Modeling the trend of coronavirus disease 2019 and restoration of operational capability of metropolitan medical service in China: a machine learning and mathematical model-based analysis

AbstractIndia has experienced an early and harshest lockdown from 25th March 2020 in response to the outbreak. However, an accurate estimation of the progression of the spread of infection and the level of preparedness to combat this disease are urgently needed. Using a data-based mathematical model, our study has made predictions on the number of cases that are expected to rise in India till 14th June 2020. The epidemiological data of daily cases have been utilized from 25th March (i.e., the first day of lockdown) to 23rd April 2020. In the study, we have stimulated two possible scenarios (optimistic and pessimistic) for the prediction. As per the optimistic approach of modelling, COVID-19 may end in the first week of June 2020 with a total of 77,900 infected cases including 2,442 fatalities. However, the results under the pessimistic scenario are a bit scary as it shows that a total of 283,300 infected cases with 10,180 fatalities till 14th June. To win the battle, 10 weeks of complete lockdown is much needed at least in the infected states and the union territories of India. Alternatively, the isolation of clusters (hotspot regions) is required if India wants a resume of some essential activities.

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An Intelligent Prediction for Detecting Bearing Vibration Characteristics Using a Machine Learning Model

Journal of Nondestructive Evaluation Diagnostics and Prognostics of Engineering Systems ◽

10.1115/1.4049938 ◽

2021 ◽

Vol 4 (3) ◽

Author(s):

Vishal G. Salunkhe ◽

R. G. Desavale

Keyword(s):

Machine Learning ◽

Mathematical Model ◽

Electrical Discharge Machining ◽

Experimental Results ◽

Machine Learning Techniques ◽

Support Vector ◽

Bearing Failure ◽

Vibration Characteristic ◽

Model Based ◽

Dimension Analysis

Abstract Bearing failure in the heavy rotating machines results in shut down of many other machines and affects the overall cost and quality of the product. Condition monitoring of bearing systems acts as a preventive and corrective measure as it avoids breakdown and saves maintenance time and cost. This research paper proposes advanced strategies for early detection and analysis of taper rolling bearings. In view of this, mathematical model-based fault diagnosis and support vector machining (SVM) are proposed in this work. A mathematical model using dimension analysis by the matrix method (Dimension Analysis Method (DAMM)) and SVM is developed that can be used to predict the vibration characteristic of the rotor-bearing system. Types of defects are created using electrical discharge machining (EDM) and analyzed, and correlation is established between dependent and independent parameters. Experiments were performed to evaluate the rotor dynamic characteristic of healthy and unhealthy bearings. Experimental results are used to validate the model obtained by the DAMM and SVM. Experimental results showed that the vibration characteristic could be evaluated by using a theoretical model and SVM. Efforts have been made to extend the service life of the machines and the assembly lines and to improve their efficiency, so as to reduce bearing failure; what provides novelty to these efforts is the use of four machine learning techniques. Thus, an automatic online diagnosis of bearing faults has been made possible with the developed model based on DAMM and SVM.

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