scholarly journals Comparison of the supervised machine learning techniques using WPT for the fault diagnosis of cylindrical roller bearing

2021 ◽  
Vol 13 (2) ◽  
pp. 50-56
Author(s):  
Umang Parmar ◽  
D.H. Pandya
Keyword(s):  
Logistic Regression ◽  
Roller Bearing ◽  
Ranking Method ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Feature Ranking ◽  
Major Purpose ◽  
Cylindrical Roller Bearing ◽  
Cylindrical Roller

In this paper, a comparative study of the Artificial Intelligence (AI) techniques for the condition monitoring of the cylindrical roller bearing is presented. For the feature selection, wavelet analysis is applied using the ‘sym2’ as the mother wavelet. Nine features are considered for the training and evaluation of the AI techniques and then effectiveness is compared. Bearing sample data consists of four different conditions as having defective inner race, defective outer race, having defects on roller and a healthy bearing. For the preparation of the sample bearing, laser machine is used for introduction of the micro size defects on the surfaces. Support Vector Machine (SVM), Artificial neural network (ANN), and logistic regression are used with feature ranking method for the data training purpose and their effectiveness of identifying the condition is the major purpose. Feature ranking method is the new way of filtering the right data in right sequence for the data training. In results, Logistic regression found more accurate in comparison with the ANN and SVM for the cylindrical bearing.

scholarly journals A REVIEW ON MACHINE LEARNING TECHNIQUES FOR ADVANCED HEALTH CARE SYSTEMS

2020 ◽  
Vol 9 (11) ◽  
pp. 1-7
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Logistic Regression ◽  
Nearest Neighbor ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
K Nearest Neighbor

Artificial intelligence is the technology that lets a machine mimic the thinking ability of a human being. Machine learning is the subset of AI, that makes this machine exhibit human behavior by making it learn from the known data, without the need of explicitly programming it. The health care sector has adopted this technology, for the development of medical procedures, maintaining huge patient’s records, assist physicians in the prediction, detection, and treatment of diseases and many more. In this paper, a comparative study of six supervised machine learning algorithms namely Logistic Regression(LR),support vector machine(SVM),Decision Tree(DT).Random Forest(RF),k-nearest neighbor(k-NN),Naive Bayes (NB) are made for the classification and prediction of diseases. Result shows out of compared supervised learning algorithms here, logistic regression is performing best with an accuracy of 81.4 % and the least performing is k-NN with just an accuracy of 69.01% in the classification and prediction of diseases.

scholarly journals Cardiac Disease Prediction using Supervised Machine Learning Techniques.

2022 ◽  
Vol 2161 (1) ◽  
pp. 012013
Author(s):  
Chiradeep Gupta ◽  
Athina Saha ◽  
N V Subba Reddy ◽  
U Dinesh Acharya
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Cardiac Disease ◽  
Performance Metrics ◽  
Confusion Matrix ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Ensemble Techniques ◽  
Learning Techniques

Abstract Diagnosis of cardiac disease requires being more accurate, precise, and reliable. The number of death cases due to cardiac attacks is increasing exponentially day by day. Thus, practical approaches for earlier diagnosis of cardiac or heart disease are done to achieve prompt management of the disease. Various supervised machine learning techniques like K-Nearest Neighbour, Decision Tree, Logistic Regression, Naïve Bayes, and Support Vector Machine (SVM) model are used for predicting cardiac disease using a dataset that was collected from the repository of the University of California, Irvine (UCI). The results depict that Logistic Regression was better than all other supervised classifiers in terms of the performance metrics. The model is also less risky since the number of false negatives is low as compared to other models as per the confusion matrix of all the models. In addition, ensemble techniques can be approached for the accuracy improvement of the classifier. Jupyter notebook is the best tool, for the implementation of Python Programming having many types of libraries, header files, for accurate and precise work.

Load distribution in cylindrical roller bearing with EHD lubricated contact force

2019 ◽  
Author(s):  
Natália Akemi Hoshikawa Tsuha ◽  
Fabio Nonato ◽  
Katia Lucchesi Cavalca Dedini
Keyword(s):  
Contact Force ◽  
Load Distribution ◽  
Roller Bearing ◽  
Lubricated Contact ◽  
Cylindrical Roller Bearing ◽  
Cylindrical Roller

scholarly journals Predictive Modelling of Employee Turnover in Indian IT Industry Using Machine Learning Techniques

2019 ◽  
Vol 23 (1) ◽  
pp. 12-21 ◽  
Author(s):  
Shikha N. Khera ◽  
Divya
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Confusion Matrix ◽  
Predictive Modelling ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
It Industry ◽  
Knowledge Based ◽  
Employee Attrition

