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Fast-growing methods of automatic data acquisition allow for collecting various types of data from the production process. This entails developing methods that are able to process vast amounts of data, providing generalised knowledge about the analysed process. Appropriate use of this knowledge can be the basis for decision-making, leading to more effective use of the company’s resources. This article presents the approach for data analysis aimed at determining the operating states of a wheel loader and the place where it operates based on the recorded data. For this purpose, we have used several methods, e.g., for clustering and classification, namely: DBSCAN, CART, C5.0. Our approach has allowed for the creation of decision rules that recognise the operating states of the machine. In this study, we have taken into account the GPS signal readings, and thanks to this, we have indicated the differences in machine operation within the designated states in the open pit and at the mine base area. In this paper, we present the characteristics of the selected clusters corresponding to the machine operation states and emphasise the differences in the context of the operation area. The knowledge obtained in this study allows for determining the states based on only a few selected most essential parameters, even without consideration of the coordinates of the machine’s workplace. Our approach enables a significant acceleration of subsequent analyses, e.g., analysis of the machine states structure, which may be helpful in the optimisation of its use.
Paulina Gackowiec; Edyta Brzychczy; Marek Kęsek. Enhancement of Machinery Activity Recognition in a Mining Environment with GPS Data. Energies 2021, 14, 3422 .
AMA StylePaulina Gackowiec, Edyta Brzychczy, Marek Kęsek. Enhancement of Machinery Activity Recognition in a Mining Environment with GPS Data. Energies. 2021; 14 (12):3422.
Chicago/Turabian StylePaulina Gackowiec; Edyta Brzychczy; Marek Kęsek. 2021. "Enhancement of Machinery Activity Recognition in a Mining Environment with GPS Data." Energies 14, no. 12: 3422.
The underground mining process can be analysed with a data-oriented or process-oriented approach. The first of them is popularand wide known as data mining while the second is still not often used in the conditions of the mining companies. The aim of thispaper is an overview of data mining and process mining applications in an underground mining domain and an investigation ofthe most popular analytic techniques used in the defined analytic perspectives (“Diagnostics and machinery”, “Geomechanics”,“Hazards”, “Mine planning and safety”). In the paper two research questions are formulated: RQ1: What are the most populardata mining/process mining tasks in the analysis of the underground mining process? and RQ2: What are the most popular datamining/process mining techniques applied in the multi-perspective analysis of the underground mining process? In the paper sixty--two published articles regarding to data mining tasks and analytic techniques in the mentioned domain have been analysed. Theresults show that predominatingly predictive tasks were formulated with regard to the analysed phenomena, with strong overrepresentationof classification task. The most frequent data mining algorithms is comprised of the following: artificial neural networks,decision trees, rule induction and regression. Only a few applications of process mining in analysis of the underground miningprocess have been found – they were briefly described in the paper.
Edyta Brzychczy. An Overview of Data Mining and Process Mining Applications in Underground Mining. Inżynieria Mineralna 2021, 1, 1 .
AMA StyleEdyta Brzychczy. An Overview of Data Mining and Process Mining Applications in Underground Mining. Inżynieria Mineralna. 2021; 1 (1):1.
Chicago/Turabian StyleEdyta Brzychczy. 2021. "An Overview of Data Mining and Process Mining Applications in Underground Mining." Inżynieria Mineralna 1, no. 1: 1.
Conformance checking is a process mining technique that compares a process model with an event log of the same process to check whether the current execution stored in the log conforms to the model and vice versa. This paper deals with the conformance checking of a longwall shearer process. The approach uses place-transition Petri nets with inhibitor arcs for modeling purposes. We use event log files collected from a few coal mines located in Poland by Famur S.A., one of the global suppliers of coal mining machines. One of the main advantages of the approach is the possibility for both offline and online analysis of the log data. The paper presents a detailed description of the longwall process, an original formal model we developed, selected elements of the approach’s implementation and the results of experiments.
Marcin Szpyrka; Edyta Brzychczy; Aneta Napieraj; Jacek Korski; Grzegorz J. Nalepa. Conformance Checking of a Longwall Shearer Operation Based on Low-Level Events. Energies 2020, 13, 6630 .
