scholarly journals Ball Bearing Fault Diagnosis using Supervised and Unsupervised Machine Learning Methods

2015 ◽  
Vol 20 (4) ◽  
Author(s):  
V. Vakharia ◽  
V. K. Gupta ◽  
P. K. Kankar
Keyword(s):  
Machine Learning ◽  
Fault Diagnosis ◽  
Ball Bearing ◽  
Learning Methods ◽  
Unsupervised Machine Learning ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Machine Learning Methods

Machine Learning for Chaotic Rayleigh Transitions

2021 ◽  
Author(s):  
Ben Tribelhorn ◽  
H. E. Dillon
Keyword(s):  
Machine Learning ◽  
Cluster Analysis ◽  
Natural Convection ◽  
Prandtl Number ◽  
Learning System ◽  
Lorenz Equations ◽  
Time Step ◽  
Learning Methods ◽  
Unsupervised Machine Learning ◽  
Machine Learning Methods

Abstract This paper is a preliminary report on work done to explore the use of unsupervised machine learning methods to predict the onset of turbulent transitions in natural convection systems. The Lorenz system was chosen to test the machine learning methods due to the relative simplicity of the dynamic system. We developed a robust numerical solution to the Lorenz equations using a fourth order Runge-Kutta method with a time step of 0.001 seconds. We solved the Lorenz equations for a large range of Raleigh ratios from 1–1000 while keeping the geometry and Prandtl number constant. We calculated the spectral density, various descriptive statistics, and a cluster analysis using unsupervised machine learning. We examined the performance of the machine learning system for different Raleigh ratio ranges. We found that the automated cluster analysis aligns well with well known key transition regions of the convection system. We determined that considering smaller ranges of Raleigh ratios may improve the performance of the machine learning tools. We also identified possible additional behaviors not shown in z-axis bifurcation plots. This unsupervised learning approach can be leveraged on other systems where numerical analysis is computationally intractable or more difficult. The results are interesting and provide a foundation for expanding the study for Prandtl number and geometry variations. Future work will focus on applying the methods to more complex natural convection systems, including the development of new methods for Nusselt correlations.

Supervised process monitoring and fault diagnosis based on machine learning methods

2019 ◽  
Vol 102 (5-8) ◽  
pp. 2321-2337 ◽  
Author(s):  
Hajer Lahdhiri ◽  
Maroua Said ◽  
Khaoula Ben Abdellafou ◽  
Okba Taouali ◽  
Mohamed Faouzi Harkat
Keyword(s):  
Machine Learning ◽  
Fault Diagnosis ◽  
Process Monitoring ◽  
Learning Methods ◽  
Machine Learning Methods
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