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Prof. Radosław Zimroz
Wroclaw University of Science and Technology, Faculty of Geoengineering, Mining and Geology, Department of Mining and Geodesy

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0 Mining Engineering
0 Predictive Maintenance
0 machines
0 Applied Signal Processing
0 Noise and Vibration

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Journal article
Published: 03 August 2021 in Electronics
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The local damage detection procedures in rotating machinery are based on the analysis of the impulsiveness and/or the periodicity of disturbances corresponding to the failure. Recent findings related to non-Gaussian vibration signals showed some drawbacks of the classical methods. If the signal is noisy and it is strongly non-Gaussian (heavy-tailed), searching for impulsive behvaior is pointless as both informative and non-informative components are transients. The classical dependence measure (autocorrelation) is not suitable for non-Gaussian signals. Thus, there is a need for new methods for hidden periodicity detection. In this paper, an attempt will be made to use alternative measures of dependence used in time series analysis that are less known in the condition monitoring (CM) community. They are proposed as alternatives for the classical autocovariance function used in the cyclostationary analysis. The methodology of the auto-similarity map calculation is presented as well as a procedure for a “quality” or “informativeness” assessment of the map is proposed. In the most complex case, the most resistant to heavy-tailed noise turned out the proposed techniques based on Kendall, Spearman and Quadrant autocorrelations. Whereas in the case of the local fault disturbed by the Gaussian noise, the most efficient proved to be a commonly-known approach based on Pearson autocorrelation. The ideas proposed in the paper are supported by simulation signals and real vibrations from heavy-duty machines.

ACS Style

Justyna Hebda-Sobkowicz; Jakub Nowicki; Radosław Zimroz; Agnieszka Wyłomańska. Alternative Measures of Dependence for Cyclic Behaviour Identification in the Signal with Impulsive Noise—Application to the Local Damage Detection. Electronics 2021, 10, 1863 .

AMA Style

Justyna Hebda-Sobkowicz, Jakub Nowicki, Radosław Zimroz, Agnieszka Wyłomańska. Alternative Measures of Dependence for Cyclic Behaviour Identification in the Signal with Impulsive Noise—Application to the Local Damage Detection. Electronics. 2021; 10 (15):1863.

Chicago/Turabian Style

Justyna Hebda-Sobkowicz; Jakub Nowicki; Radosław Zimroz; Agnieszka Wyłomańska. 2021. "Alternative Measures of Dependence for Cyclic Behaviour Identification in the Signal with Impulsive Noise—Application to the Local Damage Detection." Electronics 10, no. 15: 1863.

Journal article
Published: 22 June 2021 in Energies
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The possibility of the application of an unmanned aerial vehicle (UAV) in search and rescue activities in a deep underground mine has been investigated. In the presented case study, a UAV is searching for a lost or injured human who is able to call for help but is not able to move or use any communication device. A UAV capturing acoustic data while flying through underground corridors is used. The acoustic signal is very noisy since during the flight the UAV contributes high-energetic emission. The main goal of the paper is to present an automatic signal processing procedure for detection of a specific sound (supposed to contain voice activity) in presence of heavy, time-varying noise from UAV. The proposed acoustic signal processing technique is based on time-frequency representation and Euclidean distance measurement between reference spectrum (UAV noise only) and captured data. As both the UAV and “injured” person were equipped with synchronized microphones during the experiment, validation has been performed. Two experiments carried out in lab conditions, as well as one in an underground mine, provided very satisfactory results.

ACS Style

Paweł Zimroz; Paweł Trybała; Adam Wróblewski; Mateusz Góralczyk; Jarosław Szrek; Agnieszka Wójcik; Radosław Zimroz. Application of UAV in Search and Rescue Actions in Underground Mine—A Specific Sound Detection in Noisy Acoustic Signal. Energies 2021, 14, 3725 .

AMA Style

Paweł Zimroz, Paweł Trybała, Adam Wróblewski, Mateusz Góralczyk, Jarosław Szrek, Agnieszka Wójcik, Radosław Zimroz. Application of UAV in Search and Rescue Actions in Underground Mine—A Specific Sound Detection in Noisy Acoustic Signal. Energies. 2021; 14 (13):3725.

Chicago/Turabian Style

Paweł Zimroz; Paweł Trybała; Adam Wróblewski; Mateusz Góralczyk; Jarosław Szrek; Agnieszka Wójcik; Radosław Zimroz. 2021. "Application of UAV in Search and Rescue Actions in Underground Mine—A Specific Sound Detection in Noisy Acoustic Signal." Energies 14, no. 13: 3725.

Journal article
Published: 31 December 2020 in Sensors
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Diagnostics of industrial machinery is a topic related to the need for damage detection, but it also allows to understand the process itself. Proper knowledge about the operational process of the machine, as well as identification of the underlying components, is critical for its diagnostics. In this paper, we present a model of the signal, which describes vibrations of the sieving screen. This particular type is used in the mining industry for the classification of ore pieces in the material stream by size. The model describes the real vibration signal measured on the spring set being the suspension of this machine. This way, it is expected to help in better understanding how the overall motion of the machine can impact the efforts of diagnostics. The analysis of real vibration signals measured on the screen allowed to identify and parameterize the key signal components, which carry valuable information for the following stages of diagnostic process of that machine. In the proposed model we take into consideration deterministic components related to shaft rotation, stochastic Gaussian component related to external noise, stochastic α-stable component as a model of excitations caused by falling rocks pieces, and identified machine response to unitary excitations.