AMA StyleMarcin Szpyrka, Edyta Brzychczy, Aneta Napieraj, Jacek Korski, Grzegorz J. Nalepa. Conformance Checking of a Longwall Shearer Operation Based on Low-Level Events. Energies. 2020; 13 (24):6630.
Chicago/Turabian StyleMarcin Szpyrka; Edyta Brzychczy; Aneta Napieraj; Jacek Korski; Grzegorz J. Nalepa. 2020. "Conformance Checking of a Longwall Shearer Operation Based on Low-Level Events." Energies 13, no. 24: 6630.
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.
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 StylePiotr 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 StylePiotr 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.
The sustainable development of an organisation requires a holistic approach to the evaluation of an enterprise’s goals and activities. The essential means enabling an organisation to achieve goals are business processes. Properly managed, business processes are a source of revenue and become an implementation of business strategy. The critical elements in process management in an enterprise are process monitoring and control. It is therefore essential to identify the Key Performance Indicators (KPIs) that are relevant to the analysed processes. Process monitoring can be performed at various levels of management, as well as from different perspectives: operational, financial, security, or maintenance. Some of the indicators known from other fields (such as personnel management, finance, or lean manufacturing) can be used in mining. However, the operational mining processes require a definition of specific indicators, especially in the context of increasing the productivity of mining machines and the possibility of using sensor data from machines and devices. The article presents a list of efficiency indicators adjusted to the specifics and particular needs of the mining industry resulting from the Industry 4.0 concept, as well as sustainable business performance. Using the conducted research and analysis, a list of indicators has been developed concerning person groups, which may serve as a benchmark for mining industry entities. The presented proposal is a result of work conducted in the SmartHUB project, which aims to create an Industrial Internet of Things (IIoT) platform that will support process management in the mining industry.
Paulina Gackowiec; Marta Podobińska-Staniec; Edyta Brzychczy; Christopher Kühlbach; Toyga Özver. Review of Key Performance Indicators for Process Monitoring in the Mining Industry. Energies 2020, 13, 5169 .
AMA StylePaulina Gackowiec, Marta Podobińska-Staniec, Edyta Brzychczy, Christopher Kühlbach, Toyga Özver. Review of Key Performance Indicators for Process Monitoring in the Mining Industry. Energies. 2020; 13 (19):5169.
Chicago/Turabian StylePaulina Gackowiec; Marta Podobińska-Staniec; Edyta Brzychczy; Christopher Kühlbach; Toyga Özver. 2020. "Review of Key Performance Indicators for Process Monitoring in the Mining Industry." Energies 13, no. 19: 5169.
This article tackles the problem of checking the conformance between a business process model and the data produced by its execution in cases where the data is not given as an event log, but as a set of time series containing the evolution of the variables involved in the process. Tasks in the process model are no longer restricted to the occurrence of a single event, and instead they can be expressed as a set of temporal conditions about the values of the variables in the log. This causes a paradigm shift in conformance checking (and process mining at a more general level), and because of this, the formalization of both the data, the process model and the algorithms are here redesigned and adapted for this challenging perspective. To illustrate the effectiveness of our approach, an experimental evaluation on a real-world time series log is carried out, highlighting the benefits of this change of paradigm.
Victor Rodriguez-Fernandez; Agnieszka Trzcionkowska; Antonio Gonzalez-Pardo; Edyta Brzychczy; Grzegorz J. Nalepa; David Camacho. Conformance Checking for Time-Series-Aware Processes. IEEE Transactions on Industrial Informatics 2020, 17, 871 -881.
AMA StyleVictor Rodriguez-Fernandez, Agnieszka Trzcionkowska, Antonio Gonzalez-Pardo, Edyta Brzychczy, Grzegorz J. Nalepa, David Camacho. Conformance Checking for Time-Series-Aware Processes. IEEE Transactions on Industrial Informatics. 2020; 17 (2):871-881.
