Wyniki wyszukiwania - BazTech

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Degradation assessment of bearing based on machine learning classification matrix

Kumar Satish, Kumar Paras, Kumar Girish

Eksploatacja i Niezawodność

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2021

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Vol. 23, no. 2

395--404

EN

In the broad framework of degradation assessment of bearing, the final objectives of bearing condition monitoring is to evaluate different degradation states and to estimate the quantitative analysis of degree of performance degradation. Machine learning classification matrices have been used to train models based on health data and real time feedback. Diagnostic and prognostic models based on data driven perspective have been used in the prior research work to improve the bearing degradation assessment. Industry 4.0 has required the research in advanced diagnostic and prognostic algorithm to enhance the accuracy of models. A classification model which is based on machine learning classification matrix to assess the degradation of bearing is proposed to improve the accuracy of classification model. Review work demonstrates the comparisons among the available state-of-the-art methods. In the end, unexplored research technical challenges and niches of opportunity for future researchers are discussed.

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Modelling of Love-type waves in an elastic layer sandwiched between viscous liquid half space and size dependent couple stress substrate

Sharma Vanita, Kumar Satish

Journal of Theoretical and Applied Mechanics

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2019

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Vol. 57 nr 4

1009--1019

EN

Dispersion curves employed for designing Love wave based liquid sensing devices may provide more accurate information if due consideration is given to parameters describing microstructural behavior of the substrate. The present study involves mathematical modelling of Love waves propagating in a hybrid structure consisting of an elastic layer in the middle overlying a size dependent substrate, loaded with a viscous liquid (Newtonian) half space. Numerical computations are carried out to graphically demonstrate the effects of various parameters: characteristic length of the substrate, thickness of the elastic layer, viscosity and density of the overlying viscous liquid (Newtonian) on dispersion characteristics.

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Evaluation of evolutionary algorithms for the optimization of storm water drainage network for an urbanized area

Kumar Satish, Kaushal D. R., Gosain A. K.

Acta Geophysica

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2019

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Vol. 67, no. 1

149--165

EN

The studies pertaining to urban storm water drainage system have picked up importance lately in light of pluvial flooding. The flooding is mostly due to urban expansion, reduction in infiltration rate and environmental change. In order to minimize flooding, hydrologists are using conceptual rainfall–runoff models as a tool for predicting surface runoff and flood forecasting. Manual calibration is often a tedious process because of the involved subjectivity, which makes the automatic approach more preferable. In this study, three evolutionary algorithms (EAs), namely SFLA, GA and PSO, were used to calibrate SWMM parameters for the two study areas of the highly urbanized catchments of Delhi, India. The work incorporates auto-tuning of a widely used SWMM, via internal coupling of SWMM with all three EAs in MATLAB environment separately. Results were tested using statistical parameters, i.e., Nash–Sutcliffe efficiency (NSE), Percent Bias (PBIAS) and root-mean-square error–observations standard deviation ratio (RSR). GA results were in good agreement with the observed data in both the study area with NSE and PBIAS values lying between 0.60 and 0.91, and 1.29 and 7.41%, respectively. Also, RSR value was near zero, indicating reasonably good model performance. Subsequently, the model reasonably predicted the flooding hotspots that should be controlled to prevent any possible inundation of the surrounding areas. SFLA results were also promising, but better than PSO. Thus, the approach has demonstrated the potential use and combination of single-objective optimization algorithms and hydrodynamic models for assessing the risk in urban storm water drainage systems, providing valuable information for decision-makers.