Analysis of Data From Measuring Sensors for Prediction in Production Process Control Systems

Rymarczyk, Tomasz; Przysucha, Bartosz; Kowalski, Marcin; Bednarczuk, Piotr

doi:10.35784/iapgos.570

Cited by 2 publications

(2 citation statements)

References 21 publications

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“…by F. Rosenblatt [21]. In the formula above,  is the learning rate (0÷1), The idea of updating weights has led researchers to develop more sophisticated models.…”

Section: Prediction Models and Validationmentioning

confidence: 99%

Performance Comparison of Machine Learning Algorithms for Predictive Maintenance

Gęca¹

2020

IAPGOS

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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.

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“…by F. Rosenblatt [21]. In the formula above,  is the learning rate (0÷1), The idea of updating weights has led researchers to develop more sophisticated models.…”

Section: Prediction Models and Validationmentioning

confidence: 99%

Performance Comparison of Machine Learning Algorithms for Predictive Maintenance

Gęca¹

2020

IAPGOS

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“…The application of AI in engineering solutions yielding the desired results has encouraged numerous research works presenting a variety of applications. Artificial intelligence has emerged, among others, in the context of monitoring or modelling the operation of machines or equipment [ 12 , 13 ], development and evaluation of manufacturing technologies [ 14 , 15 , 16 , 17 ], new engineering materials [ 18 , 19 ] or as support for civil engineering [ 20 , 21 ], as well as transport [ 22 ], electrical [ 23 ] or geological engineering [ 24 ]. It is impossible to mention all the areas of engineering problems in which it is applied, and the literature presented only signals the openness of the research community to its implementation.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Selected Mechanical Properties of Polymer Composites with Alumina Modifiers

Kosicka

Krzyżak²,

Dorobek

et al. 2022

Materials

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Forecasting is one of the cognitive methods based on empirical knowledge supported by appropriate modeling methods that give information about the way the relations between factors and how the phenomenon under study will develop in the future. In this article, a selection is made of a suitable architecture for a predictive model for a set of data obtained during testing of the properties of polymer composites with a matrix in the form of epoxy resin with trade name L285 (Havel Composites) with H285 MGS hardener (Havel Composites), and with the addition of the physical modifier noble alumina with mass percentages of 5%, 10%, 15%, 20% and 25% for the following grain sizes: F220, F240, F280, F320, F360, respectively. In order to select the optimal architecture for the predictive model, the results of the study were tested on five types of predictive model architectures results were tested on five types of prediction model architectures, with five-fold validation, including the mean square error (MSE) metric and R2 determined for Young’s modulus (Et), maximum stress (σm), maximum strain (εm) and Shore D hardness (⁰Sh). Based on the values from the forecasts and the values from the empirical studies, it was found that in 63 cases the forecast should be considered very accurate (this represents 63% of the forecasts that were compared with the experimental results), while 15 forecasts can be described as accurate (15% of the forecasts that were compared with the experimental results). In 20 cases, the MPE value indicated the classification of the forecast as acceptable. As can be seen, only for two forecasts the MPE error takes values classifying them to unacceptable forecasts (2% of forecasts generated for verifiable cases based on experimental results).

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Analysis of Data From Measuring Sensors for Prediction in Production Process Control Systems

Cited by 2 publications

References 21 publications

Performance Comparison of Machine Learning Algorithms for Predictive Maintenance

Performance Comparison of Machine Learning Algorithms for Predictive Maintenance

Prediction of Selected Mechanical Properties of Polymer Composites with Alumina Modifiers

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