Wyniki wyszukiwania - BazTech

1

Research on integrated scheduling of equipment predictive maintenance and production decision based on physical modeling approach

Zhang Qinglei, Yang Lei, Duan Jianguo, Qin Jiyun, Zhou Ying

Eksploatacja i Niezawodność

|

2024

|

Vol. 26, no. 1

art. no. 175409

EN

Equipment performance deteriorates continuously during the production process, which makes it difficult to achieve the expected effect of production decisions made in advance. Predictive maintenance and production decisions integrated scheduling aim to rationalise maintenance activities. It has been extensively researched. However, past studies have assumed that faults obey a specific probability distribution based on historical data. It is difficult to analyse equipment that is brand new into service or has poor historical failure data. Thus, in this paper, we construct a twin model of a device based on a physical modelling approach and tune it to ensure high fidelity of the model. Degradation curves were created based on equipment characteristics and developed maintenance activities.Develop an integrated scheduling model for predictive maintenance and production decisions with the goal of minimising maximum processing time. An improved genetic algorithm is used to solve the problem optimally. Finally, apply a practical scenario to verify the effectiveness of the proposed method.

2

Fault Detection and Prediction for a Wood Chip Screw Conveyor

Henriques Lucas, Farinha Torres, Mendes Mateus

Eksploatacja i Niezawodność

|

2024

|

Vol. 26, no. 3

art. no. 189323

EN

Equipment maintenance is a key aspect to maximize its availability. Nowadays, much of the functioning and condition monitoring data from industrial machines is collected through sensors and stored, for off-line analysis. The present work focuses on data analysis of a screw conveyor of a biomass industry. The screw velocity and load were monitored and analysed, in order to detect and predict possible faults. A machine learning approach was used to detect anomalies, where different algorithms were tested with the data available, in order to train an anomaly classifier. The anomaly classifier was able to accurately identify most anomalies, based on historical data, temporal patterns and information of the maintenance interventions performed. The research carried out allowed to conclude that the Extra Trees Classifier algorithm achieved the best performance, among all algorithms tested, with 0.7974 F-score in the test set. The anomaly classifier has been shown to achieve remarkable accuracy in identifying anomalies. This research not only improves understanding of the performance of screw conveyors in biomass industries, but also highlights the practical utility of employing machine learning for proactive fault detection.

3

A fuzzy logic modelling of predictive maintenance in rotating machinery

Prommachan W., Surin P., Srinoi P., Pipathattakul M.

Archives of Materials Science and Engineering

|

2024

|

Vol. 126, nr 1

15--22

EN

Purpose: The study aims to investigate and assess the application of Fuzzy Logic to construct a predictive maintenance model for rotating machinery. Design/methodology/approach: The research uses a mixed approach, with both quantitative and qualitative approaches, and are four main steps: 1) surveying and analysing existing predictive maintenance techniques; 2) determining appropriate expert assessment criteria for predictive maintenance techniques; 3) vibration analysis by the experts; 4) evaluate the performance of rotating machinery with fuzzy logic. Findings: The result of the study will be used to build a rotating machinery predictive maintenance model, which is very similar to the traditional method. The obtained data showed that the efficiency of the rotating machinery and the vibration level were compliant with the standard ISO 10816-3. Thus, such data can be planned for maintenance to maximize benefit. Research limitations/implications: Future research should optimise the model and add additional modules for automatic data collection. The production monitoring system should help collect data by considering downtime, predicting the functional service life of rotating machinery, etc. Practical implications: The proposed model can be used in small water pumps in order to perform predictive maintenance. The conceptual framework was tested, particularly with rotating machinery. Originality/value: The fuzzy logic model is described as the fuzzy of a process with linguistics for greater clarity.