Chicago/Turabian StyleVictor Rodriguez-Fernandez; Agnieszka Trzcionkowska; Antonio Gonzalez-Pardo; Edyta Brzychczy; Grzegorz J. Nalepa; David Camacho. 2020. "Conformance Checking for Time-Series-Aware Processes." IEEE Transactions on Industrial Informatics 17, no. 2: 871-881.
This paper investigates the application of process mining methodology on the processes of a mobile asset in mining operations as a means of identifying opportunities to improve the operational efficiency of such. Industry 4.0 concepts with related extensive digitalization of industrial processes enable the acquisition of a huge amount of data that can and should be used for improving processes and decision-making. Utilizing this data requires appropriate data processing and data analysis schemes. In the processing and analysis stage, most often, a broad spectrum of data mining algorithms is applied. These are data-oriented methods and they are incapable of mapping the cause-effect relationships between process activities. However, in this scope, the importance of process-oriented analytical methods is increasingly emphasized, namely process mining (PM). PM techniques are a relatively new approach, which enable the construction of process models and their analytics based on data from enterprise IT systems (data are provided in the form of so-called event logs). The specific working environment and a multitude of sensors relevant for the working process causes the complexity of mining processes, especially in underground operations. Hence, an individual approach for event log preparation and gathering contextual information to be utilized in process analysis and improvement is mandatory. This paper describes the first application of the concept of PM to investigate the normal working process of a roof bolter, operating in an underground mine. By applying PM, the irregularities of the operational scheme of this mobile asset have been identified. Some irregularities were categorized as inefficiencies that are caused by either failure of machinery or suboptimal utilization of the same. In both cases, the results achieved by applying PM to the activity log of the mobile asset are relevant for identifying the potential for improving the efficiency of the overall working process.
Edyta Brzychczy; Paulina Gackowiec; Mirko Liebetrau. Data Analytic Approaches for Mining Process Improvement—Machinery Utilization Use Case. Resources 2020, 9, 17 .
AMA StyleEdyta Brzychczy, Paulina Gackowiec, Mirko Liebetrau. Data Analytic Approaches for Mining Process Improvement—Machinery Utilization Use Case. Resources. 2020; 9 (2):17.
Chicago/Turabian StyleEdyta Brzychczy; Paulina Gackowiec; Mirko Liebetrau. 2020. "Data Analytic Approaches for Mining Process Improvement—Machinery Utilization Use Case." Resources 9, no. 2: 17.
Industrial event logs, especially from low-level monitoring systems, very often have no suitable structure for process-oriented analysis techniques (i.e. process mining). Such a structure should contain three main elements for process analysis, namely: timestamp of activity, activity name and case id. In this paper we present example data from a low-level machinery monitoring system used in underground mine, which can be used for the modelling and analysis of the mining process carried out in a longwall face. Raw data from the mentioned machinery monitoring system needs significant pre-processing due to the creation of a suitable event log for process mining purposes, because case id and activities are not given directly in the data. In our previous works we presented a mixture of supervised and unsupervised data mining techniques as well as domain knowledge as methods for the activity/process stages discovery in the raw data. In this paper we focus on case id identification with an heuristic approach. We summarize our experiences in this area showing the problems of real industrial data sets.
Edyta Brzychczy; Agnieszka Trzcionkowska. Creation of an Event Log from a Low-Level Machinery Monitoring System for Process Mining Purposes. Privacy Enhancing Technologies 2018, 54 -63.
AMA StyleEdyta Brzychczy, Agnieszka Trzcionkowska. Creation of an Event Log from a Low-Level Machinery Monitoring System for Process Mining Purposes. Privacy Enhancing Technologies. 2018; ():54-63.
Chicago/Turabian StyleEdyta Brzychczy; Agnieszka Trzcionkowska. 2018. "Creation of an Event Log from a Low-Level Machinery Monitoring System for Process Mining Purposes." Privacy Enhancing Technologies , no. : 54-63.
