Wyniki wyszukiwania - BazTech

1

Narzędzie SOUNDLAB AI - uczenie maszynowe do przewidywania wartości izolacyjności akustycznej

Kraus Michael Anton, Drass Michael, Riedel Henrik, Bischof Rafael, Schmeiser Leon, Stelzer Ingo

Świat Szkła

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2024

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R. 29, nr 3

42--44

2

Diagnosis of inter-turn short circuit fault in IPMSMs based on the combined use of greedy tracking and random forest

Wang Jianping, Ma Jian, Meng Dean, Zhao Xuan, Zhang Kai, Liu Qiquan, Xu Kejie

Bulletin of the Polish Academy of Sciences. Technical Sciences

|

2024

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Vol. 72, nr 2

art. no. e148943

EN

Inter-turn short circuit (ITSC) is a frequent fault of interior permanent magnet synchronous motors (IPMSM). If ITSC faults are not promptly monitored, it may result in secondary faults or even cause extensive damage to the entire motor. To enhance the reliability of IPMSMs, this paper introduces a fault diagnosis method specifically designed for identifying ITSC faults in IPMSMs. The sparse coefficients of phase current and torque are solved by clustering shrinkage stage orthogonal matching tracking (CcStOMP) in the greedy tracking algorithm.The CcStOMP algorithm can extract multiple target atoms at one time, which greatly improves the iterative efficiency. The multiple features are utilized as input parameters for constructing the random forest classifier. The constructed random forest model is used to diagnose ITSC faults with the results showing that the random forest model has a diagnostic accuracy of 98.61% using all features, and the diagnostic accuracy of selecting three of the most important features is still as high as 97.91%. The random forest classification model has excellent robustness that maintains high classification accuracy despite the reduction of feature vectors, which is a great advantage compared to other classification algorithms. The combination of greedy tracing and the random forest is not only a fast diagnostic model but also a model with good generalisation and anti-interference capability. This non-invasive method is applicable to monitoring and detecting failures in industrial PMSMs.

3

Prediction of flyrock distance induced by blasting using particle swarm optimization and multiple regression analysis: an engineering perspective

Chen Yong, Wang Minghua, Yin Heng, Zhang Tianbao

Acta Geophysica

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2024

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Vol. 72, no. 1

287--301

EN

Flyrock is one of the major safety hazards induced by blasting operations. However, few studies were for predicting blasting-induced flyrock distance from the perspective of engineers. The present paper attempts to provide an engineer-friendly equation predicting blasting-induced flyrock distance. Data used in the present study contains s seven blasting parameters including borehole diameter, blasthole length, powder factor, stemming length, maximum charge per delay, burden, and flyrock distance is obtained. Data is inputted into Random Forest for feature selection. The selected features are formulated as two candidate equations, including Multiple Linear Regression (MLR) equation and Multiple Nonlinear Regression (MNR) equation. Those two candidates are respectively referred by Particle Swarm Optimization for searching optimum values for the coefficients of selected features. It is proved that MLR equation has better accuracy. MLR equation is compared with two empirical equations and the MLR equation based on least squares method. It is found that the coefficient of correlation of the proposed MLR equation reaches 0.918, which is the highest compared with the scores of other three equations. The present study utilizes feature selection process to screen inputs, which effectively excludes irrelevant parameters from being considered. Plus the contribution of Particle Swarm Optimization, the accuracy of the obtained equation can be guaranteed.

4

Automated malarial retinopathy detection using transfer learning and multi-camera retinal images

Rajendra Kurup Aswathy, Wigdahl Jeff, Benson Jeremy, Martínez-Ramón Manel, Solíz Peter, Joshi Vinayak

Biocybernetics and Biomedical Engineering

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2023

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Vol. 43, no. 1

109--123

EN

Cerebral malaria (CM) is a fatal syndrome found commonly in children less than 5 years old in Sub-saharan Africa and Asia. The retinal signs associated with CM are known as malarial retinopathy (MR), and they include highly specific retinal lesions such as whitening and hemorrhages. Detecting these lesions allows the detection of CM with high specificity. Up to 23% of CM, patients are over-diagnosed due to the presence of clinical symptoms also related to pneumonia, meningitis, or others. Therefore, patients go untreated for these pathologies, resulting in death or neurological disability. It is essential to have a low-cost and high-specificity diagnostic technique for CM detection, for which We developed a method based on transfer learning (TL). Models pre-trained with TL select the good quality retinal images, which are fed into another TL model to detect CM. This approach shows a 96% specificity with low-cost retinal cameras.

