## 1 Introduction
The selection of the model's hyperparameter values was performed using Bayesian optimization and k-fold cross-validation. It is worth mentioning that the authors obtained high efficiency (average F1 above 0.99) despite the inclusion of class imbalance. The above review of the world literature in the field of passenger elevator malfunction diagnostics shows the lack of a comprehensive study of the most frequently failing system, which is the cabin doors. Although this topic has been known in principle since the early days of passenger elevators, only recently have attempts been made in the world literature to use modern diagnostic tools to improve the reliability of these devices and streamline the maintenance process. Among the studies reviewed, the vast majority focus attention on a single component of the system (for example, the locking mechanism) or carry out detection, distinguishing only failures related to door blocking. Other articles present a classification with a distinction between normal and emergency operations. Such an approach, however, does not allow the development of a system that would inform the elevator maintainer which component has most likely failed. In addition, not all researchers have taken into account in their studies the problem of class imbalance associated with the fact that under the conditions of normal operation of any system, faults occur occasionally and are a minority among the operating data of equipment. It is also worth mentioning that methods such as fault trees, despite the fact that they provide information about the potential causes of each component's failure, are not based on the actual measurement data of the device and require verification tests to be performed. Another problem of some of the methods outlined above is the need to perform measurements at high frequency, which in the case of remote acquisition can be very costly since such a large amount of data collected for many devices would require huge disk spaces or cloud storage. An additional consideration in the case of a large number of measurements is the necessity to transmit them over an internal elevator bus, which has a limited throughput. The flow of data on this bus must not be disrupted, as the most important information on the operation of the device is transmitted there. Any problems caused by excessive load on the transmission can result in malfunctions. Key contributions and novel aspects of this article are as follows:
* Inclusion of a comprehensive dataset encompassing 10 distinct types of elevator door failures to ensure robust and diverse failure modeling and development of a data preprocessing method to reduce the amount of data transmitted, ensuring that diagnostic data transmission does not overload the CAN bus, which is shared with other critical elevator components. * Comparison of fault identification effectiveness across different machine learning algorithms, including logistic regression, decision trees, support vector machines, stochastic gradient descent (SGD) classifiers, random forests, and gradient boosting. Additionally, hyperparameter tuning and validation of results were performed. * Proposal of a practical implementation strategy for deploying the developed data preprocessing method directly on the elevator door drive controller for real-time diagnostic applications. Figure 1 presents the chronological diagram of the proposed method. Initially, a test stand was built with a prototype elevator door drive system. Next, the most common failures in such systems were identified, and measurements were taken for normal operation and various types of faults. The data was then preprocessed and prepared for modeling using machine learning methods. Different data reduction techniques were also compared, including a custom method that reduced data volume by aggregating statistical features such as the mean and standard deviation. In the next stage, the effectiveness of seven machine learning algorithms was tested, hyperparameter tuning was performed, and the results were evaluated.