ACS Style

Anna Michalak; Jacek Wodecki; Michał Drozda; Agnieszka Wyłomańska; Radosław Zimroz. Model of the Vibration Signal of the Vibrating Sieving Screen Suspension for Condition Monitoring Purposes. Sensors 2020, 21, 213 .

AMA Style

Anna Michalak, Jacek Wodecki, Michał Drozda, Agnieszka Wyłomańska, Radosław Zimroz. Model of the Vibration Signal of the Vibrating Sieving Screen Suspension for Condition Monitoring Purposes. Sensors. 2020; 21 (1):213.

Chicago/Turabian Style

Anna Michalak; Jacek Wodecki; Michał Drozda; Agnieszka Wyłomańska; Radosław Zimroz. 2020. "Model of the Vibration Signal of the Vibrating Sieving Screen Suspension for Condition Monitoring Purposes." Sensors 21, no. 1: 213.

Journal article
Published: 28 December 2020 in Sensors
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Locating an inspection robot is an essential task for inspection missions and spatial data acquisition. Giving a spatial reference to measurements, especially those concerning environmental parameters, e.g., gas concentrations may make them more valuable by enabling more insightful analyses. Thus, an accurate estimation of sensor position and orientation is a significant topic in mobile measurement systems used in robotics, remote sensing, or autonomous vehicles. Those systems often work in urban or underground conditions, which are lowering or disabling the possibility of using Global Navigation Satellite Systems (GNSS) for this purpose. Alternative solutions vary significantly in sensor configuration requirements, positioning accuracy, and computational complexity. The selection of the optimal solution is difficult. The focus here is put on the assessment, using the criterion of the positioning accuracy of the mobile robot with no use of GNSS signals. Automated geodetic surveying equipment is utilized for acquiring precise ground truth data of the robot’s movement. The results obtained, with the use of several methods, compared: Wheel odometry, inertial measurement-based dead-reckoning, visual odometry, and trilateration of ultra-wideband signals. The suitability, pros, and cons of each method are discussed in the context of their application in autonomous robotic systems, operating in an underground mine environment.

ACS Style

Jarosław Szrek; Paweł Trybała; Mateusz Góralczyk; Anna Michalak; Bartłomiej Ziętek; Radosław Zimroz. Accuracy Evaluation of Selected Mobile Inspection Robot Localization Techniques in a GNSS-Denied Environment. Sensors 2020, 21, 141 .

AMA Style

Jarosław Szrek, Paweł Trybała, Mateusz Góralczyk, Anna Michalak, Bartłomiej Ziętek, Radosław Zimroz. Accuracy Evaluation of Selected Mobile Inspection Robot Localization Techniques in a GNSS-Denied Environment. Sensors. 2020; 21 (1):141.

Chicago/Turabian Style

Jarosław Szrek; Paweł Trybała; Mateusz Góralczyk; Anna Michalak; Bartłomiej Ziętek; Radosław Zimroz. 2020. "Accuracy Evaluation of Selected Mobile Inspection Robot Localization Techniques in a GNSS-Denied Environment." Sensors 21, no. 1: 141.

Journal article
Published: 27 December 2020 in Remote Sensing
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Extraction of raw materials, especially in extremely harsh underground mine conditions, is irrevocably associated with high risk and probability of accidents. Natural hazards, the use of heavy-duty machines, and other technologies, even if all perfectly organized, may result in an accident. In such critical situations, rescue actions may require advanced technologies as autonomous mobile robot, various sensory system including gas detector, infrared thermography, image acquisition, advanced analytics, etc. In the paper, we describe several scenarios related to rescue action in underground mines with the assumption that searching for sufferers should be done considering potential hazards such as seismic, gas, high temperature, etc. Thus, possibilities of rescue team activities in such areas may be highly risky. This work reports the results of testing of a UGV robotic system in an underground mine developed in the frame of the AMICOS project. The system consists of UGV with a sensory system and image processing module that are based on an adaptation of You Only Look Once (YOLO) and Histogram of Oriented Gradients (HOG) algorithms. The experiment was very successful; human detection efficiency was very promising. Future work will be related to test the AMICOS technology in deep copper ore mines.

ACS Style

Jarosław Szrek; Radoslaw Zimroz; Jacek Wodecki; Anna Michalak; Mateusz Góralczyk; Magdalena Worsa-Kozak. Application of the Infrared Thermography and Unmanned Ground Vehicle for Rescue Action Support in Underground Mine—The AMICOS Project. Remote Sensing 2020, 13, 69 .

AMA Style

Jarosław Szrek, Radoslaw Zimroz, Jacek Wodecki, Anna Michalak, Mateusz Góralczyk, Magdalena Worsa-Kozak. Application of the Infrared Thermography and Unmanned Ground Vehicle for Rescue Action Support in Underground Mine—The AMICOS Project. Remote Sensing. 2020; 13 (1):69.