5

Bivariate simulation of river fow using hybrid intelligent models in sub basins of Lake Urmia, Iran

Eslamitabar Vahed, Ahmadi Farshad, Sharafati Ahmad, Rezaverdinejad Vahid

Acta Geophysica

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2023

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Vol. 71, no. 2

873--892

EN

In this study, the performance of continuous autoregressive moving average (CARMA), CARMA-generalized autoregressive conditional heteroscedasticity (CARMA-GARCH), random forest, support vector regression and ant colony optimization (SVR-ACO), and support vector regression and ant lion optimizer (SVR-ALO) models in bivariate simulating of discharge based on the rainfall variables in monthly time scale was evaluated over four sub-basins of Lake Urmia, located in northwestern Iran. The models were assessed in two stages: train and test. The results showed that the CARMA-GARCH hybrid model offered better performance in all cases than the stand-alone CARMA. The improvement percentages of the error rate in the CARMA model compared to the CARMA-GARCH hybrid model in the Mahabad Chai, Nazlu Chai, Siminehrood, and Zola Chai sub-basins were 9, 20, 17, and 6.4%, respectively, in the training phase. Among the models, the hybrid SVR models integrated with ACO and ALO optimization algorithms presented the best performance based on the Taylor diagram and evaluation criteria. Considering the use of ant colony and ant lion optimization algorithms to optimize the support vector regression model’s parameters, these models offered the best performance in the study area to simulate the discharge. The improvement percentages of the error rate in the SVR-ACO model compared to the CARMA-GARCH hybrid model in the Mahabad Chai, Nazlu Chai, Siminehrood, and Zola Chai sub-basins were 11, 10, 19, and 21%, respectively, in the training phase. In contrast, the random forest model provided the lowest accuracy and the highest error in discharge simulation.

6

Analyzing trend and forecast of rainfall and temperature in Valmiki Tiger Reserve, India, using non parametric test and random forest machine learning algorithm

Roshani, Sajjad Haroon, Saha Tamal Kanti, Rahaman Md Hibjur, Masroor Md, Sharma Yatendra, Pal Swades

Acta Geophysica

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2023

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Vol. 71, no. 1

531--552

EN

Assessment of spatiotemporal dynamics of meteorological variables and their forecast is essential in the context of climate change. Such analysis can help suggest possible solutions for flora and fauna in protected areas and adaptation strategies to make forests and communities more resilient. The present study attempts to analyze climate variability, trend and forecast of temperature and rainfall in the Valmiki Tiger Reserve, India. We utilized rainfall and temperature gridded data obtained from the Indian Meteorological Department during 1981–2020. The Mann–Kendall test and Sen’s slope estimator were employed to examine the time series trend and magnitude of change at the annual, monthly and seasonal levels. Random forest machine learning algorithm was used to estimate seasonal prediction and forecasting of rainfall and temperature trend for the next ten years (2021–2030). The predictive capacity of the model was evaluated by statistical performance assessors of coefficient of correlation, mean absolute error, mean absolute percentage error and root mean squared error. The findings revealed a significant decreasing trend in rainfall and an increasing trend in temperature. However, a declining trend for maximum temperature has been observed for winter and post-monsoon seasons. The results of seasonal forecasting exhibited a considerable decrease in rainfall and temperature across the Reserve during all the seasons. However, the temperature will increase during the summer season. The random forest machine learning algorithm has shown its effectiveness in forecasting the temperature and rainfall variables. The findings suggest that these approaches may be used at various spatial scales in different geographical locations.

