## 1 Introduction
Furthermore, he used statistical tests to determine whether an elevator is stationary for an unusually long time. The application of the proposed method was illustrated by an example in which detected faults were compared with the elevator's service log. One of the best known ways to analyze the state of electromechanical systems for proper functioning is spectral analysis using vibration signals [8]. For this analysis, it is necessary to measure the vibration level in three axes of the device in question. In the case of an elevator, spectral analysis makes it possible to extract the frequencies characteristic of the movement under normal conditions, as well as emergency conditions. Frequency analysis of vibration signals can suppress noise created during measurements, preserve phase information, and describe non-linear couplings between signals [9]. The authors in [10; 11] additionally used a bi-spectrum autoregressive model for their analysis. Theyemphasize that the shapes and distributions of AR spectrum peaks are clearly different for normal and damaged elevators. Spectral analysis using acoustic or vibration signals; however, requires measurements to be made at high sampling rates so that spectral changes at different frequencies can be captured. Machine learning is one of the most modern methods used in diagnostics and fault detection of many devices, including elevators. Due to the wide choice of algorithms and the ability to operate on data extracted from functioning equipment, machine learning has gained popularity in the scientific and business world. [PERSON] and [PERSON] employed a feature extraction structure based on autoencoders, followed by fault detection, using random forests in three of their studies. In [12], together with [PERSON], they used accelerometer signals for fault detection. Subsequently, in [13], they proposed the extraction of elevator ride profiles and incorporated additional magnetic signals for detection. In [14], they improved detection accuracy by omitting both the ride profiling and magnetic signals, and presented results obtained from a larger dataset collected over nine months from three elevators, validating the method on entirely new data. Based on the collected data, the researchers applied a feature extraction method using a deep autoencoder. The algorithm is an example of an unsupervised artificial neural network [15], whose task is to efficiently encode data into a form with fewer dimensions. This approach allows extraction of the most characteristic features of a given set. The features created by the autoencoder that characterize the moving elevator were used for binary classification using a random forest algorithm. The same authors also attempted to use other classifiers, such as multilayer perceptron [16]. The authors in [17] used accelerometer measurements and a convolutional neural network with one-dimensional convolution layers for fault diagnosis, whose parameters were selected using particle swarm optimization. A different approach to fault diagnosis of the elevator main drive was presented by the authors in [18], who used elevator vibration signals and then subjected them to noise reduction using wavelet functions. The authors also proposed feature extraction related to the signal envelope and classification, using a stochastically constructed neural network. The idea behind this model is to configure the network randomly with limited need for human intervention. Instead of training a model with a fixed architecture, the process of constructing a neural network starts with a structure of small size and then incrementally adds hidden nodes until acceptable results are achieved. This approach does not require any prior knowledge of the network complexity for a given task [19]. [PERSON] et al. [20] used machine learning to monitor the state of a sheave-rope transmission. The literature also includes ways to diagnose elevator failures using methods that combine signal processing theory and machine learning algorithms.