Data fusion of acceleration and velocity features (dFAVF) approach for fault diagnosis in rotating machines

Kenisuomo Luwei; Jyoti Sinha; Akilu Yunusa-Kaltungo; Keri Elbhbah

Data fusion of acceleration and velocity features (dFAVF) approach for fault diagnosis in rotating machines

Kenisuomo Luwei, Jyoti Sinha, Akilu Yunusa-Kaltungo, Keri Elbhbah

Research output: Contribution to conference › Paper › peer-review

Abstract

Earlier research studies have suggested the unified
vibration-based approach for fault diagnosis (FD) in identical
machines with different foundation flexibilities and multi-rotating
speeds. Intially the acceleration-based features were used for this
approach then further work optimised the approach by combining
acceleration and velocity features from vibration data for analysis.
However the optimised approach was only tested on the identical
machines rotating at different speeds below the machine’s first
critical speed. The current paper tends to observe the optimised
approach when applied to a test rig operating below and above the
machine’s first critical speed.

Original language	English
Pages	1-6
Number of pages	6
Publication status	Published - 13 Oct 2018
Event	14th International Conference on VIBRATION ENGINEERING AND TECHNOLOGY OF MACHINERY - The Department of Civil Engineering of Faculdade de Ciências e Tecnologia of Universidade Nova de Lisboa (DEC/FCT/UNL) and IDMEC - Institute of Engineering Mechanics of Instituto Superior Técnico of University of Lisbon (IDMEC/IST/UL), Lisbon, Portugal Duration: 10 Sept 2018 → 13 Sept 2018 Conference number: 14 http://www.conf.pt/index.php/vetomac

Conference

Conference	14th International Conference on VIBRATION ENGINEERING AND TECHNOLOGY OF MACHINERY
Abbreviated title	VETOMAC 2018
Country/Territory	Portugal
City	Lisbon
Period	10/09/18 → 13/09/18
Internet address	http://www.conf.pt/index.php/vetomac

Keywords

Rotating machines
Condition monitoring
Vibration
Data fusion
faults diagnosis

Cite this

@conference{ad7d0dca98cc4525b112528d8bccd1be,

title = "Data fusion of acceleration and velocity features (dFAVF) approach for fault diagnosis in rotating machines",

abstract = "Earlier research studies have suggested the unifiedvibration-based approach for fault diagnosis (FD) in identicalmachines with different foundation flexibilities and multi-rotatingspeeds. Intially the acceleration-based features were used for thisapproach then further work optimised the approach by combiningacceleration and velocity features from vibration data for analysis.However the optimised approach was only tested on the identicalmachines rotating at different speeds below the machine{\textquoteright}s firstcritical speed. The current paper tends to observe the optimisedapproach when applied to a test rig operating below and above themachine{\textquoteright}s first critical speed.",

keywords = "Rotating machines, Condition monitoring, Vibration, Data fusion, faults diagnosis",

author = "Kenisuomo Luwei and Jyoti Sinha and Akilu Yunusa-Kaltungo and Keri Elbhbah",

year = "2018",

month = oct,

day = "13",

language = "English",

pages = "1--6",

note = "14th International Conference on VIBRATION ENGINEERING AND TECHNOLOGY OF MACHINERY, VETOMAC 2018 ; Conference date: 10-09-2018 Through 13-09-2018",

url = "http://www.conf.pt/index.php/vetomac ",

}

Data fusion of acceleration and velocity features (dFAVF) approach for fault diagnosis in rotating machines. / Luwei, Kenisuomo; Sinha, Jyoti ; Yunusa-Kaltungo, Akilu et al.
2018. 1-6 Paper presented at 14th International Conference on VIBRATION ENGINEERING AND TECHNOLOGY OF MACHINERY, Lisbon, Portugal.

Research output: Contribution to conference › Paper › peer-review

TY - CONF

T1 - Data fusion of acceleration and velocity features (dFAVF) approach for fault diagnosis in rotating machines

AU - Luwei, Kenisuomo

AU - Sinha, Jyoti

AU - Yunusa-Kaltungo, Akilu

AU - Elbhbah, Keri

N1 - Conference code: 14

PY - 2018/10/13

Y1 - 2018/10/13

N2 - Earlier research studies have suggested the unifiedvibration-based approach for fault diagnosis (FD) in identicalmachines with different foundation flexibilities and multi-rotatingspeeds. Intially the acceleration-based features were used for thisapproach then further work optimised the approach by combiningacceleration and velocity features from vibration data for analysis.However the optimised approach was only tested on the identicalmachines rotating at different speeds below the machine’s firstcritical speed. The current paper tends to observe the optimisedapproach when applied to a test rig operating below and above themachine’s first critical speed.

AB - Earlier research studies have suggested the unifiedvibration-based approach for fault diagnosis (FD) in identicalmachines with different foundation flexibilities and multi-rotatingspeeds. Intially the acceleration-based features were used for thisapproach then further work optimised the approach by combiningacceleration and velocity features from vibration data for analysis.However the optimised approach was only tested on the identicalmachines rotating at different speeds below the machine’s firstcritical speed. The current paper tends to observe the optimisedapproach when applied to a test rig operating below and above themachine’s first critical speed.

KW - Rotating machines

KW - Condition monitoring

KW - Vibration

KW - Data fusion

KW - faults diagnosis

UR - https://doi.org/10.1051/matecconf/201821121005

M3 - Paper

SP - 1

EP - 6

T2 - 14th International Conference on VIBRATION ENGINEERING AND TECHNOLOGY OF MACHINERY

Y2 - 10 September 2018 through 13 September 2018

ER -

Data fusion of acceleration and velocity features (dFAVF) approach for fault diagnosis in rotating machines

Abstract

Conference

Keywords

Other files and links

Fingerprint

Cite this