4

Application of regression models on the prediction of corrosion degradation of a crude oil distillation unit

Varbai Balázs, Wéber Richárd, Farkas Balázs, Danyi Péter, Krójer Antal, Locskai Roland, Bohács György, Hős Csaba

Advances in Materials Science

|

2024

|

Vol. 24, nr 1(79)

72--85

EN

The crude distillation unit is the most critical elements in the refining process. Moreover, most of the equipment in the distillation unit are made of general carbon steels. Data analysis models, machine learning techniques can predict corrosion degradation rates. We used Pearson’s correlation coefficient and multiple linear regression, to predict the impact of process parameters. Altogether, we have analysed 84 channels of technological parameters, and 22 different types of crude oils. Among the corrosion agents, the chloride content strongly affected the weight loss of coupons, where the highest coefficient was 0.68. The most influential parameter is found to be the pH value. Thus, an estimation method of the pH value is set up to predict the corrosion degradation rate. The regression correlation for estimating the pH value is 0.53 if the corrosion agents are not used, which can be improved to 0.76 if the corrosion agents are also used in the regression analysis.

5

Development of a hybrid predictive maintenance model

Wiercioch Jakub

Journal of KONBiN

|

2023

|

Vol. 53, iss. 2

141--157

EN

Progress in the field of technology and science enables the digitalization of manufacturing processes in the era of Industry 4.0. For this purpose, it uses tools which are referred to as the technological pillars of Industry 4.0. Simultaneously with the changes in the field of manufacturing, the interdisciplinary cooperation between production and machine maintenance planning is developing. Different types of predictive maintenance models are being developed in order to ensure the good condition of the machines, optimize maintenance costs and minimize machine downtime. The article presents the existing types of predictive maintenance and selected methods of machine diagnostics that can be used to analyze machines operating parameters. A hybrid model of predictive maintenance was developed and described. The proposed model is based on diagnostic data, historical data on failures and mathematical models. The use of complementary types of predictive maintenance in the hybrid model of predictive maintenance is particularly important in the case of high-performance production lines, where high quality of products and timeliness of orders are crucial.

PL

Postęp w dziedzinie techniki i nauki umożliwia digitalizację procesów wytwórczych w erze Przemysłu 4.0. Wykorzystuje w tym celu narzędzia, które określane są jako filary technologiczne Przemysłu 4.0. Równocześnie ze zmianami w dziedzinie produkcji rozwija się interdyscyplinarna współpraca między produkcją a planowaniem obsługi maszyn. W celu utrzymania maszyn w należytej kondycji oraz optymalizacji kosztów obsługi i czasów przestojów, rozwijają się różne typy predykcyjnych modeli obsługi. W artykule przedstawione zostały istniejące typy predykcyjnej obsługi oraz wybrane metody diagnostyki maszyn, które mogą zostać wykorzystane do badania parametrów pracy maszyn. Opracowany oraz opisany został hybrydowy model predykcyjnej obsługi, wykorzystujący dane diagnostyczne, dane historyczne dotyczące awarii oraz modele matematyczne. Wykorzystanie w hybrydowym modelu predykcyjnej obsługi uzupełniających się typów predykcyjnej obsługi jest szczególnie istotne w przypadku wysokowydajnych linii produkcyjnych, gdzie kluczowe są wysoka jakość wyrobów oraz terminowość wykonywanych zleceń.

6

Bariery i szanse implementacji utrzymania predykcyjnego budynków dworcowych

Gajzler Marcin

Inżynieria i Budownictwo

|

2023

|

R. 79, nr 11-12

678--683

PL

W artykule poruszono tematykę możliwości wdrożenia utrzymania predykcyjnego budynków dworcowych pod kątem zapewnienia ich niezawodności poprzez utrzymanie należytego stanu technicznego bez wprowadzania ograniczeń w korzystaniu. Omówiono podstawowe strategie utrzymania budynków, w tym zwrócono uwagę na obowiązki wynikające z rygorów prawnych. Na podstawie obserwacji przypadków budynków dworcowych stwierdzono brak wystarczającej infrastruktury sensorycznej do pełnej implementacji utrzymania predykcyjnego. Oprócz tego rozważono możliwości zastosowania rozwiązań umożliwiających implementację podejścia predykcyjnego. Jako alternatywę, w szczególności rozwiązanie pozwalające na akwizycję informacji i danych, przedstawiono własne podejście umożliwiające wspomaganie zarządzania technicznego na podstawie utrzymania predykcyjnego.

