Gearbox fault diagnosis based on continuous Wavelet Transformation of vibration signals measured remotely

Salem Khalifa Al-Arbi, Fengshou Gu, Luyang Guan, Yibo Fan, Andrew Ball

Research output: Contribution to journalConference article

1 Citation (Scopus)

Abstract

In gear condition monitoring, it is often impractical to measure the vibrations directly at or close to their sources. It is common practice to measure the vibration at a location away from the source due to the limitation of accessing to the machine to be monitored. The vibration measured in this way inevitably has high distortions from the vibrations due to the effect of the attenuation of signal paths and the interference from other sources. Suppressing these distortions to obtain useful information is a key issue for remote measurement based condition monitoring. This paper develops a new feature designed for remote gear damage diagnosis based on advanced signal processing. Time synchronous average (TSA) is firstly used to suppress the random noise and interferences. Then Continuous Wavelet Transformation (CWT) of TSA signals is obtained to enhance the fault characteristics. Finally wavelet peak factors and kurtosis are developed as a set of complementary features to classify different faults. The diagnosis results show that these two features can be used to detect and indicate the severity of the gear damage effectively even if vibration signals from a remote motor casing.

Original languageEnglish
Number of pages8
JournalSAE Technical Papers
DOIs
Publication statusPublished - 2010
EventSAE 2010 World Congress and Exhibition - Detroit, United States
Duration: 13 Apr 201013 Apr 2010
https://www.technology.matthey.com/article/54/4/216-222/

Fingerprint

Failure analysis
Gears
Condition monitoring
Signal processing

Cite this

@article{ad31e1c94a5c44ea80eace3e070977c3,
title = "Gearbox fault diagnosis based on continuous Wavelet Transformation of vibration signals measured remotely",
abstract = "In gear condition monitoring, it is often impractical to measure the vibrations directly at or close to their sources. It is common practice to measure the vibration at a location away from the source due to the limitation of accessing to the machine to be monitored. The vibration measured in this way inevitably has high distortions from the vibrations due to the effect of the attenuation of signal paths and the interference from other sources. Suppressing these distortions to obtain useful information is a key issue for remote measurement based condition monitoring. This paper develops a new feature designed for remote gear damage diagnosis based on advanced signal processing. Time synchronous average (TSA) is firstly used to suppress the random noise and interferences. Then Continuous Wavelet Transformation (CWT) of TSA signals is obtained to enhance the fault characteristics. Finally wavelet peak factors and kurtosis are developed as a set of complementary features to classify different faults. The diagnosis results show that these two features can be used to detect and indicate the severity of the gear damage effectively even if vibration signals from a remote motor casing.",
author = "Al-Arbi, {Salem Khalifa} and Fengshou Gu and Luyang Guan and Yibo Fan and Andrew Ball",
year = "2010",
doi = "10.4271/2010-01-0899",
language = "English",
journal = "SAE Technical Papers",
issn = "0148-7191",
publisher = "SAE International",

}

Gearbox fault diagnosis based on continuous Wavelet Transformation of vibration signals measured remotely. / Al-Arbi, Salem Khalifa; Gu, Fengshou; Guan, Luyang; Fan, Yibo; Ball, Andrew.

In: SAE Technical Papers, 2010.

Research output: Contribution to journalConference article

TY - JOUR

T1 - Gearbox fault diagnosis based on continuous Wavelet Transformation of vibration signals measured remotely

AU - Al-Arbi, Salem Khalifa

AU - Gu, Fengshou

AU - Guan, Luyang

AU - Fan, Yibo

AU - Ball, Andrew

PY - 2010

Y1 - 2010

N2 - In gear condition monitoring, it is often impractical to measure the vibrations directly at or close to their sources. It is common practice to measure the vibration at a location away from the source due to the limitation of accessing to the machine to be monitored. The vibration measured in this way inevitably has high distortions from the vibrations due to the effect of the attenuation of signal paths and the interference from other sources. Suppressing these distortions to obtain useful information is a key issue for remote measurement based condition monitoring. This paper develops a new feature designed for remote gear damage diagnosis based on advanced signal processing. Time synchronous average (TSA) is firstly used to suppress the random noise and interferences. Then Continuous Wavelet Transformation (CWT) of TSA signals is obtained to enhance the fault characteristics. Finally wavelet peak factors and kurtosis are developed as a set of complementary features to classify different faults. The diagnosis results show that these two features can be used to detect and indicate the severity of the gear damage effectively even if vibration signals from a remote motor casing.

AB - In gear condition monitoring, it is often impractical to measure the vibrations directly at or close to their sources. It is common practice to measure the vibration at a location away from the source due to the limitation of accessing to the machine to be monitored. The vibration measured in this way inevitably has high distortions from the vibrations due to the effect of the attenuation of signal paths and the interference from other sources. Suppressing these distortions to obtain useful information is a key issue for remote measurement based condition monitoring. This paper develops a new feature designed for remote gear damage diagnosis based on advanced signal processing. Time synchronous average (TSA) is firstly used to suppress the random noise and interferences. Then Continuous Wavelet Transformation (CWT) of TSA signals is obtained to enhance the fault characteristics. Finally wavelet peak factors and kurtosis are developed as a set of complementary features to classify different faults. The diagnosis results show that these two features can be used to detect and indicate the severity of the gear damage effectively even if vibration signals from a remote motor casing.

UR - http://www.scopus.com/inward/record.url?scp=85072354707&partnerID=8YFLogxK

U2 - 10.4271/2010-01-0899

DO - 10.4271/2010-01-0899

M3 - Conference article

JO - SAE Technical Papers

JF - SAE Technical Papers

SN - 0148-7191

ER -