## 1 Introduction
[PERSON] and [PERSON] [21] used the autoregressive coefficient of signals as an input value to a support vector machine. Vibration signals in elevator fault diagnosis are also used as input features to algorithms, such as neural networks [22] in traditional and fuzzy versions [23], as well as convolutional neural networks [24]. Another way to diagnose the elevator main drive was presented by [PERSON] et al. [25] using an induction electric motor that is the elevator's main drive component for analysis. The authors noted that faults in shaft guides are associated with an increase in friction in vertical motion, which is perceived by the motor as an increase in load torque. They used current signature analysis to detect such faults. To do this, they measured the current signal at a sampling rate of 1 kHz and then calculated the root mean square for every 20 samples of the signal. Then, by comparing the signals of a normally functioning and damaged elevator, it was possible to determine where the guides had degraded. The literature also shows interest in more challenging methods such as image analysis and transfer learning. The authors in [26] used images of ropes in an elevator shaft and an autocorrelation model to analyze their damage. In contrast, researchers in [27] estimated the remaining useful life of an elevator based on acoustic measurements of various elevators, using transfer learning and algorithms, such as Graph Neural Network and Long Short-Term Memory Network. Among the most sensitive points of the elevator system is certainly the brake, which provides additional protection for the elevator in the event of power loss. The authors in [28] used an Ishikawa diagram, also known as a fish diagram, which is used for cause-and-effect analysis, to study elevator brake failure. It provides a visual representation of the various events' causes and suggests solutions to problems. The authors describe the method in two steps. The first is a problem cause analysis, including their hierarchical division and the selection of relevant factors. Then a fish diagram is drawn, in which the most important causes of errors are written on the main bones, and then following down the hierarchy, smaller and smaller bones are drawn. The presented method, of course, requires expertise in the construction and operation of lifting equipment. An in-depth analysis of the phenomena occurring in these systems makes it possible to correctly develop the diagram, taking into account the effects and causes. In addition, it is worth noting that, depending on the type of damage, it may be necessary to build multiple diagrams, since failures differ not only in their potential effects, but also in their level of harm to the entire system. Failure diagnosis methods that require expert knowledge include fault trees. Trees are built for individual failures, with each tree designed to identify and prioritize potential causes of failures. Primary incidents represent the causes of failures or other events and can be directly related to a particular piece of equipment. Incidents are then grouped into so-called cut sets, which arise when several basic events occur and cause another phenomenon. Such a cause-and-effect relationship between events leads to the localization of system weak spots. In addition, in this way, it is possible to determine what is the minimum path that leads to the occurrence of failure. Fault diagnosis using fault trees also implies estimating the probability of individual events, which leads to the development of a procedure for searching for a faulty component. The authors in [29] emphasize that such searches can be very costly, as checking for individual events often involves tests that are time-consuming (resulting in downtime costs) but also require activities such as entering the elevator shaft. Therefore, researchers are trying to develop trees in such a way that the troubleshooting procedure is as efficient as possible.