An Integrated Fault Diagnosis Method for Rotating Machinery Based on Smoothness Priors Approach Fluctuation Dispersion Entropy and Density Peak Clustering

In order to fully excavate the fault feature information of rotating machinery and accurately recognize the fault category, a novel fault diagnosis method was proposed, which combines with smoothness priors approach (SPA), fluctuation dispersion entropy (FDE), and density peak clustering (DPC). Firs...

Full description

Saved in:
Bibliographic Details
Main Authors: Hongping Ge, Zhaohui Huang, Huaying Liu
Format: Article
Language:English
Published: Wiley 2022-01-01
Series:Shock and Vibration
Online Access:http://dx.doi.org/10.1155/2022/1507630
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1832553586113904640
author Hongping Ge
Zhaohui Huang
Huaying Liu
author_facet Hongping Ge
Zhaohui Huang
Huaying Liu
author_sort Hongping Ge
collection DOAJ
description In order to fully excavate the fault feature information of rotating machinery and accurately recognize the fault category, a novel fault diagnosis method was proposed, which combines with smoothness priors approach (SPA), fluctuation dispersion entropy (FDE), and density peak clustering (DPC). Firstly, the smoothness priors approach is used to decompose the collected vibration signal of rotating machinery to obtain the trend term and detrend term. Secondly, the fault features of the trend term and detrend term were quantified by fluctuation dispersion entropy to construct eigenvector matrix. Finally, the eigenvector matrix was input into the density peak clustering algorithm for fault recognition and classification. The proposed novel algorithm was applied to the experimental data of the rotating machinery under various working conditions. The experimental results show that our method can precisely identify various fault patterns of rotating machinery. Moreover, our approach can attain higher recognition accuracy than other combination clustering model algorithms involved in this paper.
format Article
id doaj-art-0da946f6f9b7449d83e27da57cac682c
institution Kabale University
issn 1875-9203
language English
publishDate 2022-01-01
publisher Wiley
record_format Article
series Shock and Vibration
spelling doaj-art-0da946f6f9b7449d83e27da57cac682c2025-02-03T05:53:39ZengWileyShock and Vibration1875-92032022-01-01202210.1155/2022/1507630An Integrated Fault Diagnosis Method for Rotating Machinery Based on Smoothness Priors Approach Fluctuation Dispersion Entropy and Density Peak ClusteringHongping Ge0Zhaohui Huang1Huaying Liu2Science and Technology College of Nanchang Hangkong UniversityScience and Technology College of Nanchang Hangkong UniversityScience and Technology College of Nanchang Hangkong UniversityIn order to fully excavate the fault feature information of rotating machinery and accurately recognize the fault category, a novel fault diagnosis method was proposed, which combines with smoothness priors approach (SPA), fluctuation dispersion entropy (FDE), and density peak clustering (DPC). Firstly, the smoothness priors approach is used to decompose the collected vibration signal of rotating machinery to obtain the trend term and detrend term. Secondly, the fault features of the trend term and detrend term were quantified by fluctuation dispersion entropy to construct eigenvector matrix. Finally, the eigenvector matrix was input into the density peak clustering algorithm for fault recognition and classification. The proposed novel algorithm was applied to the experimental data of the rotating machinery under various working conditions. The experimental results show that our method can precisely identify various fault patterns of rotating machinery. Moreover, our approach can attain higher recognition accuracy than other combination clustering model algorithms involved in this paper.http://dx.doi.org/10.1155/2022/1507630
spellingShingle Hongping Ge
Zhaohui Huang
Huaying Liu
An Integrated Fault Diagnosis Method for Rotating Machinery Based on Smoothness Priors Approach Fluctuation Dispersion Entropy and Density Peak Clustering
Shock and Vibration
title An Integrated Fault Diagnosis Method for Rotating Machinery Based on Smoothness Priors Approach Fluctuation Dispersion Entropy and Density Peak Clustering
title_full An Integrated Fault Diagnosis Method for Rotating Machinery Based on Smoothness Priors Approach Fluctuation Dispersion Entropy and Density Peak Clustering
title_fullStr An Integrated Fault Diagnosis Method for Rotating Machinery Based on Smoothness Priors Approach Fluctuation Dispersion Entropy and Density Peak Clustering
title_full_unstemmed An Integrated Fault Diagnosis Method for Rotating Machinery Based on Smoothness Priors Approach Fluctuation Dispersion Entropy and Density Peak Clustering
title_short An Integrated Fault Diagnosis Method for Rotating Machinery Based on Smoothness Priors Approach Fluctuation Dispersion Entropy and Density Peak Clustering
title_sort integrated fault diagnosis method for rotating machinery based on smoothness priors approach fluctuation dispersion entropy and density peak clustering
url http://dx.doi.org/10.1155/2022/1507630
work_keys_str_mv AT hongpingge anintegratedfaultdiagnosismethodforrotatingmachinerybasedonsmoothnesspriorsapproachfluctuationdispersionentropyanddensitypeakclustering
AT zhaohuihuang anintegratedfaultdiagnosismethodforrotatingmachinerybasedonsmoothnesspriorsapproachfluctuationdispersionentropyanddensitypeakclustering
AT huayingliu anintegratedfaultdiagnosismethodforrotatingmachinerybasedonsmoothnesspriorsapproachfluctuationdispersionentropyanddensitypeakclustering
AT hongpingge integratedfaultdiagnosismethodforrotatingmachinerybasedonsmoothnesspriorsapproachfluctuationdispersionentropyanddensitypeakclustering
AT zhaohuihuang integratedfaultdiagnosismethodforrotatingmachinerybasedonsmoothnesspriorsapproachfluctuationdispersionentropyanddensitypeakclustering
AT huayingliu integratedfaultdiagnosismethodforrotatingmachinerybasedonsmoothnesspriorsapproachfluctuationdispersionentropyanddensitypeakclustering