Chicago/Turabian Style

Jarosław Szrek; Radoslaw Zimroz; Jacek Wodecki; Anna Michalak; Mateusz Góralczyk; Magdalena Worsa-Kozak. 2020. "Application of the Infrared Thermography and Unmanned Ground Vehicle for Rescue Action Support in Underground Mine—The AMICOS Project." Remote Sensing 13, no. 1: 69.

Journal article
Published: 25 December 2020 in Energies
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The low energy efficiency and excessive power of electric motors of large-scale vibrating machines for processing bulk materials motivated a new design of the inertial drive. This drive consists of one motor and two coaxial unbalanced masses, whose rotational frequencies are related in the ratio 2:1. This approach allows for a generation of the excitation force with variable amplitude and frequency, which changes depending on the inertial characteristics and shaft rotation frequency and does not relate to the phase difference of the unbalanced masses. Because of this, the symmetry axis of the resulting vector hodograph can be changed. The spectral composition of the exciting force up to 200 Hz contains higher harmonics, the energy share of which is 25.4% from the 2nd harmonic and 14.1% from the 3rd and higher harmonics that correspondingly improves bulk material treatment in comparison to single-frequency vibrators. The finite element model is used for checking the strength capacity of the most loaded units of a dual-frequency drive. Its use allows the realization of complex trajectories of motion that are more technologically efficient for variable parameters of the treated media and energy saving in sieving screens and other vibrating machines.

ACS Style

Volodymyr Gursky; Igor Kuzio; Pavlo Krot; Radoslaw Zimroz. Energy-Saving Inertial Drive for Dual-Frequency Excitation of Vibrating Machines. Energies 2020, 14, 71 .

AMA Style

Volodymyr Gursky, Igor Kuzio, Pavlo Krot, Radoslaw Zimroz. Energy-Saving Inertial Drive for Dual-Frequency Excitation of Vibrating Machines. Energies. 2020; 14 (1):71.

Chicago/Turabian Style

Volodymyr Gursky; Igor Kuzio; Pavlo Krot; Radoslaw Zimroz. 2020. "Energy-Saving Inertial Drive for Dual-Frequency Excitation of Vibrating Machines." Energies 14, no. 1: 71.

Journal article
Published: 25 December 2020 in Remote Sensing
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Usually, substantial part of a mine haulage system is based on belt conveyors. Reliability of such system is significant in terms of mining operation continuity and profitability. Numerous methods for conveyor belt monitoring have been developed, although many of them require physical presence of the monitoring staff in the dangerous environment. In this paper, a remote sensing method for assessing a conveyor belt condition using the Terrestrial Laser Scanner (TLS) system has been described. For this purpose a methodology of semi-automatic processing of point cloud data for obtaining the belt geometry has been developed. The sample data has been collected in a test laboratory and processed with the proposed algorithms. Damaged belt surface areas have been successfully identified and edge defects were investigated. The proposed non-destructive testing methodology has been found to be suitable for monitoring the general condition of the conveyor belt and could be exceptionally successful and cost-effective if combined with an unmanned, robotic inspection system.

ACS Style

Paweł Trybała; Jan Blachowski; Ryszard Błażej; Radosław Zimroz. Damage Detection Based on 3D Point Cloud Data Processing from Laser Scanning of Conveyor Belt Surface. Remote Sensing 2020, 13, 55 .

AMA Style

Paweł Trybała, Jan Blachowski, Ryszard Błażej, Radosław Zimroz. Damage Detection Based on 3D Point Cloud Data Processing from Laser Scanning of Conveyor Belt Surface. Remote Sensing. 2020; 13 (1):55.

Chicago/Turabian Style

Paweł Trybała; Jan Blachowski; Ryszard Błażej; Radosław Zimroz. 2020. "Damage Detection Based on 3D Point Cloud Data Processing from Laser Scanning of Conveyor Belt Surface." Remote Sensing 13, no. 1: 55.

Journal article
Published: 21 December 2020 in Energies
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The monitoring of drilling processes is a well-known topic in the mining industry. It is widely used for rock mass characterization, bit wear monitoring and drilling process assessment. However on-board monitoring systems used for this purpose are installed only on a limited number of machines, and breakdowns are possible. There is a need for a data acquisition system that can be used on different drilling rigs and for an automatic data analysis procedure. In this paper, we focused on the automatic detection of drilling cycles, presenting a simple yet reliable system to be universally installed on drilling rigs. The proposed solution covers hardware and software. It is based on the measurement of electric current and acoustic signals. The signal processing methods include threshold-based segmentation, a short-time envelope spectrum and a spectrum for the representation of results. The results of the research have been verified on a real drilling rig within the testing site of its manufacturer by comparing the results with the data of the on-board monitoring system installed on the machine. Novel aspects of our approach include the detection of the pre-boring stage, which has an intermediate amplitude that masks the real drilling cycles, and the use of the percussion instantaneous frequency, which is estimated by acoustic recordings.