Over the last decades, number of embedded and portable computer systems for monitoring of activities of miners and underground environmental conditions that have been developed has increased. However, their potential in terms of computing power and analytic capabilities is still underestimated. In this paper we elaborate on the recent examples of the use of wearable devices in mining industry. We identify challenges for high level monitoring of mining personnel with the use of mobile and wearable devices. To address some of them, we propose solutions based on our recent works, including context-aware data acquisition framework, physiological data acquisition from wearables, methods for incomplete and imprecise data handling, intelligent data processing and reasoning module, hybrid localization using semantic maps, and adaptive power management. We provide a basic use case to demonstrate the usefulness of this approach.
Grzegorz J. Nalepa; Edyta Brzychczy; Szymon Bobek. On the Opportunities for Using Mobile Devices for Activity Monitoring and Understanding in Mining Applications. Privacy Enhancing Technologies 2018, 75 -83.
AMA StyleGrzegorz J. Nalepa, Edyta Brzychczy, Szymon Bobek. On the Opportunities for Using Mobile Devices for Activity Monitoring and Understanding in Mining Applications. Privacy Enhancing Technologies. 2018; ():75-83.
Chicago/Turabian StyleGrzegorz J. Nalepa; Edyta Brzychczy; Szymon Bobek. 2018. "On the Opportunities for Using Mobile Devices for Activity Monitoring and Understanding in Mining Applications." Privacy Enhancing Technologies , no. : 75-83.
In the paper we address the challenge of applying process mining techniques for discovering models of underground mining operations based on a sensor data. The paper presents practical approach of creation an event log based on industrial sensors data gathered in an underground mine monitoring systems. The proposed approach enables to generate event logs at different generalization levels based on several numbers of discovered stages of devices performance. For discovering process stages data mining techniques such as exploratory data analysis, clustering and classification have been applied. Created event log has been used in one of the process mining tasks - process model discovery.
Agnieszka Trzcionkowska; Edyta Brzychczy. Practical Aspects of Event Logs Creation for Industrial Process Modelling. Multidisciplinary Aspects of Production Engineering 2018, 1, 77 -83.
AMA StyleAgnieszka Trzcionkowska, Edyta Brzychczy. Practical Aspects of Event Logs Creation for Industrial Process Modelling. Multidisciplinary Aspects of Production Engineering. 2018; 1 (1):77-83.
Chicago/Turabian StyleAgnieszka Trzcionkowska; Edyta Brzychczy. 2018. "Practical Aspects of Event Logs Creation for Industrial Process Modelling." Multidisciplinary Aspects of Production Engineering 1, no. 1: 77-83.
The article presents probabilistic modeling of mining production in hard coal mines with the application of stochastic networks. The paper includes basic definitions and assumptions of the evolved method and the systematic enabling the description of network activities. As a results of calculations according to the mathematical model the paper presents probability distributions of output for each production flow and for a whole mine in each of the calculated variants. To estimate the risk of output results the standard deviation of the distribution was assumed. Probability distributions form the basis of the optimization procedure. An application of the developed method in a coal mine is presented.
Edyta Brzychczy. Probabilistic Modeling of Mining Production in an Underground Coal Mine. Advances in Intelligent Systems and Computing 2018, 655 -667.
AMA StyleEdyta Brzychczy. Probabilistic Modeling of Mining Production in an Underground Coal Mine. Advances in Intelligent Systems and Computing. 2018; ():655-667.
Chicago/Turabian StyleEdyta Brzychczy. 2018. "Probabilistic Modeling of Mining Production in an Underground Coal Mine." Advances in Intelligent Systems and Computing , no. : 655-667.
In the paper we address possibility of industrial process analysis based on sensor data with the use of process-oriented analytic techniques. We propose in this area usage of process mining techniques. Important issue in the context of usefulness of industrial sensor data gathered in the monitoring systems for process analysis is proper level of abstraction of an event log. We present our approach requiring creation of high-level event logs based on low-level events from longwall monitoring system in order to model and analyse the mining process in an underground mine. We use combination of unsupervised data mining techniques as well as domain knowledge to discover stages in an example process and create an event logs for further process analysis.
Edyta Brzychczy; Agnieszka Trzcionkowska. Process-Oriented Approach for Analysis of Sensor Data from Longwall Monitoring System. Advances in Intelligent Systems and Computing 2018, 611 -621.
