LAPSE:2020.0034
Published Article
LAPSE:2020.0034
Study on a Fault Identification Method of the Hydraulic Pump Based on a Combination of Voiceprint Characteristics and Extreme Learning Machine
Wanlu Jiang, Zhenbao Li, Jingjing Li, Yong Zhu, Peiyao Zhang
January 6, 2020
Aiming at addressing the problem that the faults in axial piston pumps are complex and difficult to effectively diagnose, an axial piston pump fault diagnosis method that is based on the combination of Mel-frequency cepstrum coefficients (MFCC) and the extreme learning machine (ELM) is proposed. Firstly, a sound sensor is used to realize contactless sound signal acquisition of the axial piston pump. The wavelet packet default threshold denoises the original acquired sound signals. Afterwards, windowing and framing are added to the de-noised sound signals. The MFCC voiceprint characteristics of the processed sound signals are extracted. The voiceprint characteristics are divided into a training sample set and test sample set. ELM models with different numbers of neurons in the hidden layers are established for training and testing. The relationship between the number of neurons in the hidden layer and the recognition accuracy rate is obtained. The ELM model with the optimal number of hidden layer neurons is established and trained with the training sample set. The trained ELM model is applied to the test sample set for fault diagnosis. The fault diagnosis results are obtained. The fault diagnosis results of the ELM model are compared with those of the back propagation (BP) neural network and the support vector machine. The results show that the fault diagnosis method that is proposed in this paper has a higher recognition accuracy rate, shorter training and diagnosis times, and better application prospect.
Keywords
axial piston pump, extreme learning machine, fault diagnosis, voiceprint characteristics
Suggested Citation
Jiang W, Li Z, Li J, Zhu Y, Zhang P. Study on a Fault Identification Method of the Hydraulic Pump Based on a Combination of Voiceprint Characteristics and Extreme Learning Machine. (2020). LAPSE:2020.0034
Author Affiliations
Jiang W: Hebei Province Key Laboratory of Heavy Machinery Fluid Power Transmission and Control, Yanshan University, Qinhuangdao 066004, China; Key Laboratory of Advanced Forging & Stamping Technology and Science Ministry of Education of China, Yanshan University,
Li Z: Hebei Province Key Laboratory of Heavy Machinery Fluid Power Transmission and Control, Yanshan University, Qinhuangdao 066004, China; Key Laboratory of Advanced Forging & Stamping Technology and Science Ministry of Education of China, Yanshan University,
Li J: Hebei Province Key Laboratory of Heavy Machinery Fluid Power Transmission and Control, Yanshan University, Qinhuangdao 066004, China; Key Laboratory of Advanced Forging & Stamping Technology and Science Ministry of Education of China, Yanshan University,
Zhu Y: National Research Center of Pumps, Jiangsu University, Zhenjiang 212013, China [ORCID]
Zhang P: Hebei Province Key Laboratory of Heavy Machinery Fluid Power Transmission and Control, Yanshan University, Qinhuangdao 066004, China; Key Laboratory of Advanced Forging & Stamping Technology and Science Ministry of Education of China, Yanshan University,
Journal Name
Processes
Volume
7
Issue
12
Article Number
E894
Year
2019
Publication Date
2019-12-01
Published Version
ISSN
2227-9717
Version Comments
Original Submission
Other Meta
PII: pr7120894, Publication Type: Journal Article
Record Map
Published Article

LAPSE:2020.0034
This Record
External Link

doi:10.3390/pr7120894
Publisher Version
Download
Files
Jan 6, 2020
Main Article
License
CC BY 4.0
Meta
Record Statistics
Record Views
405
Version History
[v1] (Original Submission)
Jan 6, 2020
 
Verified by curator on
Jan 6, 2020
This Version Number
v1
Citations
Most Recent
This Version
URL Here
https://psecommunity.org/LAPSE:2020.0034
 
Original Submitter
Calvin Tsay
Links to Related Works
Directly Related to This Work
Publisher Version