7

Random forest method to identify seepage in flood embankments

Król Krzysztof, Rymarczyk Tomasz, Gołąbek Michał, Wójcik Dariusz, Niderla Konrad, Kozłowski Edward

Przegląd Elektrotechniczny

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2022

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R. 98, nr 2

191--194

EN

he paper presents research on the effectiveness of testing infiltration in flood embankments using electrical impedance tomography. The usefulness of the algorithm was verified and also the best results were checked. In order to test the reconstructive algorithms obtained during the research, images were generated based on simulation measurements. For this purpose, a special model of the embankment was built. In order to obtain feedback on the degree of infiltration in the flood embankment, prediction by means of the Random Forest method was used.

PL

W artykule przedstawiono badania nad efektywnością badania infiltracji w wałach przeciwpowodziowych za pomocą elektrycznej tomografii impedancyjnej. Zweryfikowano przydatność algorytmu, a także sprawdzono najlepsze wyniki. W celu przetestowania uzyskanych w trakcie badań algorytmów rekonstrukcyjnych wygenerowano obrazy na podstawie pomiarów symulacyjnych. W tym celu zbudowano specjalny model wału przeciwpowodziowego. W celu uzyskania informacji zwrotnej o stopniu przesiąkania w wale przeciwpowodziowym zastosowano predykcję za pomocą metody Random Forest.

8

Random forest based power sustainability and cost optimization in smart grid

Durairaj Danalakshmi, Wróblewski Łukasz, Sheela A., Hariharasudan A., Urbański Mariusz

Production Engineering Archives

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2022

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Vol. 28, Iss. 1

82--92

EN

Presently power control and management play a vigorous role in information technology and power management. Instead of non-renewable power manufacturing, renewable power manufacturing is preferred by every organization for controlling resource consumption, price reduction and efficient power management. Smart grid efficiently satisfies these requirements with the integration of machine learning algorithms. Machine learning algorithms are used in a smart grid for power requirement prediction, power distribution, failure identification etc. The proposed Random Forest-based smart grid system classifies the power grid into different zones like high and low power utilization. The power zones are divided into number of sub-zones and map to random forest branches. The sub-zone and branch mapping process used to identify the quantity of power utilized and the non-utilized in a zone. The non-utilized power quantity and location of power availabilities are identified and distributed the required quantity of power to the requester in a minimal response time and price. The priority power scheduling algorithm collect request from consumer and send the request to producer based on priority. The producer analysed the requester existing power utilization quantity and availability of power for scheduling the power distribution to the requester based on priority. The proposed Random Forest based sustainability and price optimization technique in smart grid experimental results are compared to existing machine learning techniques like SVM, KNN and NB. The proposed random forest-based identification technique identifies the exact location of the power availability, which takes minimal processing time and quick responses to the requestor. Additionally, the smart meter based smart grid technique identifies the faults in short time duration than the conventional energy management technique is also proven in the experimental results.

9

Predicting the stability of open stopes using Machine Learning

Szmigiel Alicja, Apel Derek B.

Journal of Sustainable Mining

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2022

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Vol. 21, iss. 3

241--248

EN

The Mathews stability graph method was presented for the first time in 1980. This method was developed to assess the stability of open stopes in different underground conditions, and it has an impact on evaluating the safety of underground excavations. With the development of technology and growing experience in applying computer sciences in various research disciplines, mining engineering could significantly benefit by using Machine Learning. Applying those ML algorithms to predict the stability of open stopes in underground excavations is a new approach that could replace the original graph method and should be investigated. In this research, a Potvin database that consisted of 176 historical case studies was passed to the two most popular Machine Learning algorithms: Logistic Regression and Random Forest, to compare their predicting capabilities. The results obtained showed that those algorithms can indicate the stability of underground openings, especially Random Forest, which, in examined data, performed slightly better than Logistic Regression.

