## 1 Introduction
Reliability analysis is sometimes not feasible when there are difficulties in obtaining the exact probability of base events for an elevator system. In such cases, it may be reasonable to use fault trees built using fuzzy logic [30]. The high complexity of elevator systems makes the identification of potential failures and their causes a complicated issue, although of course this is highly dependent on the manufacturer and design of a particular device. Researchers in [31] study the effect of the pressure difference caused by the stack effect on the occurrence of the cabin doors re-opening problem. The problem stems from the fact that the increased pressure difference between the corridor and the elevator shaft causes an increase in motion resistance when the door closes, which can be interpreted by the control system as blocking the door and trigger re-opening. [PERSON] and [PERSON] [32] identified a total of 17 different elevator system faults, four of which were related to the cabin door drive and four to the corridor-side access door. The authors listed the following fault categories for both of these systems: mechanism failure, instrumentation defect, blockage, and other. As noted in the introduction to this paper, wide-ranging defects in the cabin doors of passenger elevators are among the most common elevator failures [33]. Therefore, in the scientific world, one can also find many attempts to diagnose passenger elevator cabin door failures, which are expected to contribute to increasing the reliability of these devices and improving maintenance efficiency. One such attempt using multi-agent systems is presented in [34]. Due to its high efficiency, machine learning was also used to diagnose the failures of elevator cabin door drive systems. In [35], the authors developed a model to diagnose mechanical faults associated with different types of door blocking. Among them, they distinguished--normal operation of the drive, using hand to block the door, blocking object in the door gap, object directly blocking the door. The authors used the electrical and mechanical parameters of the device's operation and past maintenance data sampled every 40 ms and then, using the sliding window technique, they calculated and added statistical features for a window length from 1 to 12 s. The authors compared the performance of four machine learning methods, with the best results obtained by the XGBoost algorithm (more than 90% classification accuracy with an F1 score of about 90%). However, the paper did not address the problem of strong class imbalance in the dataset, which is always present in real fault databases. Diagnostics of faults related to mechanical drive-blocking and caused by human factors are presented in [36]. The authors used operational and maintenance data of the elevator sampled at 100 Hz and then applied a convolutional neural network for feature extraction, which was then used to train a random forest cascade model. The final model contained 4 layers, each of which generated 201 random forests. The authors obtained high classification efficiency, but the article lacks information regarding the feasibility of implementing the developed method, which may have to do with its high level of complexity. In addition, the researchers also did not take into account the uneven distribution of classes in the dataset. The researchers in [37] developed a model for elevator door fault detection by focusing only on the door's locking mechanism. For this purpose, they used neural networks optimized using an MPGA (multi-population genetic algorithm). Failure identification of the elevator cabin door drive divided into normal driving with completion, normal interrupted by the user, and abnormal driving (with failure) is presented in [38]. The researchers used 17 different quantities characterizing the operation of a passenger elevator cabin door sampled at a frequency of 8 Hz. Then, they used an autoencoder for feature extraction and a support vector machine for classification.