AMA StyleEdyta Brzychczy, Agnieszka Trzcionkowska. Process-Oriented Approach for Analysis of Sensor Data from Longwall Monitoring System. Advances in Intelligent Systems and Computing. 2018; ():611-621.
Chicago/Turabian StyleEdyta Brzychczy; Agnieszka Trzcionkowska. 2018. "Process-Oriented Approach for Analysis of Sensor Data from Longwall Monitoring System." Advances in Intelligent Systems and Computing , no. : 611-621.
W artykule przedstawiono możliwość wsparcia identyfikacji procesów wymagających poprawy w organizacji z wykorzystaniem wybranych technik eksploracji procesów. Scharakteryzowano istotę reengineeringu procesów w organizacji oraz podstawowe zadania eksploracji procesów. Następnie omówiono wybrane sposoby identyfikacji procesów wymagających przeprojektowania w celu optymalizacji toku pracy i produktywności organizacji. W pracy zaprezentowano przykład analizy procesu obsługi zgłoszeń w firmie motoryzacyjnej. Dla zbioru danych obejmującego 6242 zdarzenia w 1396 sprawach realizowanych w wybranym oddziale zbudowano model procesu oraz dokonano identyfikacji problematycznych miejsc w procesie z wykorzystaniem programu ProM. W wyniku badań stwierdzono, iż najdłuższy czas oczekiwania w procesie dotyczy czynności In Progress (84 dni), przy średnim czasie realizacji sprawy 5,87 miesięcy. Analizowany proces charakteryzuje się również dużą zmiennością, co może mieć istotne znaczenie dla dalszych decyzji w zakresie konieczności jego usprawnienia.
Aneta Napieraj; Edyta Brzychczy; Marta Sukiennik. WSPARCIE IDENTYFIKACJI PROCESÓW WYMAGAJĄCYCH POPRAWY W PRZEDSIĘBIORSTWIE. Przegląd Organizacji 2018, 29 -35.
AMA StyleAneta Napieraj, Edyta Brzychczy, Marta Sukiennik. WSPARCIE IDENTYFIKACJI PROCESÓW WYMAGAJĄCYCH POPRAWY W PRZEDSIĘBIORSTWIE. Przegląd Organizacji. 2018; (1):29-35.
Chicago/Turabian StyleAneta Napieraj; Edyta Brzychczy; Marta Sukiennik. 2018. "WSPARCIE IDENTYFIKACJI PROCESÓW WYMAGAJĄCYCH POPRAWY W PRZEDSIĘBIORSTWIE." Przegląd Organizacji , no. 1: 29-35.
Kultura organizacji to pojęcie, które w dzisiejszych czasach coraz częściej jest dostrzegane w aspekcie organizacji pracy. W przypadku prac zespołowych w organizacjach, ważnym elementem są relacje pomiędzy zasobami ludzkimi a czynnościami i zadaniami, jakie poszczególne zespoły mają do wykonania. W artykule opisano wybrane metody socjometryczne, które mogą przyczyniać się do usprawniania pracy zespołów. Przedstawiono również przykład analizy pracy zespołów w wybranej organizacji, wykorzystując techniki eksploracji procesów. Na podstawie wykonanych socjogramów zidentyfikowano przykładowych pracowników, którzy są bardzo cenni dla organizacji, jak i zespoły o złożonych kompetencjach. Odpowiednia interpretacja i wykorzystanie wyników prezentowanych analiz, mogą przyczyniać się do poprawy zarówno jakości pracy w organizacji, jak i samej organizacji pracy.
Marta Sukiennik; Edyta Brzychczy; Aneta Napieraj. ANALIZA PRACY ZESPOŁÓW W ORGANIZACJI Z WYKORZYSTANIEM TECHNIK EKSPLORACJI PROCESÓW. Przegląd Organizacji 2017, 46 -52.
AMA StyleMarta Sukiennik, Edyta Brzychczy, Aneta Napieraj. ANALIZA PRACY ZESPOŁÓW W ORGANIZACJI Z WYKORZYSTANIEM TECHNIK EKSPLORACJI PROCESÓW. Przegląd Organizacji. 2017; (11):46-52.