10

Random forest in the tests of small caliber ammunition

Ampuła Dariusz

Journal of KONBiN

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2022

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Vol. 52, iss. 1

73--85

EN

In the introduction of this article the method of building a random forest model is presented, which can be used for both classification and regression tasks. The process of designing the random forest module was characterized, paying attention to the classification tasks module, which was used to build the author’s model. Based on the test results, a random forest model was designed for 7,62 mm ammunition with T-45 tracer projectile. Predictors were specified and values of stop parameters and process stop formulas were determined, on the basis of which a random forest module was built. An analysis of the resulting random forest model was made in terms of assessing its prediction and risk assessment. Finally, the designed random forest model has been refined by adding another 50 trees to the model. The enlarged random forest model occurred to be slightly stronger and it should be implemented.

PL

W artykule we wstępie przedstawiono metodę budowy modelu losowy las, którą można stosować zarówno do zadań klasyfikacyjnych, jak i do zadań regresyjnych. Scharakteryzowano proces projektowania modułu losowego lasu, zwracając uwagę na moduł zadań klasyfikacyjnych, który posłużył do budowy autorskiego modelu. Na podstawie posiadanych wyników badań, zaprojektowano model losowego lasu dla amunicji strzeleckiej kalibru 7,62 mm z pociskiem smugowym T-45. Wyszczególniono predyktory oraz określono wartości parametrów zatrzymania oraz formuły stopu procesu, na podstawie których zbudowano moduł losowego lasu. Dokonano analizy otrzymanego modelu losowego lasu pod kątem oceny jego trafności predykcji oraz oceny ryzyka. Na końcu, udoskonalono zaprojektowany model losowego lasu poprzez dodanie do modelu kolejnych 50 drzew. Powiększony model losowego lasu okazał się nieznacznie silniejszy i to on powinien być wdrożony do użytkowania.

11

PM 2.5 modelling during paddy stubble burning months using artificial intelligence techniques

Sangwan V., Deswal S.

Journal of Achievements in Materials and Manufacturing Engineering

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2022

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Vol. 110, nr 1

16--26

EN

Purpose: In this study, the artificial intelligence techniques namely Artificial Neural Network, Random Forest, and Support Vector Machine are employed for PM 2.5 modelling. The study is carried out in Rohtak city of India during paddy stubble burning months i.e., October and November. The different models are compared to check their respective efficacies and also sensitivity analysis is performed to know about the most vital parameter in PM 2.5 modelling. Design/methodology/approach: The air pollution data of October and November months from the year 2016 to 2020 was collected for the study. The months of October and November are chosen as paddy stubble burning and major festivities using fireworks occur during these months. The untoward data entries viz. zero values, blank data, etc. were eliminated from the gathered data set and thereafter 231 observations of each parameter were left for the conduct of the presented study. The different models i.e., ANN, RF, SVM, etc. had PM 2.5 as an output variable while relative humidity, sulfur dioxide, nitrogen dioxide, nitric oxide, carbon monoxide, ozone, temperature, solar radiation, wind direction and wind speed acted as input variables. The prototypes created from the training data set are verified on the testing data set. A sensitivity analysis is also done to quantify impact of various parameters on output variable i.e., PM 2.5. Findings: The performance of the SVM_RBF based model turned out to be the best with the performance parameters being the coefficient of determination, root mean square error, and mean absolute error. In the sensitivity test, sulphur dioxide (SO2) was adjudged as the most vital variable. Research limitations/implications: The quantification capacity of the generated models may go beyond the used data set of observations. Practical implications: The artificial intelligence techniques provide precise estimation and forecasting of PM 2.5 in the air during paddy stubble burning months of October and November. Originality/value: Unlike the past research work that focus on modelling of various air pollution parameters, this study in specific focuses on the modelling of most vital air pollutant i.e., PM 2.5 that too specifically during the paddy stubble burning months of October and November when the air pollution is at its peak in northern India.

12

Prediction of flexural strength of FRC pavements by soft computing techniques

Kimteta A., Thakur M.S., Sihag P., Upadhya A., Sharma N.