EN

The article discusses the possibility of implementing predictive maintenance of station buildings in terms of ensuring their reliability by maintaining proper technical condition without introducing restrictions on use. Basic building maintenance strategies were discussed, including the obligations arising from legal requirements. Based on observations of railway station buildings, it was found that there is no sufficient sensory infrastructure to fully implement predictive maintenance. In addition, potential possibilities of using solutions enabling the implementation of a predictive approach were considered. As an alternative, in particular a solution enabling the acquisition of information and data, an own approach was presented to support technical management based on predictive maintenance.

7

Hybrid feature selection and support vector machine framework for predicting maintenance failures

Tarik Mouna, Mniai Ayoub, Jebari Khalid

Applied Computer Science

|

2023

|

Vol. 19, no 2

112--124

EN

The main aim of predictive maintenance is to minimize downtime, failure risks and maintenance costs in manufacturing systems. Over the past few years, machine learning methods gained ground with diverse and successful applications in the area of predictive maintenance. This study shows that performing preprocessing techniques such as over¬sampling and feature selection for failure prediction is promising. For instance, to handle imbalanced data, the SMOTE-Tomek method is used. For feature selection, three different methods can be applied: Recursive Feature Elimination, Random Forest and Variance Threshold. The data considered in this paper for simulation are used in literature. They are used to measure aircraft engine sensors to predict engine failures, while the prediction algorithm used is a Support Vector Machine. The results show that classification accuracy can be significantly boosted by using the preprocessing techniques.

8

Utrzymanie predykcyjne w budownictwie - szanse i bariery implementacji

Gajzler Marcin

Materiały Budowlane

|

2022

|

nr 12

101--103

PL

W artykule zdefiniowano pojęcie utrzymania predykcyjnego. Na podstawie doświadczeń z jego stosowania w produkcji omówiono koncepcję utrzymania predykcyjnego w budownictwie. W tym celu wykorzystano założenia systemu wspomagania zarządzania technicznego. Dokonano analizy szans i barier systemu z punktu widzenia jego tworzenia i zastosowania. Na tej podstawie sformułowano wnioski wskazujące na zasadność budowy systemu wykorzystującego podejście utrzymania predykcyjnego i jego stosowanie.

EN

The article defines the concept of predictive maintenance. Basis of experience from its use in production, the concept of predictive maintenance in construction was discussed. For this purpose, the assumptions of technical management support system were used. An analysis of the opportunities and barriers of the system from the point of view of its creation and application was made. On this basis, conclusions were formulated indicating the reason of building a system using approach of predictive maintenance and its application.

9

Short and long forecast to implement predictive maintenance in a pulp industry

Rodrigues João Antunes, Farinha José Torres, Mendes Mateus, Mateus Ricardo, Cardoso António Marques

Eksploatacja i Niezawodność

|

2022

|

Vol. 24, no. 1

33--41

EN

Predictive maintenance is very important for effective prevention of failures in an industry. The present paper describes a case study where a wood chip pump system was analyzed, and a predictive model was proposed. An Ishikawa diagram and FMECA are used to identify possible causes for system failure. The Chip Wood has several sensors installed to monitor the working conditions and system state. The authors propose a variation of exponential smoothing technique for short time forecasting and an artificial neural network for long time forecasting. The algorithms were integrated into a dashboard for online condition monitoring, where the users are alerted when a variable is determined or predicted to get out of the expected range. Experimental results show prediction errors in general less than 10 %. The proposed technique may be of help in monitoring and maintenance of the asset, aiming at greater availability.