Chicago/Turabian StyleMarta Sukiennik; Edyta Brzychczy; Aneta Napieraj. 2017. "ANALIZA PRACY ZESPOŁÓW W ORGANIZACJI Z WYKORZYSTANIEM TECHNIK EKSPLORACJI PROCESÓW." Przegląd Organizacji , no. 11: 46-52.
In the current market situation, mining companies are faced with the necessity to take actions to improve the efficiency of the mining process. Some of these actions enforce a centralization of activities in the field of deposit economy and planning of mining operations in these companies. In the planning process with such scope the large knowledge of designers is required, which could be additionally supported by a knowledge base, supplied by information and data obtained during the completion of mining works, which also allows for use of the expert knowledge of other organizational units of the mine or the company. The paper presents an original expert system for mining works planning in the underground hard coal mines (MinePlanEx). The aim of the developed system is to support the designers of production planning in hard coal mines within the scope of: equipment selection, mining machinery combining into equipment sets and determining characteristic curves regarding the production results in the planned excavations. Knowledge of the system is represented by the rules selected with the chosen data mining techniques (association rules and classification trees) and obtained from experts. The first part of the paper presents a knowledge base, knowledge acquisition module and inference module which are the main components of the system. The second part contains an example of system operation.
Edyta Brzychczy; Marek Kęsek; Aneta Napieraj; Roman Magda. An expert system for underground coal mine planning. Gospodarka Surowcami Mineralnymi 2017, 33, 113 -127.
AMA StyleEdyta Brzychczy, Marek Kęsek, Aneta Napieraj, Roman Magda. An expert system for underground coal mine planning. Gospodarka Surowcami Mineralnymi. 2017; 33 (2):113-127.
Chicago/Turabian StyleEdyta Brzychczy; Marek Kęsek; Aneta Napieraj; Roman Magda. 2017. "An expert system for underground coal mine planning." Gospodarka Surowcami Mineralnymi 33, no. 2: 113-127.
Edyta Brzychczy; Aneta Napieraj; Marta Sukiennik. Evolutionary optimisation of coal production in underground mines …. Scientific Papers of Silesian University of Technology. Organization and Management Series 2017, 2017, 61 -76.
AMA StyleEdyta Brzychczy, Aneta Napieraj, Marta Sukiennik. Evolutionary optimisation of coal production in underground mines …. Scientific Papers of Silesian University of Technology. Organization and Management Series. 2017; 2017 (100):61-76.
Chicago/Turabian StyleEdyta Brzychczy; Aneta Napieraj; Marta Sukiennik. 2017. "Evolutionary optimisation of coal production in underground mines …." Scientific Papers of Silesian University of Technology. Organization and Management Series 2017, no. 100: 61-76.
Edyta Brzychczy; Agnieszka Trzcionkowska. NEW POSSIBILITIES FOR PROCESS ANALYSIS IN AN UNDERGROUND MINE. Scientific Papers of Silesian University of Technology. Organization and Management Series 2017, 2017, 13 -25.
AMA StyleEdyta Brzychczy, Agnieszka Trzcionkowska. NEW POSSIBILITIES FOR PROCESS ANALYSIS IN AN UNDERGROUND MINE. Scientific Papers of Silesian University of Technology. Organization and Management Series. 2017; 2017 (111):13-25.
Chicago/Turabian StyleEdyta Brzychczy; Agnieszka Trzcionkowska. 2017. "NEW POSSIBILITIES FOR PROCESS ANALYSIS IN AN UNDERGROUND MINE." Scientific Papers of Silesian University of Technology. Organization and Management Series 2017, no. 111: 13-25.
This article presents examples of solutions supporting the design of certain elements of the mining process in coal mines. The focus is on two fuzzy systems: the first supports the selection of equipment for longwall faces (FSES); and the second supports the estimation of production results (FSOE). System FSES generates proposals for equipment in designed longwall faces. The module of fuzzing in this system enables a fuzzing operation for the following quantitative variables: longwall length; longwall height; longitudinal and crosswise incline of the longwall, workability of the coal and thickness of rock vein in a given section of the longwall. The knowledge base includes over 100 fuzzy rules indicating possible options for equipment under specified site conditions. After a proposal of equipment is generated, it is then possible to insert the values obtained into the second system FSOE, which estimates output for a given shift time using the chosen parameters. The module of fuzzing in system FSOE includes 9 variables, which are crucial in determining shift output for the given longwall face. The knowledge base in this system contains over 2000 rules. As a result of the operation of both systems, the designer receives both a proposal of equipment for the designed longwall face and the size of shift output under the given conditions. Operation of the two systems has been presented using a case study.
