## 1 Introduction  
In the modern economic approach to industrial production carried out under the pressure of market demand and competition, the cost involved in using the means of production amounts substantially to the expenses for the maintenance and repair of equipment and/or technological machinery [1], which often totals to a surprisingly large amount for any mining company [2; 3; 4]. One of the major disadvantages in terms of productivity and production results is equipment failure, as it negatively influences production times and increases expenses at the organisation level [2]. Although mining technological equipment and machinery are designed so that they can function for long periods of time, they are nevertheless affected by various factors that determine the degradation of various component elements. Keeping mining equipment in good working condition involves the use of methods that can be used to evaluate the state of the component elements of their infrastructure. The corrosion of metal structures is widely encountered in various industrial fields. To monitor the corrosion phenomenon, there are different methods based on certain principles. These methods are subject to applicability limits [5; 6; 7]. Non-destructive (ND) techniques are crucial to identifying corrosion early on [8; 9], allowing for timely maintenance and the prevention of potential safety hazards. By implementing ND techniques, industries can ensure the longevity and reliability of their materials without compromising performance [9]. In the study of the corrosion of carbon steel pipes, [PERSON] et al. [10] propose a hybrid machine learning approach to feature extraction and classification for corrosion detection and severity assessment. This approach combines Wavelet Packet Transform (WPT) algorithms integrated with Fast Fourier Transform (FFT) and Linear Support Vector Classifier (L-SVC) algorithms. [10]. The integration of WPT and FFT allows the analyst to capture both time and frequency domain information, enhancing the accuracy of feature extraction. The use of L-SVC provides a robust classification model that can effectively predict the severity of corrosion based on the extracted features [10]. The results obtained by the authors confirmed that the proposed automatic learning approach can be used effectively for corrosion detection and severity assessments in SHM applications. Researchers have highlighted different AE-based corrosion detection [5; 6; 11; 12], prediction, and reliability assessment models for online condition monitoring of carbon steel pipelines [9; 13; 14; 15] and acoustic emission signal forecasting [16; 17], also highlighting the importance of acoustic emission (AE) techniques compared to other non-destructive methods [9]. A hybrid method using the Hilbert-Huang transform and the wavelet transform was implemented by [PERSON] et al. [18] for the detection and localisation of structural damage. The numerical results obtained from the experimental models surpass those of the other models in terms of accuracy and prediction rate. [PERSON] et al. [19] applied structural damage detection techniques based on wavelet analyses of spatially distributed structural response measurements. Thus, different local disturbances that cannot be highlighted from the measured total response data could be perceived from the component wavelets. The corrosion of mining equipment frequently occurs due to the periodic mechanical demands to which their structural elements are exposed and the environmental conditions in which this equipment operates. The corrosion of mining equipment can occur as an effect of various causes, including structural properties, material properties, chemical factors, and environmental factors (Figure 1). Equipment exposed to corrosive environments is susceptible to degradation and loss of structural integrity, which can lead to severe damage and structural instability. Natural frequencies are essential characteristics of any structure, reflecting its dynamic behaviour in the absence of external disturbances.