TY - GEN
T1 - Condition Monitoring of Lubricant Shortage for Gearboxes Based on Compressed Thermal Images
AU - Tang, Xiaoli
AU - Li, Ke
AU - van Vuuren, Pieter A.
AU - Guo, Junfeng
AU - Otuyemi, Funso
AU - Gu, Fengshou
AU - Ball, Andrew D.
N1 - Funding: Funding This research was funded by the NSFC-RS joint research project under grants 11911530177 in China and IE181496 in UK.
PY - 2020/8/28
Y1 - 2020/8/28
N2 - Condition monitoring of gearboxes is a crucial task because gearboxes are essential power transmission components whose failure can lead to a catastrophic breakdown of machines. The common faults of a gearbox system, such as tooth breakage, wear, scuffing, spalling and lubricant starvation, have a significant influence on the inside friction and heat dissipation, and consequently, it changes the temperature field distribution within the gearbox. Thermal imaging is a promising technique in the field of machine condition monitoring via the variation detection of heat distribution. However, the thermal images require significant storage space, a high transfer rate and high-speed hardware. To achieve intelligent and efficient machine condition monitoring with the advanced thermal imaging technique, this study reduces the dimensionality of thermal images of a two-stage gearbox system via compressive sensing (CS) and classifies three different lubricant shortage conditions based on the compressed features with an intelligent convolutional neural network (CNN). The experimental results demonstrate that the compressed thermal images contain sufficient fault information and are capable of diagnosing the inadequate lubrication faults for gearboxes operating at various working conditions.
AB - Condition monitoring of gearboxes is a crucial task because gearboxes are essential power transmission components whose failure can lead to a catastrophic breakdown of machines. The common faults of a gearbox system, such as tooth breakage, wear, scuffing, spalling and lubricant starvation, have a significant influence on the inside friction and heat dissipation, and consequently, it changes the temperature field distribution within the gearbox. Thermal imaging is a promising technique in the field of machine condition monitoring via the variation detection of heat distribution. However, the thermal images require significant storage space, a high transfer rate and high-speed hardware. To achieve intelligent and efficient machine condition monitoring with the advanced thermal imaging technique, this study reduces the dimensionality of thermal images of a two-stage gearbox system via compressive sensing (CS) and classifies three different lubricant shortage conditions based on the compressed features with an intelligent convolutional neural network (CNN). The experimental results demonstrate that the compressed thermal images contain sufficient fault information and are capable of diagnosing the inadequate lubrication faults for gearboxes operating at various working conditions.
KW - Compressive sensing (CS)
KW - Condition monitoring
KW - Convolutional neural network (CNN)
KW - Gearboxes
KW - Thermal imaging
UR - http://www.scopus.com/inward/record.url?scp=85091288147&partnerID=8YFLogxK
UR - https://link.springer.com/chapter/10.1007%2F978-3-030-57745-2_76
U2 - 10.1007/978-3-030-57745-2_76
DO - 10.1007/978-3-030-57745-2_76
M3 - Conference publication
AN - SCOPUS:85091288147
SN - 9783030577445
VL - 166
T3 - Smart Innovation, Systems and Technologies
SP - 927
EP - 938
BT - Advances in Asset Management and Condition Monitoring, COMADEM 2019
A2 - Ball, Andrew
A2 - Gelman, Len
A2 - Rao, B.K.N.
PB - Springer
T2 - 32nd International Congress and Exhibition on Condition Monitoring and Diagnostic Engineering Management, COMADEM 2019
Y2 - 3 September 2019 through 5 September 2019
ER -