## 1 Introduction  
Shaft tube deformations due to surrounding rock mass pressure [1, 2, 3, 4], the operation of the hoisting conveyances, and damage related to the extreme corrosion that affects mine shafts [5, 6, 7, 8] result in misalignments of the guide rails of shaft steelwork. These misalignments are the sources of interaction forces between the hoisting conveyances and the shaft steelwork; they are transverse to the shaft axis and are called guiding forces [9, 10]. Accelerometric measurements are commonly used in monitoring structures. Based on changes in the modal properties of a structure, it is possible to draw conclusions about its condition [11, 12]. In monitoring a mine shaft hoisting system, accelerometer sensors are placed on conveyances to assess how the condition of the steelwork affects its movement. The detection of the size and shape of guide rail faults based on accelerometric measurements has been the subject of several studies [13; 14; 15; 16; 17; 18; 19]. Conducting measurements under shaft conditions is difficult; hence, many researchers carry out their research on physical models at the laboratory scale. The detection of defects in guide rails boils down to the analysis of acceleration waveforms as a function of time or position and the determination of their specific patterns corresponding to the passage of the conveyance through a given fault. Typically, these faults are discontinuous in nature and are dumped off neighboring guides or excessive gaps between guides. Acceleration characteristics due to varying operating factors [13] represent a characteristic frequency-unknown problem for which they require the use of advanced data analysis methods for determining specific patterns corresponding to typical faults. [PERSON] et al. [14] used a multi-time-scale technique and a dynamic time-warping algorithm in their study. [PERSON] et al. [15] and [PERSON] et al. [16] used a convolutional neural network. [PERSON] et al. [17] used the boosting-tree method and compared their results with those of other data analysis methods. [PERSON] et al. [18] used a support vector machine to detect faults. The techniques used allowed the researchers to detect faults with an accuracy of more than 90%. It should be noted, however, that the detection of defects required measurements at high frequencies, which is not a problem for research on physical models made in the laboratory, but causes great difficulty in transferring the methods to the industrial scale, where guide rails often exceed 1000 m. The amount of data that are produced while running these methods can cause a computational problem; therefore, in field tests, the measurement frequency must be limited. Any study of the behavior of the hoisting conveyance in a shaft steelwork system is complicated by the fact that the conveyance is not rigid and cannot be treated as a rigid body [19]. One way to account for the non-rigid behavior of the hoisting conveyance is an analytical dynamic model built from points with mass and inertia connected by springs and viscous damper elements. Solving the equations of motion for such models allows the calculation of the guiding force as a function of time. [PERSON] [20] used a dynamic model of conveyance to determine the vibration of a skip in the context of the fatigue strength of its components. [PERSON] et al. [21] and [PERSON] et al. [22] conducted studies to evaluate the effects of magnetorheological dampers on vibration reduction in a skip-steelwork system. On the basis of studies on dynamic models, [PERSON] and [PERSON] [23] analyzed the risk of the unstable operation of a hoisting system and the damaging resonance of an entire mine hoist system. For the safe operation of shaft steelwork, the key features are the load-carrying capacity of the steelwork as a function of corrosion loss and the load effect as a function of guiding forces.