Information technology (IT) industry in India has been facing a systemic issue of high attrition in the past few years, resulting in monetary and knowledge-based loses to the companies. The aim of this research is to develop a model to predict employee attrition and provide the organizations opportunities to address any issue and improve retention. Predictive model was developed based on supervised machine learning algorithm, support vector machine (SVM). Archival employee data (consisting of 22 input features) were collected from Human Resource databases of three IT companies in India, including their employment status (response variable) at the time of collection. Accuracy results from the confusion matrix for the SVM model showed that the model has an accuracy of 85 per cent. Also, results show that the model performs better in predicting who will leave the firm as compared to predicting who will not leave the company.

scholarly journals Dynamic Performance of a Squeeze Film Damper with a Cylindrical Roller Bearing under a Large Static Radial Loading Range

Machines ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 14 ◽  
Author(s):  
Hans Meeus ◽  
Jakob Fiszer ◽  
Gabriël Van De Velde ◽  
Björn Verrelst ◽  
Wim Desmet ◽  
...  
Keyword(s):  
Roller Bearing ◽  
Squeeze Film ◽  
Stable Operation ◽  
Large Power ◽  
Squeeze Film Damper ◽  
Depth Analysis ◽  
Rotor Support ◽  
Cylindrical Roller Bearing ◽  
Radial Loading ◽  
Cylindrical Roller

Turbomachine rotors, supported by little damped rolling element bearings, are generally sensitive to unbalance excitation. Accordingly, most machines incorporate squeeze film damper technology to dissipate mechanical energy caused by rotor vibrations and to ensure stable operation. When developing a novel geared turbomachine able to cover a large power range, a uniform mechanical drivetrain needs to perform well over the large operational loading range. Especially, the rotor support, containing a squeeze film damper and cylindrical roller bearing in series, is of vital importance in this respect. Thus, the direct objective of this research project was to map the performance of the envisioned rotor support by estimating the damping ratio based on the simulated and measured vibration response during run-up. An academic test rig was developed to provide an in-depth analysis on the key components in a more controlled setting. Both the numerical simulation and measurement results exposed severe vibration problems for an insufficiently radial loaded bearing due to a pronounced anisotropic bearing stiffness. As a result, a split first whirl mode arose with its backward component heavily triggered by the synchronous unbalance excitation. Hence, the proposed SFD does not function properly in the lower radial loading range. Increasing the static load on the bearing or providing a modified rotor support for the lower power variants will help mitigating the vibration issues.

Investigation on the drag effect of a rolling element confined in the cavity of a cylindrical roller bearing

2021 ◽  
pp. 135065012110260
Author(s):  
Wenjun Gao ◽  
Shuo Zhang ◽  
Xiaohang Li ◽  
Zhenxia Liu
Keyword(s):  
Open Space ◽  
Roller Bearing ◽  
Flow Characteristics ◽  
Experimental Tests ◽  
Windward Side ◽  
Leeward Side ◽  
Drag Effect ◽  
Rolling Element ◽  
Cylindrical Roller Bearing ◽  
Cylindrical Roller

In cylindrical roller bearings, the drag effect may be induced by the rolling element translating in a fluid environment of the bearing cavity. In this article, the computational fluid dynamics method and experimental tests are employed to analyse its flow characteristics and pressure distribution. The results indicate that the pressure difference between the windward side and the leeward side of the cylinder is raised in view of it blocking the flow field. Four whirl vortexes are formed in four outlets of two wedge-shaped areas between the front part of the cylindrical surface and adjacent walls for the cylinder of L/ D = 1.5 at Re = 4.5 × 103. Vortex shedding is found in the direction of cylinder axis at Re = 4.5 × 104. The relationship between drag coefficient and Reynolds number is illustrated, obviously higher than that of the two-dimensional cylinder in open space.

Dynamics of Rolling-Element Bearings—Part I: Cylindrical Roller Bearing Analysis

1979 ◽  
Vol 101 (3) ◽  
pp. 293-302 ◽  
Author(s):  
P. K. Gupta
Keyword(s):  
Differential Equations ◽  
Normal Force ◽  
Equations Of Motion ◽  
Roller Bearing ◽  
Rolling Element Bearings ◽  
Analytical Formulation ◽  
Rolling Element ◽  
Cylindrical Roller Bearing ◽  
Cylindrical Roller ◽  
Bearing Analysis

An analytical formulation for the roller motion in a cylindrical roller bearing is presented in terms of the classical differential equations of motion. Roller-race interaction is analyzed in detail and the resulting normal force and moment vectors are determined. Elastohydrodynamic traction models are considered in determining the roller-race tractive forces and moments. Formulation for the roller end and race flange interaction during skewing of the roller is also considered. Roller-cage interactions are assumed to be either hydrodynamic or fully metallic. Simple relationships are used to determine the churning and drag losses.

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