ACS Style

Jacek Wodecki; Mateusz Góralczyk; Pavlo Krot; Bartłomiej Ziętek; Jaroslaw Szrek; Magdalena Worsa-Kozak; Radoslaw Zimroz; Paweł Śliwiński; Andrzej Czajkowski. Process Monitoring in Heavy Duty Drilling Rigs—Data Acquisition System and Cycle Identification Algorithms. Energies 2020, 13, 6748 .

AMA Style

Jacek Wodecki, Mateusz Góralczyk, Pavlo Krot, Bartłomiej Ziętek, Jaroslaw Szrek, Magdalena Worsa-Kozak, Radoslaw Zimroz, Paweł Śliwiński, Andrzej Czajkowski. Process Monitoring in Heavy Duty Drilling Rigs—Data Acquisition System and Cycle Identification Algorithms. Energies. 2020; 13 (24):6748.

Chicago/Turabian Style

Jacek Wodecki; Mateusz Góralczyk; Pavlo Krot; Bartłomiej Ziętek; Jaroslaw Szrek; Magdalena Worsa-Kozak; Radoslaw Zimroz; Paweł Śliwiński; Andrzej Czajkowski. 2020. "Process Monitoring in Heavy Duty Drilling Rigs—Data Acquisition System and Cycle Identification Algorithms." Energies 13, no. 24: 6748.

Review
Published: 21 December 2020 in Energies
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Tumbling mills have been widely implemented in many industrial sectors for the grinding of bulk materials. They have been used for decades in the production of fines and in the final stages of ore comminution, where optimal levels for the enrichment particles’ sizes are obtained. Even though these ubiquitous machines of relatively simple construction have been subjected to extensive studies, the industry still struggles with very low energy efficiency of the comminution process. Moreover, obtaining an optimal size for the grinding product particles is crucial for the effectiveness of the following processes and waste production reduction. New, innovative processing methods and machines are being developed to tackle the problem; however, tumbling mills are still most commonly used in all ranges of the industry. Since heavy equipment retrofitting is the most costly approach, process optimization with dedicated models and control systems is the most preferable solution for energy consumption reduction. While the classic technological measurements in mineral processing are well adopted by the industry, nowadays research focuses on new methods of the mill’s internal dynamics analysis and control. This paper presents a retrospective overview of the existing models of internal load motion, an overview of the innovations in process control, and some recent research and industrial approaches from the energy consumption reduction point of view.

ACS Style

Mateusz Góralczyk; Pavlo Krot; Radosław Zimroz; Szymon Ogonowski. Increasing Energy Efficiency and Productivity of the Comminution Process in Tumbling Mills by Indirect Measurements of Internal Dynamics—An Overview. Energies 2020, 13, 6735 .

AMA Style

Mateusz Góralczyk, Pavlo Krot, Radosław Zimroz, Szymon Ogonowski. Increasing Energy Efficiency and Productivity of the Comminution Process in Tumbling Mills by Indirect Measurements of Internal Dynamics—An Overview. Energies. 2020; 13 (24):6735.

Chicago/Turabian Style

Mateusz Góralczyk; Pavlo Krot; Radosław Zimroz; Szymon Ogonowski. 2020. "Increasing Energy Efficiency and Productivity of the Comminution Process in Tumbling Mills by Indirect Measurements of Internal Dynamics—An Overview." Energies 13, no. 24: 6735.

Journal article
Published: 18 December 2020 in Sensors
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The problem solved in this research is the diagnosis of the radial clearances in bearing supports and the loosening of fastening bolts due to their plastic elongation (creep) or weak tightening using vibration signals. This is an important issue for the maintenance of the heavy-duty gearboxes of powerful mining machines and rolling mills working in non-stationary regimes. Based on a comprehensive overview of bolted joint diagnostic methods, a solution to this problem based on a developed nonlinear dynamical model of bearing supports is proposed. Diagnostic rules are developed by comparing the changes of natural frequency and its harmonics, the amplitudes and phases of shaft transient oscillations. Then, the vibration signals are measured on real gearboxes while the torque is increasing in the transmission during several series of industrial trials under changing bearings and bolts conditions. In parallel, dynamical torque is measured and its interrelation with vibration is determined. It is concluded that the radial clearances are the most influencing factors among the failure parameters in heavy-duty gearboxes of industrial machines working under impulsive and step-like loading. The developed diagnostics algorithm allows condition monitoring of bearings and fastening bolts, allowing one to undertake timely maintenance actions to prevent failures.

ACS Style

Pavlo Krot; Volodymyr Korennoi; Radoslaw Zimroz. Vibration-Based Diagnostics of Radial Clearances and Bolts Loosening in the Bearing Supports of the Heavy-Duty Gearboxes. Sensors 2020, 20, 7284 .

AMA Style

Pavlo Krot, Volodymyr Korennoi, Radoslaw Zimroz. Vibration-Based Diagnostics of Radial Clearances and Bolts Loosening in the Bearing Supports of the Heavy-Duty Gearboxes. Sensors. 2020; 20 (24):7284.

Chicago/Turabian Style

Pavlo Krot; Volodymyr Korennoi; Radoslaw Zimroz. 2020. "Vibration-Based Diagnostics of Radial Clearances and Bolts Loosening in the Bearing Supports of the Heavy-Duty Gearboxes." Sensors 20, no. 24: 7284.