10

Double Layered Priority based Gray Wolf Algorithm (PrGWO-SK) for safety management in IoT network through anomaly detection

Agrawal Akhileshwar Prasad, Singh Nanhay

Eksploatacja i Niezawodność

|

2022

|

Vol. 24, no. 4

641--654

EN

For mitigating and managing risk failures due to Internet of Things (IoT) attacks, many Machine Learning (ML) and Deep Learning (DL) solutions have been used to detect attacks but mostly suffer from the problem of high dimensionality. The problem is even more acute for resource starved IoT nodes to work with high dimension data. Motivated by this problem, in the present work a priority based Gray Wolf Optimizer is proposed for effectively reducing the input feature vector of the dataset. At each iteration all the wolves leverage the relative importance of their leader wolves’ position vector for updating their own positions. Also, a new inclusive fitness function is hereby proposed which incorporates all the important quality metrics along with the accuracy measure. In a first, SVM is used to initialize the proposed PrGWO population and kNN is used as the fitness wrapper technique. The proposed approach is tested on NSL-KDD, DS2OS and BoTIoT datasets and the best accuracies are found to be 99.60%, 99.71% and 99.97% with number of features as 12,6 and 9 respectively which are better than most of the existing algorithms.

11

Analysis and prediction of leak detection in the low-pressure heat treatment of metal equipment

Kłos Sławomir, Patalas-Maliszewska Justyna, Bazel Michal

Eksploatacja i Niezawodność

|

2022

|

Vol. 24, no. 4

719--727

EN

The low-pressure heat treatment of metals enables the continuous improvement of the mechanical and plastic properties of products, such as hardness, abrasion resistance, etc. A significant problem related to the operation of vacuum furnaces for heat treatment is that they become unsealed during operation, resulting from the degradation of seals or the thermal expansion of the construction materials. Therefore, research was undertaken to develop a prediction model for detecting leaks in vacuum furnaces, the use of which will reduce the risk of degradation in the charge being processed. Unique experimental studies were carried out to detect leakages in a vacuum pit furnace, simulated using the ENV 116 reference slot. As a consequence, a prediction model for the detection of leaks in vacuum furnaces- which are used in the heat treatment of metals- was designed, using an artificial neural network. (93% for MLP 15-10-1) was developed. The model was implemented in a predictive maintenance system, in a real production company, as an element in the monitoring of the operation of vacuum furnaces.

12

Predictive management in the context of industry 4.0

Krynke Marek

Quality Production Improvement - QPI

|

2021

|

Vol. 3, Iss. 1

41--52

EN

The article presents information on the potential of Industry 4.0 in the field of maintenance. This work explores the potential and trends of predictive maintenance management in an industrial big data environment. The development of predictive maintenance, its technical challenges and in the context of Industry 4.0 was presented. In addition, a case study that illustrates how maintenance management and predictive maintenance can be applied to the maintenance of wind turbines is discussed.

13

Predictive maintenance techniques for wear reducing and elimination of equipment failures in hydrostatic drive systems

Marinescu Alexandru Daniel, Popescu Teodor Costinel, Nieśpiałowski Krzysztof, Popescu Ana-Maria Carla

Mining Machines

|

2021

|

no. 1

36--45

EN

Maintenance is a very important activity, which is necessary for the good operation of any technical system, even for the hydraulic drive systems. The predictive maintenance evaluates the state of technical performances of a system, by identifying the wear and thus avoiding the failures of the equipment. Using three methods of the predictive maintenance, namely infrared thermography, vibration analysis and oil analysis, the authors present their results of an experimental research on hydraulic pumps. The authors obtained thermograms, vibration spectra and diagrams of the oil contaminants which helped them indicate the proper or the malfunction of the studied pumps. Although they were only made on pumps, their investigations highlight the need of widely implementation of these modern and efficient methods in the industrial activities for the quick monitoring of the hydraulic machinery and equipment wear, before their failure occurs. Obviously, the goal is to have strong maintenance instruments in hydraulic drive systems diagnosis.

