Gear wear monitoring by modulation signal bispectrum based on motor current signal analysis

Ruiliang Zhang, Fengshou Gu, Haram Mansaf, Tie Wang, Andrew Ball

Research output: Contribution to journalArticle

20 Citations (Scopus)

Abstract

Gears are important mechanical components for power transmissions. Tooth wear is one of the most common failure modes, which can present throughout a gear’s lifetime. It is significant to accurately monitor gear wear progression in order to take timely predictive maintenances. Motor current signature analysis (MCSA) is an effective and non-intrusive approach which is able to monitor faults from both electrical and mechanical systems. However, little research has been reported in monitoring the gear wear and estimating its severity based on MCSA. This paper presents a novel gear wear monitoring method through a modulation signal bispectrum based motor current signal analysis (MSB-MCSA). For a steady gear transmission, it is inevitable to exist load and speed oscillations due to various errors including wears. These oscillations can induce small modulations in the current signals of the driving motor. MSB is particularly effective in characterising such small modulation signals. Based on these understandings, the monitoring process was implemented based on the current signals from a run-to-failure test of an industrial two stages helical gearbox under a moderate accelerated fatigue process. At the initial operation of the test, MSB analysis results showed that the peak values at the bifrequencies of gear rotations and the power supply can be effective monitoring features for identifying faulty gears and wear severity as they exhibit agreeable changes with gear loads. A monotonically increasing trend established by these features allows a clear indication of the gear wear progression. The dismantle inspection at 477 h of operation, made when one of the monitored features is about 123% higher than its baseline, has found that there are severe scuffing wear marks on a number of tooth surfaces on the driving gear, showing that the gear endures a gradual wear process during its long test operation. Therefore, it is affirmed that the MSB-MSCA approach proposed is reliable and accurate for monitoring gear wear deterioration.
Original languageEnglish
Pages (from-to)202-213
Number of pages12
JournalMechanical Systems and Signal Processing
Volume94
Early online date6 Mar 2017
DOIs
Publication statusPublished - 15 Sep 2017

Fingerprint

Signal analysis
Gears
Modulation
Wear of materials
Monitoring
Gear teeth
Process monitoring
Power transmission
Failure modes
Deterioration
Inspection

Cite this

@article{0ed50a24fa5646ddac12e1d1da7433c8,
title = "Gear wear monitoring by modulation signal bispectrum based on motor current signal analysis",
abstract = "Gears are important mechanical components for power transmissions. Tooth wear is one of the most common failure modes, which can present throughout a gear’s lifetime. It is significant to accurately monitor gear wear progression in order to take timely predictive maintenances. Motor current signature analysis (MCSA) is an effective and non-intrusive approach which is able to monitor faults from both electrical and mechanical systems. However, little research has been reported in monitoring the gear wear and estimating its severity based on MCSA. This paper presents a novel gear wear monitoring method through a modulation signal bispectrum based motor current signal analysis (MSB-MCSA). For a steady gear transmission, it is inevitable to exist load and speed oscillations due to various errors including wears. These oscillations can induce small modulations in the current signals of the driving motor. MSB is particularly effective in characterising such small modulation signals. Based on these understandings, the monitoring process was implemented based on the current signals from a run-to-failure test of an industrial two stages helical gearbox under a moderate accelerated fatigue process. At the initial operation of the test, MSB analysis results showed that the peak values at the bifrequencies of gear rotations and the power supply can be effective monitoring features for identifying faulty gears and wear severity as they exhibit agreeable changes with gear loads. A monotonically increasing trend established by these features allows a clear indication of the gear wear progression. The dismantle inspection at 477 h of operation, made when one of the monitored features is about 123{\%} higher than its baseline, has found that there are severe scuffing wear marks on a number of tooth surfaces on the driving gear, showing that the gear endures a gradual wear process during its long test operation. Therefore, it is affirmed that the MSB-MSCA approach proposed is reliable and accurate for monitoring gear wear deterioration.",
keywords = "Motor current signal analysis, Modulation signal bispectrum, Gear wear, Gearbox monitoring",
author = "Ruiliang Zhang and Fengshou Gu and Haram Mansaf and Tie Wang and Andrew Ball",
note = "Metadata checked - JC; 18/08/17",
year = "2017",
month = "9",
day = "15",
doi = "10.1016/j.ymssp.2017.02.037",
language = "English",
volume = "94",
pages = "202--213",
journal = "Mechanical Systems and Signal Processing",
issn = "0888-3270",
publisher = "Academic Press Inc.",

}

Gear wear monitoring by modulation signal bispectrum based on motor current signal analysis. / Zhang, Ruiliang; Gu, Fengshou; Mansaf, Haram; Wang, Tie; Ball, Andrew.

