Rotary drilling technology with diamond tools is still essential in progressively extracting the earth’s resources. Since investigating the disintegration mechanism in actual conditions is very difficult, the practice must start with laboratory research. Identifying and classifying the drilling stand and its aggregates as objects will contribute to the clarification of certain problems related to streamlining the process, optimizing the working regime, preventing emergencies, and reducing energy and economic demands. For these purposes, the cluster method was designed and applied. Applying the clustering method has a significant place in complex and dynamic processes. Eight vibration signals were measured and processed during the operation of the aggregates, such as the motor, pump, and hydrogenerator, with a sampling frequency of 18 kHz and a time interval of 30 s. Subsequently, 16 symptoms were designed and numerically calculated in the time and frequency domain, creating the symptom vector of the aggregate. The aim of the study and article was the classification of aggregates as objects into recognizable clusters. The results show that the strong symptoms include a measure of variability, variance in the signal, and kurtosis. The weak symptoms are skewness and the moment of the signal spectrum. Visualization in the symptom plane and space proved their influence on cluster formation. According to the cluster analysis results, six to seven clusters presenting the activity of the aggregates were classified. It was found that the boundaries between the clusters were not sharp. As part of the research, the centroids of clusters of aggregates and the distances between them were calculated. Classified clusters can rebuild reference clusters for objects with a similar character in a broader context.

Underground coal gasification (UCG) is a clean coal mining technology without significant environmental impacts. This technology can also be used in deep, hard-to-reach seams or deposits affected by tectonic disturbances, where conventional mining is impossible. Several techniques and methods have been investigated worldwide to support the process control of UCG. Global research focuses on the control of UCG operating parameters to stabilize or to optimize the performance of the underground reactor during energy conversion. This paper studies recent research in the field of UCG control and compares individual control techniques and possibilities for practical application. The paper focuses on advanced control methods that can be implemented in an in situ control system (e.g., adaptive control, extremum seeking control, and robust control). The study investigates control methods that ensure desired syngas calorific value or maximization. The review showed that robust control techniques such as sliding mode control and model predictive control have the most significant potential, and achieve the best results despite their complexity. In addition, some methods have been investigated through simulation or experimentally. The paper aims to give the reader an overview of the given issue and to alert the practice to recent research in the given area.

The paper proposes a rock classification method using the vibration signal of the rotary drilling process. This proposed method classifies four rocks with a reliability of 100 %, from the vibration signal record of a lasting 1/4 second. For the design of a suitable classification method, several attributes of the vibration signal were calculated for two different signal recording lengths. Cluster dendrogram, ANOVA test, and boxplot were used to determine useful attributes and proper signal length. The classification rule was found using a decision tree, machine learning tool. The publication gradually describes the process of creating the classification method and the results of the reliability verification of the proposed classification method. Rock disintegration by rotary drilling was carried out under standardized experimental conditions. The disintegration rocks were andesite, granite, limestone, and concrete.

This research aims to classify rock types based on the vibration signal propagated from the experimental rotary drilling process, where the generated vibration signal is a source of information. Its measurement and processing provide important information about the rock disintegration process, the drilled rock, the drilling tool, and the drilling parameters. For the design of a suitable classification method, several attributes of the vibration signal were calculated for two different signal recording lengths. A cluster dendrogram, an ANOVA test, and a boxplot were used to determine attributes and proper signal length. The classification rule was found using a decision tree, a machine-learning tool. This publication gradually describes the process of creating the classification method and the results of the reliability verification of the proposed classification method. The disintegrated rocks were andesite, granite, limestone, and concrete used as artificial rock. This proposed method classified these three rock types and concrete with a reliability of 100% from a vibration signal record lasting 1/4 s.

In today’s era, it is important to analyze and utilize various signals in industrial or laboratory applications. Measured signals provide critical information about the controlled system, which can be contained precisely within a narrow frequency range. Many methods and algorithms exist to process such signals in both the time and frequency domains. In particular, signal processing in the frequency domain is primary in industrial practice because dominant components within a specific narrow frequency band are sought. The discrete Fourier transformation (DFT) algorithm is the tool used in practice to find these frequency components. The DFT algorithm provides the full frequency spectrum with a higher number of calculation steps, and its spectrum frequency resolution is low. Therefore, research has focused on finding a method to achieve high-frequency spectrum resolution. An important factor in selecting the technique was that such an algorithm should be implementable on a microprocessor-based system under harsh industrial conditions. Research results showed that the DFT ZOOM method meets these requirements. The frequency zoom has many advantages but requires some modification. It is implemented in high-performance analyzers, but a thorough and detailed description of the respective algorithm is lacking in technical articles and literature. This article mathematically and theoretically describes the modified frequency zoom algorithm in detail. The steps of the frequency zoom, from creating an analytical signal through frequency shifting and decimation to the frequency analysis of the signal, are realized. The algorithm allows for the analysis of a signal with high-frequency resolution in a limited frequency band. A significant modification of DFT ZOOM is that of using the Hilbert transform to create an analytic signal. This resolves the aliasing issue caused by the overlap between fundamental and sideband spectra. Results from processing deterministic and stochastic signals using the modified DFT ZOOM are presented. The presented experimental results contribute to a more detailed frequency analysis of the signal. As part of this scientific research, the issues of frequency zoom were thoroughly addressed, solving the partial problems of this algorithm, both in theory and in the context of signal theory.