Journal article
Published: 01 December 2020 in Measurement
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Cyclostationary analysis is a useful approach in diagnostics of the machinery with rotating components. It allows indicating the cyclic modulations in the signal via analysis of a bi-frequency map called Cyclic Spectral Coherence (CSC). A non-zero CSC value at two frequencies means the presence of the cyclic process. Unfortunately, we have found that for some cyclostationary signals CSC provides difficult to interpret information. These disturbances in the CSC map have been linked to the presence of non-Gaussian noise. To prove it an original procedure has been proposed. Using simulations covering the model of signal, α-stable distribution, and Monte Carlo simulations it has been shown that indeed increasing presence of non-Gaussian noise makes worse the quality of diagnostic information extracted from CSC map. It has been recalled that the Cyclic Spectral Coherence is based on the autocovariance function of a given signal, thus it is properly defined for data coming from the distribution with the finite second moment. Finally, the authors selected three real examples that confirm the simulation-based findings. The main conclusion is before using the CSC analysis for cyclostationary signal one should validate the type of the noise. If noise is Gaussian - the CSC will bring optimal results. For the increasing level of impulsive non-cyclic noise, the CSC map becomes more and more disturbed and the detection of periodic excitation is difficult. Performed simulations on a very generic model some guidelines have been formulated regarding the acceptable level of non-Gaussian noise.

ACS Style

Jacek Wodecki; Anna Michalak; Agnieszka Wyłomańska; Radosław Zimroz. Influence of non-Gaussian noise on the effectiveness of cyclostationary analysis – Simulations and real data analysis. Measurement 2020, 171, 108814 .

AMA Style

Jacek Wodecki, Anna Michalak, Agnieszka Wyłomańska, Radosław Zimroz. Influence of non-Gaussian noise on the effectiveness of cyclostationary analysis – Simulations and real data analysis. Measurement. 2020; 171 ():108814.

Chicago/Turabian Style

Jacek Wodecki; Anna Michalak; Agnieszka Wyłomańska; Radosław Zimroz. 2020. "Influence of non-Gaussian noise on the effectiveness of cyclostationary analysis – Simulations and real data analysis." Measurement 171, no. : 108814.

Journal article
Published: 30 November 2020 in Energies
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Air-quality measurements in a deep underground mine are a critical issue. The cost of ventilation, as well as the geometry of the considered mine, make this process very difficult, and local air quality may be a danger to miners. Thus, portable, personal devices are required to inform miners about gas hazards. There are available tools for that purpose; however, they do not allow the storage of data collected during a shift. Moreover, they do not allow the basic analysis of the acquired data cost-effectively. This paper aims to present a system using low-cost gas sensors and microcontrollers, and takes advantage of commonly used smartphones as a computing and visualization resource. Finally, we demonstrate monitoring system results from a test in an underground mine located in Poland.

ACS Style

Bartłomiej Ziętek; Aleksandra Banasiewicz; Radosław Zimroz; Jarosław Szrek; Sebastian Gola. A Portable Environmental Data-Monitoring System for Air Hazard Evaluation in Deep Underground Mines. Energies 2020, 13, 6331 .

AMA Style

Bartłomiej Ziętek, Aleksandra Banasiewicz, Radosław Zimroz, Jarosław Szrek, Sebastian Gola. A Portable Environmental Data-Monitoring System for Air Hazard Evaluation in Deep Underground Mines. Energies. 2020; 13 (23):6331.

Chicago/Turabian Style

Bartłomiej Ziętek; Aleksandra Banasiewicz; Radosław Zimroz; Jarosław Szrek; Sebastian Gola. 2020. "A Portable Environmental Data-Monitoring System for Air Hazard Evaluation in Deep Underground Mines." Energies 13, no. 23: 6331.

Journal article
Published: 11 November 2020 in Sensors
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The problem of the informative frequency band (IFB) selection for local fault detection is considered in the paper. There are various approaches that are very effective in this issue. Most of the techniques are vibration-based and they are related to the cyclic impulses detection (associated with the local fault) in the background noise. However, when the background noise in the vibration signal has non-Gaussian impulsive behavior, the classical methods seem to be insufficient. Recently, new techniques were proposed by several authors and interesting approaches were tested for different non-Gaussian signals. We demonstrate the comparative analysis related to the results for three selected techniques proposed in recent years, namely the Alpha selector, Conditional Variance-based selector, and Spearman selector. The techniques seem to be effective for the IFB selection for the non-Gaussian distributed vibration signals. The main purpose of this article is to investigate how spectral overlapping of informative and non-informative impulsive components will affect diagnostic procedures. According to our knowledge, this problem was not considered in the literature for the non-Gaussian signals. Nevertheless, as we demonstrated by the simulations, the level of overlapping and the location of a center frequency of the mentioned frequency bands have a significant influence on the behavior of the considered selectors. The discussion about the effectiveness of each analyzed method is conducted. The considered problem is supported by real-world examples.

