On the use of temperature for online condition monitoring of geared systems - A review

Abstract Gear unit condition monitoring is a key factor for mechanical system reliability management. When they are subjected to failure, gears and bearings may generate excessive vibration, debris and heat. Vibratory, acoustic or debris analyses are proven approaches to perform condition monitoring. An alternative to those methods is to use temperature as a condition indicator to detect gearbox failure. The review focuses on condition monitoring studies which use this thermal approach. According to the failure type and the measurement method, it exists a distinction whether it is contact (e.g. thermocouple) or non-contact temperature sensor (e.g. thermography). Capabilities and limitations of this approach are discussed. It is shown that the use of temperature for condition monitoring has a clear potential as an alternative to vibratory or acoustic health monitoring.

[1]  Mark Craig.  Advanced condition monitoring to predict rolling element bearing wear using multiple in-line and off-line sensing , 2010 .

[2]  Philippe Velex,et al.  Gear and motor fault modeling and detection based on motor current analysis , 2013 .

[3]  Philippe Dufrenoy,et al.  Failure Mechanisms of TGV Brake Discs , 2007 .

[4]  Fabrice Ville,et al.  Prediction of Power Losses Due to Tooth Friction in Gears , 2006 .

[5]  Bo-Suk Yang,et al.  Evaluation of thermography image data for machine fault diagnosis , 2010 .

[6]  Pierre Dehombreux,et al.  Evaluation of the Performance of Infrared Thermography for on-Line Condition Monitoring of Rotating Machines , 2011 .

[7]  Yue Wang,et al.  Supervisory control and data acquisition data-based non-linear state estimation technique for wind turbine gearbox condition monitoring , 2013 .

[8]  Hong Shan Zhao,et al.  Wind Turbine Gearbox Failure Prediction Based on Time Series Analysis and Statistical Process Control , 2011 .

[9]  Qing Wang,et al.  The detection of wind turbine shaft misalignment using temperature monitoring. , 2017 .

[10]  Fabrice Ville,et al.  Investigations on power losses in high-speed gears , 2006 .

[11]  Stephan Ebersbach,et al.  The investigation of the condition and faults of a spur gearbox using vibration and wear debris analysis techniques , 2006 .

[12]  S. Glavatskih.  A method of temperature monitoring in fluid film bearings , 2004 .

[13]  Yingning Qiu,et al.  Applying thermophysics for wind turbine drivetrain fault diagnosis using SCADA data , 2016 .

[14]  Robert J.K. Wood,et al.  Electrostatic wear monitoring of rolling element bearings , 2007 .

[15]  Fabrice Ville,et al.  Gear tooth pitting modelling and detection based on transmission error measurements , 2013 .

[16]  Ahmet Kahraman,et al.  Load-Independent Spin Power Losses of a Spur Gear Pair: Model Formulation , 2009 .

[17]  M. Amarnath,et al.  Assessment of surface contact fatigue failure in a spur geared system based on the tribological and vibration parameter analysis , 2015 .

[18]  Lina Bertling Tjernberg,et al.  An Artificial Neural Network Approach for Early Fault Detection of Gearbox Bearings , 2015, IEEE Transactions on Smart Grid.

[19]  Tom Howard,et al.  Development of a novel bearing concept for improved wind turbine gearbox reliability , 2016 .

[20]  J. Antonino-Daviu,et al.  Detection of mechanical faults in induction machines with infrared thermography: Field cases , 2016, IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society.

[21]  A. Olver.  The Mechanism of Rolling Contact Fatigue: An Update , 2005 .

[22]  Andrew Kusiak,et al.  Analyzing bearing faults in wind turbines: A data-mining approach , 2012 .

[23]  T. Jayakumar,et al.  Infrared thermography for condition monitoring – A review , 2013 .

[24]  Fabrice Ville,et al.  A Note on Flow Regimes and Churning Loss Modeling , 2011 .