Archives of Materials Science and Engineering

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2022

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Vol. 117, nr 1

13--24

EN

Purpose: The mechanical characteristics of concrete used in rigid pavements can be improved by using fibre-reinforced concrete. The purpose of the study was to predict the flexural strength of the fibre-reinforced concrete for ten input variables i.e., cement, fine aggregate, coarse aggregate, water, superplasticizer/high range water reducer, glass fibre, polypropylene fibre, steel fibres, length and diameter of fibre and further to perform the sensitivity analysis to determine the most sensitive input variable which affects the flexural strength of the said fibre-reinforced concrete. Design/methodology/approach: The data used in the study was acquired from the published literature to create the soft computing modes. Four soft computing techniques i.e., Artificial neural networks (ANN), Random forests (RF), Random trees RT), and M5P, were applied to predict the flexural strength of fibre-reinforced concrete for rigid pavement using ten significant input variables as stated in the ‘purpose’. The most performing algorithm was determined after evaluating the applied models on the threshold of five statistical indices, i.e., the coefficient of correlation, mean absolute error, root mean square error, relative absolute error, and root relative squared error. The sensitivity analysis for most sensitive input variable was performed with out-performing model, i.e., ANN. Findings: The testing stage findings show that the Artificial neural networks model outperformed other applicable models, having the highest coefficient of correlation (0.9408), the lowest mean absolute error (0.8292), and the lowest root mean squared error (1.1285). Furthermore, the sensitivity analysis was performed using the artificial neural networks model. The results demonstrate that polypropylene fibre-reinforced concrete significantly influences the prediction of the flexural strength of fibre-reinforced concrete. Research limitations/implications: Large datasets may enhance machine learning technique performance. Originality/value: The article's novelty is that the most suitable model amongst the four applied techniques has been identified, which gives far better accuracy in predicting flexural strength.

13

Classification and modelling of sound emission signals in selected tribosystems

Kekez Michał, Jurczak Wojciech, Ozimina Dariusz

Tribologia

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2021

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nr 3

19--26

EN

The paper presents an analysis of the sound level recorded during dry sliding friction conditions. Balls with a diameter of 6 mm placed on pins were made of 100Cr6 steel, silicon carbide (SiC), and corundum (Al2 O3 ), while rotating discs with a height of 6 mm and a diameter of 42 mm were made of 100Cr6 steel. Each pin and disc system was tested for two values of the relative humidity of the air (50 ± 5% and 90 ± 5%). Models of the A-sound level were developed using regression trees and random forest. The paper presents an analysis of the accuracy of the models obtained. Classifications of the six tests performed on the basis of sound level descriptors were also carried out.

PL

W pracy przedstawiono analizę poziomu dźwięku zarejestrowanego podczas tarcia technicznie suchego w ruchu ślizgowym. Podczas sześciu testów tribologicznych stosowano próbkę wykonaną ze stali 100Cr6 oraz trzy przeciwpróbki, wykonane ze stali 100Cr6, węglika krzemu (SiC) i korundu (Al2 O3 ), przy czym każdy układ próbka – przeciwpróbka był testowany dla dwóch wartości wilgotności względnej powietrza (50 ± 5% i 90 ± 5%). Opracowano modele poziomu dźwięku A z użyciem drzew regresji i lasu losowego. W pracy zamieszczono analizę dokładności otrzymanych modeli. Została również przeprowadzona klasyfikacja sześciu wykonanych testów w oparciu o deskryptory poziomu dźwięku.

14

Comparative study for deriving stagedischarge - sediment concentration relationships using soft computing techniques

Sihag P., Sadikhani M. R., Vambol V., Vambol S., Prabhakar A. K., Sharma N.