ACS Style

Jakub Nowicki; Justyna Hebda-Sobkowicz; Radoslaw Zimroz; Agnieszka Wylomanska. Local Defect Detection in Bearings in the Presence of Heavy-Tailed Noise and Spectral Overlapping of Informative and Non-Informative Impulses. Sensors 2020, 20, 6444 .

AMA Style

Jakub Nowicki, Justyna Hebda-Sobkowicz, Radoslaw Zimroz, Agnieszka Wylomanska. Local Defect Detection in Bearings in the Presence of Heavy-Tailed Noise and Spectral Overlapping of Informative and Non-Informative Impulses. Sensors. 2020; 20 (22):6444.

Chicago/Turabian Style

Jakub Nowicki; Justyna Hebda-Sobkowicz; Radoslaw Zimroz; Agnieszka Wylomanska. 2020. "Local Defect Detection in Bearings in the Presence of Heavy-Tailed Noise and Spectral Overlapping of Informative and Non-Informative Impulses." Sensors 20, no. 22: 6444.

Journal article
Published: 22 October 2020 in Sensors
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Monitoring the condition of rotating machinery, especially planetary gearboxes, is a challenging problem. In most of the available approaches, diagnostic procedures are related to advanced signal pre-processing/feature extraction methods or advanced data (features) analysis by using artificial intelligence. In this paper, the second approach is explored, so an application of decision trees for the classification of spectral-based 15D vectors of diagnostic data is proposed. The novelty of this paper is that by a combination of spectral analysis and the application of decision trees to a set of spectral features, we are able to take advantage of the multidimensionality of diagnostic data and classify/recognize the gearbox condition almost faultlessly even in non-stationary operating conditions. The diagnostics of time-varying systems are a complicated issue due to time-varying probability densities estimated for features. Using multidimensional data instead of an aggregated 1D feature, it is possible to improve the efficiency of diagnostics. It can be underlined that in comparison to previous work related to the same data, where the aggregated 1D variable was used, the efficiency of the proposed approach is around 99% (ca. 19% better). We tested several algorithms: classification and regression trees with the Gini index and entropy, as well as the random tree. We compare the obtained results with the K-nearest neighbors classification algorithm and meta-classifiers, namely: random forest and AdaBoost. As a result, we created the decision tree model with 99.74% classification accuracy on the test dataset.

ACS Style

Piotr Lipinski; Edyta Brzychczy; Radoslaw Zimroz. Decision Tree-Based Classification for Planetary Gearboxes’ Condition Monitoring with the Use of Vibration Data in Multidimensional Symptom Space. Sensors 2020, 20, 5979 .

AMA Style

Piotr Lipinski, Edyta Brzychczy, Radoslaw Zimroz. Decision Tree-Based Classification for Planetary Gearboxes’ Condition Monitoring with the Use of Vibration Data in Multidimensional Symptom Space. Sensors. 2020; 20 (21):5979.

Chicago/Turabian Style

Piotr Lipinski; Edyta Brzychczy; Radoslaw Zimroz. 2020. "Decision Tree-Based Classification for Planetary Gearboxes’ Condition Monitoring with the Use of Vibration Data in Multidimensional Symptom Space." Sensors 20, no. 21: 5979.

Journal article
Published: 02 October 2020 in Sensors
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Condition monitoring is a well-established field of research; however, for industrial applications, one may find some challenges. They are mostly related to complex design, a specific process performed by the machine, time-varying load/speed conditions, and the presence of non-Gaussian noise. A procedure for vibration analysis from the sieving screen used in the raw material industry is proposed in the paper. It is more for pre-processing than the damage detection procedure. The idea presented here is related to identification and extraction of two main types of components: (i) deterministic (D)—related to the unbalanced shaft(s) and (ii) high amplitude, impulsive component randomly (R) appeared in the vibration due to pieces of ore falling down of moving along the deck. If we could identify these components, then we will be able to perform classical diagnostic procedures for local damage detection in rolling element bearing. As deterministic component may be AM/FM modulated and each impulse may appear with different amplitude and damping, there is a need for an automatic procedure. We propose a method for signal processing that covers two main steps: (a) related to R/D decomposition and including signal segmentation to neglect AM/FM modulations, iterative sine wave fitting using the least square method (for each segment), signal filtering technique by subtraction fitted sine from the raw signal, the definition of the criterion to stop iteration by residuals analysis, (b) impulse segmentation and description (beginning, end, max amplitude) that contains: detection of the number of impulses in a decomposed random part of the raw signal, detection of the max value of each impulse, statistical analysis (probability density function) of max value to find regime-switching), modeling of the envelope of each impulse for samples that protrude from the signal, extrapolation (forecasting) envelope shape for samples hidden in the signal. The procedure is explained using simulated and real data. Each step is very easy to implement and interpret thus the method may be used in practice in a commercial system.

ACS Style

Karolina Gąsior; Hanna Urbańska; Aleksandra Grzesiek; Radosław Zimroz; Agnieszka Wyłomańska. Identification, Decomposition and Segmentation of Impulsive Vibration Signals with Deterministic Components—A Sieving Screen Case Study. Sensors 2020, 20, 5648 .

