Adaptive Diagnosis for Rotating Machineries Using Information Geometrical Kernel-ELM Based on VMD-SVD
Rotating machineries often work under severe and variable operation conditions, which brings challenges to fault diagnosis. To deal with this challenge, this paper discusses the concept of adaptive diagnosis, which means to diagnose faults under variable operation conditions with self-adaptively and...
Main Authors: | , , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2018-01-01
|
Series: | Entropy |
Subjects: | |
Online Access: | http://www.mdpi.com/1099-4300/20/1/73 |
id |
doaj-89c0a2d55a8444cf81a421f9f1deefa8 |
---|---|
record_format |
Article |
spelling |
doaj-89c0a2d55a8444cf81a421f9f1deefa82020-11-24T23:02:45ZengMDPI AGEntropy1099-43002018-01-012017310.3390/e20010073e20010073Adaptive Diagnosis for Rotating Machineries Using Information Geometrical Kernel-ELM Based on VMD-SVDZhipeng Wang0Limin Jia1Yong Qin2State Key Lab of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, ChinaState Key Lab of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, ChinaState Key Lab of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, ChinaRotating machineries often work under severe and variable operation conditions, which brings challenges to fault diagnosis. To deal with this challenge, this paper discusses the concept of adaptive diagnosis, which means to diagnose faults under variable operation conditions with self-adaptively and little prior knowledge or human intervention. To this end, a novel algorithm is proposed, information geometrical extreme learning machine with kernel (IG-KELM). From the perspective of information geometry, the structure and Riemannian metric of Kernel-ELM is specified. Based on the geometrical structure, an IG-based conformal transformation is created to improve the generalization ability and self-adaptability of KELM. The proposed IG-KELM, in conjunction with variation mode decomposition (VMD) and singular value decomposition (SVD) is utilized for adaptive diagnosis: (1) VMD, as a new self-adaptive signal processing algorithm is used to decompose the raw signals into several intrinsic mode functions (IMFs). (2) SVD is used to extract the intrinsic characteristics from the matrix constructed with IMFs. (3) IG-KELM is used to diagnose faults under variable conditions self-adaptively with no requirement of prior knowledge or human intervention. Finally, the proposed method was applied on fault diagnosis of a bearing and hydraulic pump. The results show that the proposed method outperforms the conventional method by up to 7.25% and 7.78% respectively, in percentages of accuracy.http://www.mdpi.com/1099-4300/20/1/73fault diagnosisinformation geometrykernel extreme learning machinevariation mode decomposition |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Zhipeng Wang Limin Jia Yong Qin |
spellingShingle |
Zhipeng Wang Limin Jia Yong Qin Adaptive Diagnosis for Rotating Machineries Using Information Geometrical Kernel-ELM Based on VMD-SVD Entropy fault diagnosis information geometry kernel extreme learning machine variation mode decomposition |
author_facet |
Zhipeng Wang Limin Jia Yong Qin |
author_sort |
Zhipeng Wang |
title |
Adaptive Diagnosis for Rotating Machineries Using Information Geometrical Kernel-ELM Based on VMD-SVD |
title_short |
Adaptive Diagnosis for Rotating Machineries Using Information Geometrical Kernel-ELM Based on VMD-SVD |
title_full |
Adaptive Diagnosis for Rotating Machineries Using Information Geometrical Kernel-ELM Based on VMD-SVD |
title_fullStr |
Adaptive Diagnosis for Rotating Machineries Using Information Geometrical Kernel-ELM Based on VMD-SVD |
title_full_unstemmed |
Adaptive Diagnosis for Rotating Machineries Using Information Geometrical Kernel-ELM Based on VMD-SVD |
title_sort |
adaptive diagnosis for rotating machineries using information geometrical kernel-elm based on vmd-svd |
publisher |
MDPI AG |
series |
Entropy |
issn |
1099-4300 |
publishDate |
2018-01-01 |
description |
Rotating machineries often work under severe and variable operation conditions, which brings challenges to fault diagnosis. To deal with this challenge, this paper discusses the concept of adaptive diagnosis, which means to diagnose faults under variable operation conditions with self-adaptively and little prior knowledge or human intervention. To this end, a novel algorithm is proposed, information geometrical extreme learning machine with kernel (IG-KELM). From the perspective of information geometry, the structure and Riemannian metric of Kernel-ELM is specified. Based on the geometrical structure, an IG-based conformal transformation is created to improve the generalization ability and self-adaptability of KELM. The proposed IG-KELM, in conjunction with variation mode decomposition (VMD) and singular value decomposition (SVD) is utilized for adaptive diagnosis: (1) VMD, as a new self-adaptive signal processing algorithm is used to decompose the raw signals into several intrinsic mode functions (IMFs). (2) SVD is used to extract the intrinsic characteristics from the matrix constructed with IMFs. (3) IG-KELM is used to diagnose faults under variable conditions self-adaptively with no requirement of prior knowledge or human intervention. Finally, the proposed method was applied on fault diagnosis of a bearing and hydraulic pump. The results show that the proposed method outperforms the conventional method by up to 7.25% and 7.78% respectively, in percentages of accuracy. |
topic |
fault diagnosis information geometry kernel extreme learning machine variation mode decomposition |
url |
http://www.mdpi.com/1099-4300/20/1/73 |
work_keys_str_mv |
AT zhipengwang adaptivediagnosisforrotatingmachineriesusinginformationgeometricalkernelelmbasedonvmdsvd AT liminjia adaptivediagnosisforrotatingmachineriesusinginformationgeometricalkernelelmbasedonvmdsvd AT yongqin adaptivediagnosisforrotatingmachineriesusinginformationgeometricalkernelelmbasedonvmdsvd |
_version_ |
1725635265371832320 |