Journal of Achievements in Materials and Manufacturing Engineering

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2021

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Vol. 104, nr 2

57--76

EN

Purpose: Knowledge of sediment load carried by any river is essential for designing and planning of hydro power and irrigation projects. So the aim of this study is to develop and evaluating the best soft-computing-based model with M5P and Random Forest regressionbased techniques for computation of sediment using datasets of daily discharge, daily gauge and sediment load at the Champua gauging site of the Upper Baitarani river basin of India. Design/methodology/approach: Last few decades, the soft computing techniques based models have been successfully used in water resources modelling and estimation. In this study, the potential of tree based models are examined by developing and comparing sediment load prediction models, based on M5P tree and Random forest regression (RF). Several M5P and RF based models have been applied to a gauging site of the Baitarani River at Odisha, India. To evaluate the performance of the selected M5P and RF-based models, three most popular statistical parameters are selected such as coefficient of correlation, root mean square error and mean absolute error. Findings: A comparison of the results suggested that RF-based model could be applied successfully for the prediction of sediment load concentration with a relatively higher magnitude of prediction accuracy. In RF-based models Qt, Q(t-1), Q(t-2), S(t-1), S(t-2), Ht and H(t-1) combination based M10 model work superior than other combination based models. Another major outcome of this investigation is Qt, Q(t-1) and S(t-1) based model M4 works better than other input combination based models using M5P technique. The optimum input combination is Qt, Q(t-1) and S(t-1) for the prediction of sediment load concentration of the Baitarani River at Odisha, India. Research limitations/implications: The developed models were tested for Baitarani River at Odisha, India.

15

Assessment of Approaches for the Extraction of Building Footprints from Pléiades Images

Taha Lamyaa Gamal El-deen, Ibrahim Rania Elsayed

Geomatics and Environmental Engineering

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2021

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Vol. 15, no. 4

101--116

EN

The Marina area represents an official new gateway of entry to Egypt and the development of infrastructure is proceeding rapidly in this region. The objective of this research is to obtain building data by means of automated extraction from Pléiades satellite images. This is due to the need for efficient mapping and updating of geodatabases for urban planning and touristic development. It compares the performance of random forest algorithm to other classifiers like maximum likelihood, support vector machines, and backpropagation neural networks over the well-organized buildings which appeared in the satellite images. Images were subsequently classified into two classes: buildings and non-buildings. In addition, basic morphological operations such as opening and closing were used to enhance the smoothness and connectedness of the classified imagery. The overall accuracy for random forest, maximum likelihood, support vector machines, and backpropagation were 97%, 95%, 93% and 92% respectively. It was found that random forest was the best option, followed by maximum likelihood, while the least effective was the backpropagation neural network. The completeness and correctness of the detected buildings were evaluated. Experiments confirmed that the four classification methods can effectively and accurately detect 100% of buildings from very high-resolution images. It is encouraged to use machine learning algorithms for object detection and extraction from very high-resolution images.

16

Analyzing rear-end crash severity for a mountainous expressway in China via a classification and regression tree with random forest approach

Wang Yonggang, Luo Xianyu

Archives of Civil Engineering

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2021

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Vol. 67, nr 4

591--604

EN

To understand the contributory factors to rear-end accident severity on mountainous expressways, a total of 1039 rear-end accidents, occurring on G5 Jingkun Expressway from Hechizhai to Qipanguan in Shaanxi, China over the period of 2012 to 2017, were collected, and a non-parametric Classification and Regression Tree (CART) model was used to explore the relationship between severity outcomes and driver factors, vehicle characteristics, roadway geometry and environmental conditions. Then the random forest model was introduced to examine the accuracy of variable selection and rank their importance. The results show that driver’s risky driving behaviours, vehicle type, radius of curve, angle of deflection, type of vertical curve, time, season, and weather are significantly associated with rear-end accident severity. Speeding and driving while drunk and fatigued are more prone to result in severe consequences for such accidents and driving while fatigued is found to have the highest fatality probability, especially during the night period (18:00-24:00). The involvement of heavy trucks increases the injury probability significantly, but decreases the fatality probability. In addition, adverse weather and sharp curve with radius less than 1000 m are the most risk combination of factors. These findings can help agencies more effectively establish stricter regulations, adopt technical measures and strengthen safety education to ensure driver's driving safety on mountainous expressways for today and tomorrow.