PL

Konserwacja to bardzo ważna czynność, niezbędna do prawidłowego działania każdego systemu technicznego, nawet hydraulicznych układów napędowych. Konserwacja predykcyjna ocenia stan wydajności technicznej systemu poprzez identyfikację zużycia i unikanie w ten sposób awarii urządzeń. Wykorzystując trzy nowoczesne metody konserwacji predykcyjnej, tj. termografię w podczerwieni, analizę drgań i analizę oleju, autorzy przedstawiają wyniki badań eksperymentalnych hydraulicznych pomp. Autorzy uzyskali termogramy, widma drgań i diagramy zanieczyszczeń olejowych, które pomogły wskazać prawidłową lub nieprawidłową pracę badanych pomp. Chociaż zostały wykonane tylko na pompach, ich badania podkreślają potrzebę szerokiego wdrażania tych nowoczesnych i efektywnych metod w działalności przemysłowej do szybkiego monitorowania zużycia maszyn i urządzeń hydraulicznych, zanim dojdzie do ich awarii. Oczywiście celem jest posiadanie mocnych narzędzi konserwacyjnych w diagnostyce hydraulicznych układów napędowych.

14

Monitoring temperatury przetwornika ultradźwiękowego w konserwacji predykcyjnej systemu ultradźwiękowego

Jarosz Dominika, Kiełbasiński Marcin, Gostkowski Henryk, Kluk Piotr

Elektronika : konstrukcje, technologie, zastosowania

|

2021

|

Vol. 62, nr 7

30--36

PL

W artykule przedstawiono koncepcję oraz praktyczną realizację konserwacji predykcyjnej opartej na zdalnym monitoringu temperatury przetwornika ultradźwiękowego dużej mocy, który jest jednym z głównych elementów przemysłowych systemów zgrzewania i wycinania ultradźwiękowego.

EN

This paper presents the concept and practical implementation of predictive maintenance based on remote temperature monitoring of a high-power ultrasonic transducer, which is one of the main components of industrial ultrasonic welding and cutting systems.

15

Predicting length of fatigue cracks by means of machine learning algorithms in the small-data regime

Badora Maciej, Sepe Marzia, Bielecki Marcin, Graziano Antonino, Szolc Tomasz

Eksploatacja i Niezawodność

|

2021

|

Vol. 23, no. 3

575--585

EN

In this paper several statistical learning algorithms are used to predict the maximal length of fatigue cracks based on a sample composed of 31 observations. The small-data regime is still a problem for many professionals, especially in the areas where failures occur rarely. The analyzed object is a high-pressure Nozzle of a heavy-duty gas turbine. Operating parameters of the engines are used for the regression analysis. The following algorithms are used in this work: multiple linear and polynomial regression, random forest, kernel-based methods, AdaBoost and extreme gradient boosting and artificial neural networks. A substantial part of the paper provides advice on the effective selection of features. The paper explains how to process the dataset in order to reduce uncertainty; thus, simplifying the analysis of the results. The proposed loss and cost functions are custom and promote solutions accurately predicting the longest cracks. The obtained results confirm that some of the algorithms can accurately predict maximal lengths of the fatigue cracks, even if the sample is small.

16

A data-driven predictive maintenance strategy based on accurate failure prognostics

Chen Chuang, Wang Cunsong, Lu Ningyun, Jiang Bin, Xing Yin

Eksploatacja i Niezawodność

|

2021

|

Vol. 23, no. 2

387--394

EN

Maintenance is fundamental to ensure the safety, reliability and availability of engineering systems, and predictive maintenance is the leading one in maintenance technology. This paper aims to develop a novel data-driven predictive maintenance strategy that can make appropriate maintenance decisions for repairable complex engineering systems. The proposed strategy includes degradation feature selection and degradation prognostic modeling modules to achieve accurate failure prognostics. For maintenance decision-making, the perfect time for taking maintenance activities is determined by evaluating the maintenance cost online that has taken into account of the failure prognostic results of performance degradation. The feasibility and effectiveness of the proposed strategy is confirmed using the NASA data set of aero-engines. Results show that the proposed strategy outperforms the two benchmark maintenance strategies: classical periodic maintenance and emerging dynamic predictive maintenance.