Edyta Brzychczy; Marek Kęsek; Aneta Napieraj; Marta Sukiennik. The Use of Fuzzy Systems in the Designing of Mining Process in Hard Coal Mines. Archives of Mining Sciences 2014, 59, 741 -760.
AMA StyleEdyta Brzychczy, Marek Kęsek, Aneta Napieraj, Marta Sukiennik. The Use of Fuzzy Systems in the Designing of Mining Process in Hard Coal Mines. Archives of Mining Sciences. 2014; 59 (3):741-760.
Chicago/Turabian StyleEdyta Brzychczy; Marek Kęsek; Aneta Napieraj; Marta Sukiennik. 2014. "The Use of Fuzzy Systems in the Designing of Mining Process in Hard Coal Mines." Archives of Mining Sciences 59, no. 3: 741-760.
A new calculation tool allowing the user to model and optimise production in underground coal mines is presented in the paper. Hard coal plays an essential role in the world economics. Its consumption in 2011 increased faster than for any other energy produced from raw materials (excluding renewable sources). Achieving the planned output levels depends, first of all, on results acquired in the mining design process. It is now possible to support this design process with modern tools, which can significantly increase future mine efficiency as well as the quality of the raw material extracted. The calculation service for optimisation of the production in underground coal mines (OPTiCoalMine) is one of such tools.
Edyta Brzychczy. A Modern Tool for Modelling and Optimisation of Production in Underground Coal Mine. Transactions on Petri Nets and Other Models of Concurrency XV 2014, 317 -334.
AMA StyleEdyta Brzychczy. A Modern Tool for Modelling and Optimisation of Production in Underground Coal Mine. Transactions on Petri Nets and Other Models of Concurrency XV. 2014; ():317-334.
Chicago/Turabian StyleEdyta Brzychczy. 2014. "A Modern Tool for Modelling and Optimisation of Production in Underground Coal Mine." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 317-334.
In the paper a problem of diagnostic data classification is discussed. The classic condition monitoring approach requires two examples of machines: one in a good and one in a bad condition. From the industrial perspective such a requirement is often very difficult to fulfill, especially in the case of machines with an unique design. To overcome it, we proposed to use the Artificial Immune System (AIS) based approach to classify multidimensional diagnostic data. AIS allows to recognize a change of the machine condition based on a training phase using the dataset related to a good condition. To validate the proposed procedure and assess efficiency of the condition recognition, an extra data set from another machine (of the same type) in a bad condition was used. In the paper several key issues related to the selection of parameters have been discussed.
Edyta Brzychczy; Piotr Lipiński; Radoslaw Zimroz; Patryk Filipiak. Artificial Immune Systems for Data Classification in Planetary Gearboxes Condition Monitoring. Proceedings of the 2nd Annual International Conference on Material, Machines and Methods for Sustainable Development (MMMS2020) 2013, 235 -247.
AMA StyleEdyta Brzychczy, Piotr Lipiński, Radoslaw Zimroz, Patryk Filipiak. Artificial Immune Systems for Data Classification in Planetary Gearboxes Condition Monitoring. Proceedings of the 2nd Annual International Conference on Material, Machines and Methods for Sustainable Development (MMMS2020). 2013; ():235-247.
Chicago/Turabian StyleEdyta Brzychczy; Piotr Lipiński; Radoslaw Zimroz; Patryk Filipiak. 2013. "Artificial Immune Systems for Data Classification in Planetary Gearboxes Condition Monitoring." Proceedings of the 2nd Annual International Conference on Material, Machines and Methods for Sustainable Development (MMMS2020) , no. : 235-247.