17

Analysis of the Effect of News Sentiment on Stock Market Prices through Event Embedding

Sridhar Sashank, Sanagavarapu Sowmya

Annals of Computer Science and Information Systems

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2021

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Vol. 25

147--150

EN

Stock market price prediction models have remained a prominent challenge for the investors owing to their volatile nature. The impact of macroeconomic events such as news headlines is studied here using a standard dataset with closing stock price rates for a chosen period by performing sentiment analysis using a Random Forest classifier. A Bi-LSTM time-series forecasting model is constructed to predict the stock prices by using the polarity of the news headlines. It is observed that Random Forest Classifiers predict the polarity of news articles with an accuracy of 84.92%.

18

An intelligent multimodal framework for identifying children with autism spectrum disorder

Chen Jingying, Liao Mengyi, Wang Guangshuai, Chen Chang

International Journal of Applied Mathematics and Computer Science

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2020

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Vol. 30, no. 3

435--448

EN

Early identification can significantly improve the prognosis of children with autism spectrum disorder (ASD). Yet existing identification methods are costly, time consuming, and dependent on the manual judgment of specialists. In this study, we present a multimodal framework that fuses data on a child’s eye fixation, facial expression, and cognitive level to automatically identify children with ASD, to improve the identification efficiency and reduce costs. The proposed methodology uses an optimized random forest (RF) algorithm to improve classification accuracy and then applies a hybrid fusion method based on the data source and time synchronization to ensure the reliability of the classification results. The classification accuracy of the framework was 91%, which is higher than that of the RF, support vector machine, and discriminant analysis methods. The results suggest that data on a child’s eye fixation, facial expression, and cognitive level are useful for identifying children with ASD. Because the proposed framework can separate ASD children from typically developing (TD) children, it can facilitate the early identification of ASD and may improve intervention programs for children with ASD.

19

Performance comparison of machine learning algotihms for predictive maintenance

Gęca Jakub

Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska

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2020

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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.

20

Profilaktyka zagrożenia tąpaniami w warunkach eksploatacji złoża rud miedzi na dużych głębokościach, na przykładzie pola SI-XXV/1 O/ZG Rudna

Bodlak Maciej, Butra Jan, Gradecki Dawid

Cuprum : czasopismo naukowo-techniczne górnictwa rud

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2020

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nr 1

5--19

PL

Artykuł przedstawia wyniki analizy przyczynowo-skutkowej warunków geologiczno-górniczych występujących w polu eksploatacyjnym SI-XXV/1 O/ZG Rudna, przebiegu dotychczasowej eksploatacji złoża rud miedzi oraz towarzyszącej jej aktywności sejsmicznej. W celu dokonania oceny efektywności dotychczasowej eksploatacji, a także przeprowadzenia analizy skuteczności profilaktyki zagrożenia tąpaniami, podzielono cały przebieg czasowy eksploatacji na okresy, w których roboty prowadzono różnymi systemami eksploatacji – J-UG-PS, R-UO, R-UO/H i J-SZ/UG. Przeprowadzono analizę skuteczności zapobiegania zagrożeniom poprzez prowokację wstrząsów góro-tworu, w zależności od stosowanego systemu eksploatacji, sposobu rozcięcia calizny oraz zakresu aktywnej profilaktyki zagrożenia tąpaniami. Na tej podstawie dokonano weryfikacji metod profilaktyki tąpaniowej i przed-stawiono jej proponowany, optymalny zakres dla dalszych robót wybierkowych.

EN

The article presents results of geological and mining conditions analysis for SI-XXV/1 mining panel in “Rudna” mine, including description of past exploitation and accompanying seismic activity. For evaluation of done exploitation and for analysis of rockburst hazard prevention methods efficiency, all past exploitation has been divided into periods, during which works were handled with different exploitation methods – J-UG-PS, R-UO, R-UO/H and J-SZ/UG. Analysis of bumps provocation efficiency has been made for different exploitation methods, rockbody cutting methods and scope of active rockbursts prevention methods. On that basis verification of rockbursts prevention methods was performed and optimal scope of these methods for future exploitation has been evaluated.