In: Mechanical Systems and Signal Processing, Vol. 94, 15.09.2017, p. 202-213.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Gear wear monitoring by modulation signal bispectrum based on motor current signal analysis

AU - Zhang, Ruiliang

AU - Gu, Fengshou

AU - Mansaf, Haram

AU - Wang, Tie

AU - Ball, Andrew

N1 - Metadata checked - JC; 18/08/17

PY - 2017/9/15

Y1 - 2017/9/15

N2 - Gears are important mechanical components for power transmissions. Tooth wear is one of the most common failure modes, which can present throughout a gear’s lifetime. It is significant to accurately monitor gear wear progression in order to take timely predictive maintenances. Motor current signature analysis (MCSA) is an effective and non-intrusive approach which is able to monitor faults from both electrical and mechanical systems. However, little research has been reported in monitoring the gear wear and estimating its severity based on MCSA. This paper presents a novel gear wear monitoring method through a modulation signal bispectrum based motor current signal analysis (MSB-MCSA). For a steady gear transmission, it is inevitable to exist load and speed oscillations due to various errors including wears. These oscillations can induce small modulations in the current signals of the driving motor. MSB is particularly effective in characterising such small modulation signals. Based on these understandings, the monitoring process was implemented based on the current signals from a run-to-failure test of an industrial two stages helical gearbox under a moderate accelerated fatigue process. At the initial operation of the test, MSB analysis results showed that the peak values at the bifrequencies of gear rotations and the power supply can be effective monitoring features for identifying faulty gears and wear severity as they exhibit agreeable changes with gear loads. A monotonically increasing trend established by these features allows a clear indication of the gear wear progression. The dismantle inspection at 477 h of operation, made when one of the monitored features is about 123% higher than its baseline, has found that there are severe scuffing wear marks on a number of tooth surfaces on the driving gear, showing that the gear endures a gradual wear process during its long test operation. Therefore, it is affirmed that the MSB-MSCA approach proposed is reliable and accurate for monitoring gear wear deterioration.

AB - Gears are important mechanical components for power transmissions. Tooth wear is one of the most common failure modes, which can present throughout a gear’s lifetime. It is significant to accurately monitor gear wear progression in order to take timely predictive maintenances. Motor current signature analysis (MCSA) is an effective and non-intrusive approach which is able to monitor faults from both electrical and mechanical systems. However, little research has been reported in monitoring the gear wear and estimating its severity based on MCSA. This paper presents a novel gear wear monitoring method through a modulation signal bispectrum based motor current signal analysis (MSB-MCSA). For a steady gear transmission, it is inevitable to exist load and speed oscillations due to various errors including wears. These oscillations can induce small modulations in the current signals of the driving motor. MSB is particularly effective in characterising such small modulation signals. Based on these understandings, the monitoring process was implemented based on the current signals from a run-to-failure test of an industrial two stages helical gearbox under a moderate accelerated fatigue process. At the initial operation of the test, MSB analysis results showed that the peak values at the bifrequencies of gear rotations and the power supply can be effective monitoring features for identifying faulty gears and wear severity as they exhibit agreeable changes with gear loads. A monotonically increasing trend established by these features allows a clear indication of the gear wear progression. The dismantle inspection at 477 h of operation, made when one of the monitored features is about 123% higher than its baseline, has found that there are severe scuffing wear marks on a number of tooth surfaces on the driving gear, showing that the gear endures a gradual wear process during its long test operation. Therefore, it is affirmed that the MSB-MSCA approach proposed is reliable and accurate for monitoring gear wear deterioration.

KW - Motor current signal analysis

KW - Modulation signal bispectrum

KW - Gear wear

KW - Gearbox monitoring

UR - http://www.sciencedirect.com/science/journal/08883270/94/supp/C?sdc=1

U2 - 10.1016/j.ymssp.2017.02.037

DO - 10.1016/j.ymssp.2017.02.037

M3 - Article

VL - 94

SP - 202

EP - 213

JO - Mechanical Systems and Signal Processing

JF - Mechanical Systems and Signal Processing

SN - 0888-3270

ER -