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Fault Diagnosis of Planetary Gear Carrier Packs: A Class Imbalance and Multiclass Classification Problem

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Abstract

Fault diagnosis plays a key role in monitoring manufactured products for the purpose of quality control. Among the several fault diagnosis approaches, knowledge-based fault diagnosis, which uses signals from sensors and machine learning algorithms instead of a priori information, is widely employed to diagnose the status of products. In this paper, we propose a knowledge-based procedure to establish a fault diagnosis model. The model is aimed to diagnose planetary gear carrier packs, which have many fault types and an unbalanced number of samples in the sample classes, using transmission error. In the procedure, the best feature subset that contains the most important features is selected using two different feature selection processes. Several ensemble algorithms are used during the model training process. The imbalance ratio between classes of samples is addressed. The number of weak learners is automatically determined by a genetic algorithm. Finally, the performance of the proposed procedure is validated by comparison with other models trained without applying the proposed procedure. We observed that it is important to incorporate the class imbalance technique in the training process as it reduces the misclassification of faulty products as normal ones. This reduction is important in production quality control.

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Abbreviations

α t :

Weight of the tth weak hypothesis

ADASYN:

Adaptive synthetic

Bagging:

Bootstrap aggregating

C :

Target class

CART:

Classification and regression tree

CI:

Class imbalance

D t(i):

Weight of the ith sample at the tth iteration

ε t :

Error of a hypothesis at the tth iteration

g i :

ith feature

GA:

Genetic algorithm

G_mean:

Geometric mean

h t :

Weak hypothesis at the tth iteration

H :

The final hypothesis

IR:

Imbalance ratio

I(a;b):

Mutual information of two random variables, a and b

k :

Number of classes

m :

Number of samples

μ :

Mean

MIQ:

Mutual information quotient

mRMR:

Min-redundancy and max-relevance

n :

Number of the fault types

N i,j :

Number of ith class samples that are classified as jth class

OVA:

One-versus-all

p(a):

Probability density function of variable, a

RSA:

Revolution synchronous averaging

σ :

Deviation

S :

Objective subset

SBS:

Sequential backward selection

SFS:

Sequential forward selection

SMOTE:

Synthetic minority over-sampling technique

TE:

Transmission error

V I :

Relevance

W I :

Redundancy

x :

Input

y :

Output

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Acknowledgements

This research was supported by the Chung-Ang University Research Scholarship Grants in 2017. This work was also supported by the Technology Innovation Program (10073196, Development of Prognostics and Quality Improvement Technologies for Enhancing Productivity and Yield Rate of Automobile Components by Minimizing Downtime of Manufacturing Robots/Process Equipment) funded By the Ministry of Trade, industry and Energy (MOTIE, Korea).

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Authors and Affiliations

  1. School of Mechanical Engineering, Chung-Ang University, Seoul, 06974, Republic of Korea

    Soonyoung Han & Hae-Jin Choi

  2. The George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA

    Seung-Kyum Choi

  3. Department of Mechanical System Engineering, Chung-Ang University, Seoul, 06974, Republic of Korea

    Jae-Sung Oh

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Han, S., Choi, HJ., Choi, SK. et al. Fault Diagnosis of Planetary Gear Carrier Packs: A Class Imbalance and Multiclass Classification Problem. Int. J. Precis. Eng. Manuf. 20, 167–179 (2019). https://doi.org/10.1007/s12541-019-00082-4

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