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Datum unit optimization for robustness of a journal bearing diagnosis system

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International Journal of Precision Engineering and Manufacturing Aims and scope Submit manuscript

An Erratum to this article was published on 12 January 2016

Abstract

This paper describes a robust diagnosis method for a rotor system with a journal bearing. To enhance the robustness of a journal bearing diagnosis system, it is of great importance to define an optimum datum unit for featuring anomaly states of the rotor system. To support the research goal, this study makes use of three measures for class separation, including Kullback-Leibler divergence (KLD), Fisher discriminant ratio (FDR), and a newly proposed measure: probability of separation (PoS). From the viewpoint of class separability, this work found that PoS is more attractive than other methods for quantification of class separation. PoS offers favorable properties like normalization, boundedness, and high sensitivity. A generic algorithm integrated with one of three measures consistently suggested the optimum datum units among the feasible datum units. Optimum datum units were found to be one-cycle for time-domain features and sixty-cycles for frequency-domain features. The support vector machine (SVM) classifier with the optimum datum units was used for diagnosing a normal and three anomaly states. The health classification results showed that the proposed optimum datum units can outperform other datum units.

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Abbreviations

KLD:

Kullback-Leibler divergence

FDR:

Fisher discriminant ratio

PoS:

probability of separation

DC:

direct current

AC:

alternating current

RMS:

root mean square

s(f):

power spectral density function

f(x):

probability density function

F(x):

cumulative density function

P(i):

probability mass function

μi :

mean value of i th class data

σi :

standard deviation of i th class data

Π:

cost function value of genetic algorithm

PoS j :

PoS value of j th feature

ρi,j :

correlation coefficient between i th and j th features

k :

generation number of genetic algorithm

g n c :

n th feature of the feature set c

β :

lower limit criteria of genetic algorithm

N p :

number of feature subsets

x i :

i th n-dimensional training data

y i :

class index of i th n-dimensional training data

w :

normal vector of hyper-plane

b :

bias value

ξ i :

slack variable

C :

penalty coefficient

Φ:

transform function

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Authors and Affiliations

  1. Department of Mechanical and Aerospace Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, South Korea

    Byung Chul Jeon, Joon Ha Jung & Byeng Dong Youn

  2. Power Generation Laboratory, Korea Electric Power Corporation Research Institute, 105 Munji-ro, Yuseong-gu, Daejeon, 34056, South Korea

    Yeon-Whan Kim & Yong-Chae Bae

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  1. Byung Chul Jeon
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Correspondence to Byeng Dong Youn.

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Jeon, B.C., Jung, J.H., Youn, B.D. et al. Datum unit optimization for robustness of a journal bearing diagnosis system. Int. J. Precis. Eng. Manuf. 16, 2411–2425 (2015). https://doi.org/10.1007/s12541-015-0311-y

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