NDLI: Fault diagnosis based on characteristics of the vibration energy on the hydraulic pump

Content Provider	IEEE Xplore Digital Library
Author	Liu Siyuan Jiang Wanlu Wang Jing
Copyright Year	2010
Description	Author affiliation: College of Mechanical Engineering, Yanshan University, Qinhuangdao, China (Liu Siyuan; Jiang Wanlu; Wang Jing)
Abstract	In order to solve the problem that the fault characteristics of hydraulic pump have ambiguity and signal-to-noise of inspected signals is low, a new method based on characteristics of the energy spectrum of vibration signals combing rough set (RS) and principal component analysis (PCA) algorithm is proposed in this paper. This algorithm processes noise abatement for collected vibration signals by wavelet analysis, and decompose-reconstructs characteristic signals at different spectrum; afterwards, eigenvector is constituted by normalized treatment. In addition, it utilizes PCA method to decouple relativity of characteristics among faults in order to reduce dimension of fault characteristics. At last, it establishes rules knowledge base of fault diagnosis through the method of distinguished matrix of the rough set theory for characteristics vector. Moreover, the paper indicates the validity of the method based on experiment of axial piston pump.
Starting Page	2601
Ending Page	2605
File Size	239651
Page Count	5
File Format	PDF
ISBN	9781424477371
e-ISBN	9781424477395
DOI	10.1109/MACE.2010.5536876
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-06-26
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Noise reduction Wavelet analysis Matrix decomposition Principal component analysis (PCA) Hydraulic pump Fault diagnosis Pistons Rough set (RS) Signal processing Set theory Signal analysis Principal component analysis
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Fault Diagnosis of Hydraulic Pump Based on Rough Set and PCA Algorithm

Study on application of Principal Component Analysis to fault detection in hydraulic pump

An improved fault detection algorithm based on wavelet analysis and kernel principal component analysis

Kernel principal component analysis fault diagnosis method based on sound signal processing and its application in hydraulic pump

Self-adaptive signal processing integrated wavelet theory & PCA for process monitoring

Analyze and Fault Diagnosis by Multi-scale PCA

Fault diagnosis method integrated on scale-wavelet power spectrum, rough set and neural network

Improving signal-to-noise ratio (SNR) for inchoate fault detection based on principal component analysis (PCA)

Improved Robust PCA using low-rank denoising with optimal singular value shrinkage

Fault diagnosis based on characteristics of the vibration energy on the hydraulic pump

Similar Documents

Fault Diagnosis of Hydraulic Pump Based on Rough Set and PCA Algorithm

Study on application of Principal Component Analysis to fault detection in hydraulic pump

An improved fault detection algorithm based on wavelet analysis and kernel principal component analysis

Kernel principal component analysis fault diagnosis method based on sound signal processing and its application in hydraulic pump

Self-adaptive signal processing integrated wavelet theory & PCA for process monitoring

Analyze and Fault Diagnosis by Multi-scale PCA

Fault diagnosis method integrated on scale-wavelet power spectrum, rough set and neural network

Improving signal-to-noise ratio (SNR) for inchoate fault detection based on principal component analysis (PCA)

Improved Robust PCA using low-rank denoising with optimal singular value shrinkage

Fault diagnosis based on characteristics of the vibration energy on the hydraulic pump