[25]  Roque Alfredo Osornio-Rios,et al.  Self-adjustment methodology of a thermal camera for detecting faults in industrial machinery , 2016, IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society.

[26]  Christophe Changenet,et al.  A windage power loss model based on CFD study about the volumetric flow rate expelled by spur gears , 2012 .

[27]  Sofie Van Hoecke,et al.  Thermal image based fault diagnosis for rotating machinery , 2015 .

[28]  Mustafa Demetgul,et al.  Thermal analysis MLP neural network based fault diagnosis on worm gears , 2016 .

[29]  M. Szafarczyk,et al.  Selected Diagnostic Methods for Machine Tools Acceptance Tests , 1985 .

[30]  Baldev Raj,et al.  Condition monitoring of exhaust system blowers using infrared thermography , 2008 .

[31]  David Infield,et al.  Online wind turbine fault detection through automated SCADA data analysis , 2009 .

[32]  Hong-shan Zhao,et al.  Early fault prediction of wind turbine gearbox based on temperature measurement , 2012, 2012 IEEE International Conference on Power System Technology (POWERCON).

[33]  L. Burstein,et al.  Prediction of machine residual service life: method and computing , 2004 .

[34]  Ramiro C. Martins,et al.  Evolution of tooth flank roughness during gear micropitting tests , 2011 .

[35]  Meik Schlechtingen,et al.  Comparative analysis of neural network and regression based condition monitoring approaches for wind turbine fault detection , 2011 .

[36]  Sergei Glavatskih,et al.  Simultaneous monitoring of oil film thickness and temperature in fluid film bearings , 2000 .

[37]  Miguel A. Sanz-Bobi,et al.  SIMAP: Intelligent System for Predictive Maintenance: Application to the health condition monitoring of a windturbine gearbox , 2006, Comput. Ind..

[38]  P. C. Paris,et al.  Study of thermal effects associated with crack propagation during very high cycle fatigue tests , 2008 .

[39]  A. Mohanty,et al.  Shaft misalignment detection by thermal imaging of support bearings , 2015 .

[40]  Trevor S. Slack,et al.  A Review of Rolling Contact Fatigue , 2009 .

[41]  Yong Shen,et al.  A method of health monitoring on helicopter speed reducer based on FMECA , 2016, 2016 Prognostics and System Health Management Conference (PHM-Chengdu).

[42]  Djoeli Satrijo,et al.  Confirmation of Thermal Images and Vibration Signals for Intelligent Machine Fault Diagnostics , 2012 .

[43]  Fabrice Ville,et al.  A Note on Flow Regimes and Churning Loss Modelling , 2011 .

[44]  Sofie Van Hoecke,et al.  Data-driven imbalance and hard particle detection in rotating machinery using infrared thermal imaging , 2017 .

[45]  Dong-Pyo Hong,et al.  Fault Diagnosis of Ball Bearings within Rotational Machines Using the Infrared Thermography Method , 2010 .

[46]  Yingning Qiu,et al.  Monitoring wind turbine gearboxes , 2013 .

[47]  Bernd-Robert Höhn,et al.  Influence of oil temperature on gear failures , 2004 .

[48]  Constantine Tarawneh,et al.  Temperature Profiles of Railroad Tapered Roller Bearings With Defective Inner and Outer Rings , 2016 .

[49]  Fengshou Gu,et al.  Thermal image enhancement using bi-dimensional empirical mode decomposition in combination with relevance vector machine for rotating machinery fault diagnosis , 2013 .

[50]  M. Fillon,et al.  The Significance of Oil Thermal Properties on the Performance of a Tilting-Pad Thrust Bearing , 2002 .

[51]  Zhongxiao Peng,et al.  An integrated approach to fault diagnosis of machinery using wear debris and vibration analysis , 2003 .

[52]  Gui Yun Tian,et al.  Multiple cracks detection and visualization using magnetic flux leakage and eddy current pulsed thermography , 2015 .