AMA Style

Karolina Gąsior, Hanna Urbańska, Aleksandra Grzesiek, Radosław Zimroz, Agnieszka Wyłomańska. Identification, Decomposition and Segmentation of Impulsive Vibration Signals with Deterministic Components—A Sieving Screen Case Study. Sensors. 2020; 20 (19):5648.

Chicago/Turabian Style

Karolina Gąsior; Hanna Urbańska; Aleksandra Grzesiek; Radosław Zimroz; Agnieszka Wyłomańska. 2020. "Identification, Decomposition and Segmentation of Impulsive Vibration Signals with Deterministic Components—A Sieving Screen Case Study." Sensors 20, no. 19: 5648.

Journal article
Published: 09 September 2020 in Applied Sciences
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Damage detection in complex mechanical structures is important for cost-effective and safe operation. Conveyor belts with steel cords are used for bulk material transport in mining companies. Due to harsh environmental conditions, both covers and cords are subjected to damage. As lengths of conveyors may vary from dozens of meters to kilometers, a belt loop consists of many connected belt pieces. Thus, the condition of splices between belt pieces is also critical. For both steel cord damage/wear detection and splice condition evaluations the NDT techniques based on magnetic field measurement and variability analysis are used. To obtain appropriate resolution, multi-channel data are collected. Here we propose a pre-processing technique developed for signal synchronization for biased splices data. The biased splices mean a phase shift between signals from a multi-channel sensor due to the design technology of the splice. As the quality of the splice is related to the appropriate precision of splice production, splice evaluation is defined as a similarity analysis of each signal with respect to the estimated pattern. Due to the mentioned phase shift, signals should be "synchronized" first, before final analysis. In industrial conditions, many factors may influence the signal shape. Thus, the problem of automated synchronization by shifting the signals may be defined as a multidimensional optimization problem. Here, we proposed to use a genetic algorithm with an algorithmically simple cost function for that purpose. In this paper, the authors propose an automated procedure applied to real measurement data and final results. A multidimensional optimization has been compared to simple signal shifting according to several criteria, and GA-based results were the best.

ACS Style

Tomasz Kozłowski; Jacek Wodecki; Radosław Zimroz; Ryszard Błażej; Monika Hardygóra. A Diagnostics of Conveyor Belt Splices. Applied Sciences 2020, 10, 6259 .

AMA Style

Tomasz Kozłowski, Jacek Wodecki, Radosław Zimroz, Ryszard Błażej, Monika Hardygóra. A Diagnostics of Conveyor Belt Splices. Applied Sciences. 2020; 10 (18):6259.

Chicago/Turabian Style

Tomasz Kozłowski; Jacek Wodecki; Radosław Zimroz; Ryszard Błażej; Monika Hardygóra. 2020. "A Diagnostics of Conveyor Belt Splices." Applied Sciences 10, no. 18: 6259.

Journal article
Published: 29 August 2020 in Measurement
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Local damage detection in bearings focuses on the identification of periodically impulsive components. Popular methods assume presence of either non-Gaussian noise or different frequency band for informative and non-informative impulses, and use statistics to select appropriate band. Here two impulsive sources occupy the same frequency range: a fault-related signal of interest, and non-cyclic noise describing random events during particular technological process (crushing, sieving etc.). The task is formulated as damage detection in presence of non-Gaussian impulsive noise. We propose to use Nonnegative Matrix Factorization of spectrogram for separation of cyclic and non-cyclic impulsive components. Partial information is fused into a single data set for each component. Finally, post-processing is implemented to allow to recover the time series of each component. New method allows to detect and extract impulsive signal (damage in bearing) in presence high amplitude non-cyclic impulsive signal. Moreover, the algorithm allows to properly indicate lack of any damage.

ACS Style

Jacek Wodecki; Anna Michalak; Radosław Zimroz. Local damage detection based on vibration data analysis in the presence of Gaussian and heavy-tailed impulsive noise. Measurement 2020, 169, 108400 .

AMA Style

Jacek Wodecki, Anna Michalak, Radosław Zimroz. Local damage detection based on vibration data analysis in the presence of Gaussian and heavy-tailed impulsive noise. Measurement. 2020; 169 ():108400.

Chicago/Turabian Style

Jacek Wodecki; Anna Michalak; Radosław Zimroz. 2020. "Local damage detection based on vibration data analysis in the presence of Gaussian and heavy-tailed impulsive noise." Measurement 169, no. : 108400.

Conference paper
Published: 28 August 2020 in Blockchain Technology and Innovations in Business Processes
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Operation of industrial metallurgical plants is associated with significant wear in spindles, gearboxes and bearings where difficult to implement digital diagnostic tools due to harsh operating conditions. Angular and radial gaps produce extremely high dynamic loads and abrupt failures in the multi-stand hot rolling mills. Reliable vibration monitoring is very complicated due to inherent changes of technological regimes, treated material and drive speed. It appears more beneficial to monitor dynamic torques in addition to vibration signals, but this is restricted to the installation of strain gauges. The more acceptable approach is to monitor static torques of electric motors and, having identified multi-body models, to calculate remaining useful life (RUL) of elements. Based on this approach, the new monitoring system is developed for the multi-stand mill with integration into plant automation infrastructure. Parameters adaptation of nonlinear dynamical models is provided and technological loads optimization by the criterion of RUL in rolling stands. System supports a database of maintenance actions and elements failures. Reports are generated on overloading and RUL.