17

A new intelligentapproach in predictive maintenance of separation system

Marichal G. N., Ávila D., Hernández A., Padrón I.

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

|

2020

|

Vol. 14, no. 2

385--390

EN

Reducing contaminant emissions is an important task of any industry, included the maritime one. In fact, in April 2018, IMO (International Maritime Organization) adopted an Initial Strategy on reduction of Greenhouse gas (GHG) emissions from ships. An essential part responsible for producing these emissions is the diesel engine. For that reason vessels include separation systems for heavy fuel oils. The purpose of this work is to improve the predictive maintenance techniques incorporating new intelligent approaches. An analysis of vibrations of this separation system was made and their characteristics were used in a Genetic Neuro-Fuzzy System in order to design an intelligent maintenance based on condition monitoring. The achieved results show that the proposed method provides an improvement since it indicates if a maintenance operation is necessary before the schedule one or if it could be possible extend the next maintenance service.

18

Performance comparison of machine learning algotihms for predictive maintenance

Gęca Jakub

Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska

|

2020

|

T. 10, nr 3

32--35

EN

The consequences of failures and unscheduled maintenance are the reasons why engineers have been trying to increase the reliability of industrial equipment for years. In modern solutions, predictive maintenance is a frequently used method. It allows to forecast failures and alert about their possibility. This paper presents a summary of the machine learning algorithms that can be used in predictive maintenance and comparison of their performance. The analysis was made on the basis of data set from Microsoft Azure AI Gallery. The paper presents a comprehensive approach to the issue including feature engineering, preprocessing, dimensionality reduction techniques, as well as tuning of model parameters in order to obtain the highest possible performance. The conducted research allowed to conclude that in the analysed case, the best algorithm achieved 99.92% accuracy out of over 122 thousand test data records. In conclusion, predictive maintenance based on machine learning represents the future of machine reliability in industry.

PL

Skutki związane z awariami oraz niezaplanowaną konserwacją to powody, dla których od lat inżynierowie próbują zwiększyć niezawodność osprzętu przemysłowego. W nowoczesnych rozwiązaniach obok tradycyjnych metod stosowana jest również tzw. konserwacja predykcyjna, która pozwala przewidywać awarie i alarmować o możliwości ich powstawania. W niniejszej pracy przedstawiono zestawienie algorytmów uczenia maszynowego, które można zastosować w konserwacji predykcyjnej oraz porównanie ich skuteczności. Analizy dokonano na podstawie zbioru danych Azure AI Gallery udostępnionych przez firmę Microsoft. Praca przedstawia kompleksowe podejście do analizowanego zagadnienia uwzględniające wydobywanie cech charakterystycznych, wstępne przygotowanie danych, zastosowanie technik redukcji wymiarowości, a także dostrajanie parametrów poszczególnych modeli w celu uzyskania najwyższej możliwej skuteczności. Przeprowadzone badania pozwoliły wskazać najlepszy algorytm, który uzyskał dokładność na poziomie 99,92%, spośród ponad 122 tys. rekordów danych testowych. Na podstawie tego można stwierdzić, że konserwacja predykcyjna prowadzona w oparciu o uczenie maszynowe stanowi przyszłość w zakresie podniesienia niezawodności maszyn w przemyśle.

19

Predicting motor oil condition using artificial neural networks and principal component analysis

Rodrigues Joao, Costa Ines, Farinha J. Torres, Mendes Mateus, Margalho Luis

Eksploatacja i Niezawodność

|

2020

|

Vol. 22, no. 3

440--448

EN

The safety and performance of engines such as Diesel, gas or even wind turbines depends on the quality and condition of the lubricant oil. Assessment of engine oil condition is done based on more than twenty variables that have, individually, variations that depend on the engines’ behaviour, type and other factors. The present paper describes a model to automatically classify the oil condition, using Artificial Neural Networks and Principal Component Analysis. The study was done using data obtained from two passenger bus companies in a country of Southern Europe. The results show the importance of each variable monitored for determining the ideal time to change oil. In many cases, it may be possible to enlarge intervals between maintenance interventions, while in other cases the oil passed the ideal change point.

