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Worm gear condition monitoring and fault detection from thermal images via deep learning method
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General Directorate of Tea Enterprises 53080 Rize, Turkey
Karadeniz Technical University, Mechanical Engineering Department 61080 Trabzon, Turkey
Publication date: 2020-09-30
Eksploatacja i Niezawodność – Maintenance and Reliability 2020;22(3):544-556
Worm gearboxes (WG) are often preferred, because of their high torque, quickly reducing speed capacity and good meshing effectiveness, in many industrial applications. However, WGs may face with some serious problems like high temperature at the speed reducer, gear wearing, pitting, scoring, fractures and damages. In order to prevent any damage, loss of time and money, it is an important issue to detect and classify the faults of WGs and develop the maintenance plans accordingly. The present study addresses the application of the deep learning method, convolutional neural network (CNN), in the field of thermal imaging that were gathered from a test rig operating on different loads and speeds. Deep learning approaches, have proven their powerful capability to exploit faulty information from big data and make intelligently diagnostic decisions. Studies concerning the condition monitoring of WGs in the literature are limited. This is the first study on WGs with infrared thermography rather than vibration and sound measurements which have some deficiencies about hardware requirements, restricted measurement abilities and noisy signals. For comparison, CNN was also trained, with vibration and sound data which were collected from the healthy and faulty WGs. The results of fault diagnosis show that thermal image based CNN model on WG has achieved 100% success rate whereas the vibration performance was 83.3 % and sound performance was 81.7%. As a result, thermal image based CNN model showed a better diagnosing performance than the others for WGs. Moreover, condition monitoring of WGs, can be performed correctly with less measurement costs via thermal imaging methods.
Al-Arbi S K. Condition Monitoring of Gear Systems using Vibration Analysis, Doctoral Dissertation. University of Huddersfield, 2012.
Al-Musawi A K, Anayi F, Packianather M. Three-Phase Induction Motor Fault detection based on Thermal Image Segmentation. Infrared Physics & Technology 2019; 104: 103140,
Bagavathiappan S, Lahiri B B, Saravanan T, Philip J, Jayakumar T. Infrared thermography for condition monitoring, A review. Infrared Physics & Technology 2013; 60: 35-55,
Barz B, Denzler J. Deep learning on small datasets without pre-training using cosine loss. The IEEE Winter Conference on Applications of Computer Vision 2020; 1371-1380,
Carden E P, Fanning P. Vibration based condition monitoring: a review. Structural health monitoring 2004; 3(4): 355-377,
Cohen L. Time-Frequency Analysis, New Jersey: Prentice Hall PTR, 1995.
Dodge S, Karam L. Understanding how image quality affects deep neural networks. In 2016 eighth international conference on quality of multimedia experience (QoMEX) IEEE 2016; 1-6,
Ghodake S, Mishra A K, Deokar A V. A Review Paper on Fault Detection of Worm Gearbox. International Advanced Research Journal in Science, Engineering and Technology 2016; 3(1): 161-164.
Goyal D, Pabla B S, Dhami S S. Condition monitoring parameters for fault diagnosis of fixed axis gearbox: a review. Archives of Computational Methods in Engineering 2017; 24(3): 543-556,
Goyal D, Pabla B S, Dhami S S. Non-contact sensor placement strategy for condition monitoring of rotating machine-elements. Engineering Science and Technology 2019; 22(2): 489-501,
Haberhauer H, Bodenstein F. Maschinenelemente. Berlin: Springer, 2009.
