Vibration Signature Analysis using Variable Tukey Window: A Case Study on Bearing Fault Data

被引：0

作者：

Dubey, Rahul ^{[1
]}

Agrawal, Dheeraj ^{[1
]}

机构：

[1] Maulana Azad Natl Inst Technol, Dept Elect & Commun, Bhopal, India

来源：

2015 INTERNATIONAL CONFERENCE ON INDUSTRIAL INSTRUMENTATION AND CONTROL (ICIC) | 2015年

关键词：

Bearing; Signal Processing; EMD; iMF; Hilbert transform; EMPIRICAL MODE DECOMPOSITION; DIAGNOSIS; GEAR;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The bearing is used to provide free linear movement to the moving part or for free rotation around a fixed axis. When the machine is in use, the multi component vibration sinals are enerated. These vibration sinals can be captured by accelerometers. Vibration sinal analysis is a very effective tool in finding bearing fault. The methods based on empirical mode decomposition (EMD) have been used for bearing fault diagnosis in mechanical systems. Boundary distortion is considered as a serious problem in the application of EMD. High values of Kurtosis analysis of the IMFs due to boundary distortion may mislead to faults, even if the fault is not present. In EMD process, the boundary distortion of successive IMFs increases, a variable Tukey window is used in this paper to address the increasing boundary distortion problem. In the analyze method boundary distortion problem is minimized by using a variable window for all IMFs.

引用

页码：540 / 544

页数：5

共 50 条

[21] A Comparative Analysis of Detecting Bearing Fault, Using Infrared Thermography, Vibration Analysis and Air-Borne Sound [J].

Athanasopoulos, Nikolaos G. ;

Botsaris, Pantelis N. .

ADVANCES IN CONDITION MONITORING OF MACHINERY IN NON-STATIONARY OPERATIONS, 2014, :171-181

[22] Bearing Severity Fault Evaluation Using Contour Maps-Case Study [J].

Ricardo Soto-Ocampo, Cesar ;

David Cano-Moreno, Juan ;

Manuel Mera, Jose ;

Maroto, Joaquin .

APPLIED SCIENCES-BASEL, 2021, 11 (14)

[23] Evaluation of Current Signature in Bearing Defects by Envelope Analysis of the vibration in induction motors [J].

dos Santos Areias, Isac Antonio ;

Borges da Silva, Luiz Eduardo ;

Bonaldi, Erik Leandro ;

de Lacerda de Oliveira, Levy Ely ;

Lambert-Torres, Germano ;

Bernardes, Vitor Almeida .

ENERGIES, 2019, 12 (21)

[24] A Novel Fault Detection Method for Rolling Bearings Based on Non-Stationary Vibration Signature Analysis [J].

Zhen, Dong ;

Guo, Junchao ;

Xu, Yuandong ;

Zhang, Hao ;

Gu, Fengshou .

SENSORS, 2019, 19 (18)

[25] New Method of Data Treatment Related to Induction Machine Bearing Fault Using Axial Vibration Analytic Signal [J].

Bouguerne, Abla ;

Lebaroud, Abdesselam ;

Boukadoum, Aziz .

2013 8TH INTERNATIONAL CONFERENCE AND EXHIBITION ON ECOLOGICAL VEHICLES AND RENEWABLE ENERGIES (EVER), 2013,

[26] Bearing Fault Diagnosis Using Wavelet Analysis [J].

Chen, Kang ;

Li, Xiaobing ;

Wang, Feng ;

Wang, Tanglin ;

Wu, Cheng .

2012 INTERNATIONAL CONFERENCE ON QUALITY, RELIABILITY, RISK, MAINTENANCE, AND SAFETY ENGINEERING (ICQR2MSE), 2012, :699-702

[27] Classification-Based Fuel Injection Fault Detection of a Trainset Diesel Engine Using Vibration Signature Analysis [J].

Ayati, Moosa ;

Shirazi, Farzad A. ;

Ansari-Rad, Saeed ;

Zabihihesari, Alireza .

JOURNAL OF DYNAMIC SYSTEMS MEASUREMENT AND CONTROL-TRANSACTIONS OF THE ASME, 2020, 142 (05)

[28] VIBRATION BASED PREDICTIVE FAULT ANALYSIS OF BEARING SEAL FAILURE AND CAVITATION ON INDUSTRIAL MONOBLOCK CENTRIFUGAL PUMP USING DEEP LEARNING ALGORITHM [J].

Manikandan, S. ;

Duraivelu, K. .

JURNAL TEKNOLOGI-SCIENCES & ENGINEERING, 2023, 85 (05) :151-162

[29] Analysis of Statistical Features for Bearing Fault Classification using Ensemble Technique [J].

Udmale, Sandeep S. ;

Singh, Sanjay Kumar .

2019 10TH INTERNATIONAL CONFERENCE ON COMPUTING, COMMUNICATION AND NETWORKING TECHNOLOGIES (ICCCNT), 2019,

[30] Bearing Fault Feature Extraction Using Autoregressive Coefficients, Linear Discriminant Analysis and Support Vector Machine Under Variable Operating Conditions [J].

Kedadouche, Mourad ;

Liu, Zhaoheng ;

Thomas, Marc .

ADVANCES IN CONDITION MONITORING OF MACHINERY IN NON-STATIONARY OPERATIONS, CMMNO 2016, 2018, 9

← 1 2 3 4 5 →