ACS Style

Pavlo Krot; Ihor Prykhodko; Valentin Raznosilin; Radoslaw Zimroz. Model Based Monitoring of Dynamic Loads and Remaining Useful Life Prediction in Rolling Mills and Heavy Machinery. Blockchain Technology and Innovations in Business Processes 2020, 399 -416.

AMA Style

Pavlo Krot, Ihor Prykhodko, Valentin Raznosilin, Radoslaw Zimroz. Model Based Monitoring of Dynamic Loads and Remaining Useful Life Prediction in Rolling Mills and Heavy Machinery. Blockchain Technology and Innovations in Business Processes. 2020; ():399-416.

Chicago/Turabian Style

Pavlo Krot; Ihor Prykhodko; Valentin Raznosilin; Radoslaw Zimroz. 2020. "Model Based Monitoring of Dynamic Loads and Remaining Useful Life Prediction in Rolling Mills and Heavy Machinery." Blockchain Technology and Innovations in Business Processes , no. : 399-416.

Journal article
Published: 30 July 2020 in Engineering Failure Analysis
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This research paper represents the results of abrupt failure investigation in the structure of industrial plant for hydrostatic pressure testing of tubes. The significant damages of testing machine elements are observed after the moving tube impact when the opposite tube end cap has been suddenly released because of the screw-thread defect. The water pressure inside the tube at the time of the incident was only 20 MPa while the maximal pressure of 125 MPa is required by tubes tests specification. Therefore, based on admitted assumptions, the detailed analysis is conducted of static and dynamic forces acting on elements of structure to avoid full machine destruction in case of the next incidents under higher pressures. Conditions are determined of the sharp cap end penetration into thrust plate and possible damage of upper protection casing from the water jet and tube pieces impacts under conditions of the crack opening in the tested tube. The two options are proposed of plant modernization to reinforce structure for safety work under pressure from 70 MPa to 125 MPa depending on tube size and testing conditions. The developed improvements of machine structure allow simultaneously increasing the productivity of testing plant up to 14–20 tubes per hour depending on their sizes.

ACS Style

Pavlo V. Krot; Radoslaw Zimroz. Failure analysis and modernization of high-pressure hydraulic press for drilling tubes testing. Engineering Failure Analysis 2020, 117, 104772 .

AMA Style

Pavlo V. Krot, Radoslaw Zimroz. Failure analysis and modernization of high-pressure hydraulic press for drilling tubes testing. Engineering Failure Analysis. 2020; 117 ():104772.

Chicago/Turabian Style

Pavlo V. Krot; Radoslaw Zimroz. 2020. "Failure analysis and modernization of high-pressure hydraulic press for drilling tubes testing." Engineering Failure Analysis 117, no. : 104772.

Journal article
Published: 20 July 2020 in Applied Sciences
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It is well known that mechanical systems require supervision and maintenance procedures. There are a lot of condition monitoring techniques that are commonly used, and in the era of IoT and predictive maintenance one may find plenty of solutions for various applications. Unfortunately in the case of belt conveyors used in underground mining a list of possible solutions shrinks quickly. The reason is that they are specific mechanical systems—the typical conveyor is located in the mining tunnel and its length may vary between 100 and 1000 m. According to mining regulations, visual inspection of the conveyor route should be done before it will start the operation. On the other hand, since environmental conditions in mining tunnels are extremely harsh and the risk of accidents is high, there is a tendency to minimize human presence in the tunnels. In this paper, we propose a prototype of an inspection robot based on a UGV platform that could support maintenance staff during the inspection. At present, the robot is controlled by an operator using radio however, we plan to make it autonomous. Moreover, its support could be significant—the robot can “see” elements of the conveyor route (RGB camera) and can identify hot spots using infrared thermography. Moreover, the detected hot spots could be localized and its position can be stored together with both types of images. In parallel, it is possible to preview images in a real-time and stored data allow analysing state of conveyor system after the inspection mission. It is also important that due to radio control systems, an operator can stay in a safe place. Such a robot can be classified as a mobile monitoring system for spatially distributed underground infrastructure.

ACS Style

Jarosław Szrek; Jacek Wodecki; Ryszard Błażej; Radoslaw Zimroz. An Inspection Robot for Belt Conveyor Maintenance in Underground Mine—Infrared Thermography for Overheated Idlers Detection. Applied Sciences 2020, 10, 4984 .

AMA Style

Jarosław Szrek, Jacek Wodecki, Ryszard Błażej, Radoslaw Zimroz. An Inspection Robot for Belt Conveyor Maintenance in Underground Mine—Infrared Thermography for Overheated Idlers Detection. Applied Sciences. 2020; 10 (14):4984.

Chicago/Turabian Style

Jarosław Szrek; Jacek Wodecki; Ryszard Błażej; Radoslaw Zimroz. 2020. "An Inspection Robot for Belt Conveyor Maintenance in Underground Mine—Infrared Thermography for Overheated Idlers Detection." Applied Sciences 10, no. 14: 4984.