\)99\% \\ \hline this article & Machine learning & 11 &
\begin{tabular}{c} cycle aggregation with transmission \\ after every cycle (\(\sim\)12 s) \\ \end{tabular} & 7 & F1 score \(\sim\)97\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of cabin door drives fault diagnosis methods. itself protects users and personnel from injury in the event of any of these failures. The primary objective of the research was to enable quick diagnosis and notification to restore the device to normal operation as soon as possible. As part of future work, broader analyses of the system's vibration measurements are planned. It will also be possible to examine the impact of the accelerometer's placement and the method of processing measurement data (e.g., Fourier transform) on the effectiveness of the diagnostic model. Additionally, feature importance studies for the dataset will be conducted. It is likely that some diagnostic parameters can be discarded due to their minimal impact on the model's performance, thereby reducing the number of required measurement sensors. Work is currently underway to implement a monitoring and data collection system for malfunctions in operational elevators. In the future, this system will be used for research focused on predicting malfunctions in advance, enabling predictive maintenance. Conceptualization, D.C. and K.K. ; Methodology, D.C. and K.K. ; Software, J.G. and B.D. ; Validation, J.G. ; Formal analysis, D.C.; Investigation, J.G. and B.D. ; Resources, K.K.