Faults Diagnosis of Ball Bearing Based on Probabilistic Neural Network

Xin Xin Ji; Zhong Min Wang

doi:10.4028/www.scientific.net/AMM.543-547.1149

Paper Titles

Determination of Microelements in Balanophora involucrate Hook. f. by ICP-AES
p.1132

ICP-AES Determination of Elements in Elsholtzia rugulosa Hemsl. after Microwave Assisted Digestion
p.1136

Data Collision Detection Technology of the Electronic Countermeasures' Simulation Scenario
p.1140

Vibration Fault Diagnosis on Gearbox of Belt Conveyor in Coal Mine
p.1145

Faults Diagnosis of Ball Bearing Based on Probabilistic Neural Network
p.1149

Necessity Analysis of Filthy Improved Monitoring Methods
p.1153

Research Progress for Determination of the Antioxidant Content in Transformer Oil
p.1157

Error Parameters Identification of Accelerometer Based on AFSA
p.1161

The Validation of Hand Anthropometry Using 3D Laser Scan
p.1167

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 543-547Faults Diagnosis of Ball Bearing Based on...

Faults Diagnosis of Ball Bearing Based on Probabilistic Neural Network

Abstract:

To solve the difficulties of establishing precise mathematical model of ball bearing fault diagnosis, a classification method based on probabilistic neural network (PNN) used for ball bearing fault mode classification is proposed. Firstly, this paper analyzed the basic theory of PNN, and then a mapping relationship between feature vector and fault mode is set up based on PNN. Secondly, selections of ball bearing fault features and practical procedure of neural network setting and training are discussed. Experiments and compared with the algorithm of back propagation neural network (BPNN) prove that PNN method is feasible and has better diagnosis efficiency than BPNN.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 543-547)

Pages:

1149-1152

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.543-547.1149

Citation:

Cite this paper

Online since:

March 2014

Authors:

Xin Xin Ji*, Zhong Min Wang

Keywords:

Ball Bearing, Fault Diagnosis, Probabilistic Neural Network (PNN)

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] Jang G, Jeong S W. Vibration analysis o f a rotating system due to the effect of ball bearing waviness [J]. Journal of Sound and Vibration, 2004, 269: pp.709-726.

DOI: 10.1016/s0022-460x(03)00127-5

Google Scholar

[2] Huang H H, Wang H P. An integrated monitoring and diagnostic system for ball bearings [J]. The International Journal of Advanced Manufacturing Technology, 1996, 12: pp.37-46.

Google Scholar

[3] Spencht D F. Probabilistics neural networks [J]. Neural networks, 1990, 3 (1): pp.109-118.

Google Scholar

[4] Hornik K. Approximation capabilities of multiplayer feed-forward network [J]. Neural Networks, 1991, 4(2): pp.252-257.

Google Scholar

[5] Li W H, Shi T L, Liao G. L, etc. Feature extraction and classification of gear faults using principal component analysis [J]. Journal of Quality in Maintenance Engineering, 2003, 9(2): pp.132-143.

DOI: 10.1108/13552510310482389

Google Scholar

[6] Wang Z M, Zhou P, Li C N. Fault Diagnosis of Roller Bearing Based on an Improved Probabilistic Neural Network [J]. Mechanical Science and Technology for Aerospace Engineering, 2013, 32(5): pp.729-732.

Google Scholar