PL

Bezpieczeństwo i wydajność silników takich, jak silniki Diesla czy gazowe, a nawet turbiny wiatrowe, zależą od jakości i stanu oleju smarowego. Stanu oleju silnikowego ocenia się na podstawie ponad dwudziestu zmiennych, z których każda ulega wahaniom w zależności od typu i zachowania silnika oraz innych czynników. W niniejszym artykule opisano model, który pozwala na automatyczną klasyfikację stanu oleju, z wykorzystaniem sztucznych sieci neuronowych i analizy składowych głównych. Badania przeprowadzono na podstawie danych uzyskanych od dwóch przewoźników pasażerskich działających na terenie jednego z krajów położonych na południu Europy. Wyniki pokazują, że każda z monitorowanych zmiennych ma znaczenie dla określenia idealnego czasu na wymianę oleju. Podczas gdy w wielu przypadkach w badanych przedsiębiorstwach możliwe było zwiększenie odstępów czasowych między działaniami konserwacyjnymi, w innych, idealny moment wymiany oleju został przekroczony.

20

Prosta metoda detekcji cykli odstawy urobku samojezdnych ładowarek łyżkowych

Stefaniak Paweł, Skoczylas Artur, Koperska Wioletta

Cuprum : czasopismo naukowo-techniczne górnictwa rud

|

2020

|

nr 2

33--46

PL

W górnictwie podziemnym rud miedzi transport poziomy urobku realizowany jest za pomocą samojezdnych maszyn załadowczo-odstawczych. Przykładowo, w kopalniach rud miedzi KGHM Polska Miedź S.A., gdzie stosowany jest komorowo-filarowy system eksploatacji złoża, odstawa urobku realizowana jest głównie przy współpracy ładowarek łyżkowych i wozów odstawczych. W przypadku krótszych tras odstawy proces ogranicza się już tylko do ładowarek. Obecnie obserwuje się globalny trend w zakresie rozwoju predykcyjnego utrzymania ruchu maszyn górniczych, nawigacji, jak również optymalizacji produkcji z wykorzystaniem przemysłowego internetu rzeczy (ang. Industrial Internet of Things, IIoT). Rozwój analityki w tym zakresie wymaga niestety pełnego wglądu w przebieg pracy maszyny w wyrobiskach górniczych, zapewniający prowadzenie wielowymiarowych analiz do szerszego zrozumienia kontekstów eksploatacji maszyny. W artykule przedstawiono metodę identyfikacji cykli odstawy, jak również składowych podprocesów, realizowanych w każdym pojedynczym cyklu. Zaproponowany algorytm bazuje na użyciu operacji splotu, w celu detekcji skoków obserwowanych w sygnale ciśnienia z siłownika układu hydraulicznego wychyłu łyżki.

EN

In underground mining of cooper ores, horizontal transport of material is performed using self-propelled machines, especially Load-Haul-Dump machines. For example, in KGHM Polska Miedź S.A. underground mines, where room-and-pillar system is used to deposit exploitation, the haulage process is provided by wheel loaders and haul trucks with suitably adjusted operation configuration. In case of shorter haulage routes, only wheel loaders take part in haulage process. Currently, there is observed a global tendency reliant on develop predictive maintenance as well as navigation or production optimization using Industrial Internet of Things (IIoT). Unfortunately, analytics development in this domain requires full insight into machine’s workflow in mining excavations and multivariate analysis in order widely understanding of machine operating contexts. In this article, a quick method to haulage cycle identification on example of wheel loader has been proposed. Developed algorithm is based on hydraulic pressure signal segmentation which provides to recognize loading operation, haulage and return of machine to mining face after unloading material in dumping point. The method is based on smooth hydraulic pressure signal in order to reduce signal interference but introduce to apply a convolution of smoothed signal with inverted step function. The advantage of the algorithm is its simplicity, high accuracy, robustness and low algorithmic complexity.