Hoang D T, Kang H J. Rolling element bearing fault diagnosis using convolutional neural network and vibration image. Cognitive Systems Research 2019; 53: 42-50,
Janssens O, Schulz R, Slavkovikj V, Stockman K, Loccufier M, Van de Walle R, Van Hoecke S. Thermal image based fault diagnosis for rotating machinery. Infrared Physics & Technology 2015; 73: 78-87,
Janssens O, Van de Walle R, Loccufier M, Van Hoecke S. Deep learning for infrared thermal image based machine health monitoring. IEEE/ASME Transactions on Mechatronics 2017; 23(1): 151-159,
Jing L, Zhao M, Li P, Xu X. A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox. Measurement 2017; 111: 1-10,
Kannojia S P, Jaiswal G. Effects of Varying Resolution on Performance of CNN based Image Classification: An Experimental Study. International Journal of Computer Sciences and Engineering 2018; 6(9): 451-456,
Kłosowski G, Rymar czyk T, Kania K, Świć A, Cieplak T. Maintenance of industrial reactors supported by deep learning driven ultrasound tomography. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2020; 22(1): 138-147,
Lei Y, Lin J, He Z, Zuo M J. A review on empirical mode decomposition in fault diagnosis of rotating machinery. Mechanical Systems and Signal Processing 2013; 35(1-2): 108-126,
Li C, Sanchez R V, Zurita G, Cerrada M, Cabrera D, Vásquez R E. Gearbox fault diagnosis based on deep random forest fusion of acoustic and vibratory signals. Mechanical Systems and Signal Processing 2016; 76: 283-293,
Li C, Sánchez R V, Zurita G, Cerrada M, Cabrera D. Fault diagnosis for rotating machinery using vibration measurement deep statistical feature learning. Sensors 2016; 16(6): 895,
Li C, Sanchez R V. Gearbox fault identification and classification with convolutional neural Networks. Shock and Vibration 2015; Article ID: 390134,
Li X, Li J, He D, Qu Y. Gear pitting fault diagnosis using raw acoustic emission signal based on deep learning. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2019; 21(3): 403-410,
Li Y, Du X, Wan F, Wang X, Yu H. Rotating machinery fault diagnosis based on convolutional neural network and infrared thermal imaging. Chinese Journal of Aeronautics 2020; 33(2): 427-438,
Li Y, Gu J X, Zhen D, Xu M, Ball A. An Evaluation of Gearbox Condition Monitoring Using Infrared Thermal Images Applied with Convolutional Neural Networks. Sensors 2019; 19(9): 2205,
Li Y, Wang K. Modified convolutional neural network with global average pooling for intelligent fault diagnosis of industrial gearbox. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2020; 22(1): 63-72,
Liu Z, Zhang L. A review of failure modes, condition monitoring and fault diagnosis methods for large-scale wind turbine bearings. Measurement 2020; 149: 107002,
Loutas T H, Roulias D, Pauly E, Kostopoulos V. The combined use of vibration, acoustic emission and oil debris on-line monitoring towards a more effective condition monitoring of rotating machinery. Mechanical Systems and Signal Processing 2011; 25(4): 1339-1352,
Loutas T H, Sotiriades G, Kalaitzoglou I, Kostopoulos V. Condition monitoring of a single-stage gearbox with artificially induced gear cracks utilizing on-line vibration and acoustic emission measurements. Applied Acoustics 2009; 70(9): 1148-1159,
Maldague X. Theory and Practice of Infrared Technology for Non-Destructive Testing. New York: John Wiley-Interscience, 2001.
Meola C. Infrared thermography recent advances and future trends. Sharjah: Bentham Science Publishers, 2012,
Mohanty A R. Machinery Condition Monitoring: Principles and Practices. New York: CRC Press, Taylor&Francis Group, 2015,
Osman S, Wang W. An enhanced Hilbert-Huang transform technique for bearing condition monitoring. Measurement Science and Technology 2013; 24(8): 085004,
Randall R B. Vibration-based Condition Monitoring: Industrial, Aerospace and Automotive Applications. West Sussex: John Wiley & Sons Ltd., 2011,
Ring E F J, Ammer K. Infrared thermal imaging in medicine. Physiological Measurement 2012; 33(3): R33,
Sait A S, Sharaf-Eldeen Y I. A review of gearbox condition monitoring based on vibration analysis techniques diagnostics and prognostics. In: Proulx T (Ed.), Rotating Machinery, Structural Health Monitoring, Shock and Vibration 2011; 5: 307-324,
Sasai S, Zhen Y X, Suetake T, Tanita Y, Omata S, Tagami H. Palpation of the skin with a robot finger: an attempt to measure skin stiffness with a probe loaded with a newly developed tactile vibration sensor and displacement sensor. Skin Research and Technology 1999; 5(4):237-246,
Sharma V, Parey A. A review of gear fault diagnosis using various condition indicators. Procedia Engineering 2016; 144: 253-263,
Sharma V, Parey A. Frequency domain averaging based experimental evaluation of gear fault without tachometer for fluctuating speed conditions. Mechanical Systems and Signal Processing 2017; 85: 278-295,
Singh G, Kumar T C A, Naikan V N A. Induction motor inter turn fault detection using infrared thermographic analysis. Infrared Physics & Technology 2016; 77: 277-282,
Singh G, Naikan V N A. Infrared thermography based diagnosis of inter-turn fault and cooling system failure in three phase induction motor. Infrared Physics & Technology 2017; 87: 134-138,
Sun B, Li M M, Liao B P, Yang X, Cao Y T, Cui B F, Feng Q, Ren Y, Yang, D Z. Time-variant reliability modeling based on hybrid nonprobability method. Archive of Applied Mechanics 2020; 90(2): 209-219,
Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A. Going deeper with convolutions. In: 28th IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston MA, 2015: 1-9,
Tanaka Y, Horita Y, Sano A. Finger-mounted skin vibration sensor for active touch. In: Isokoski P, Springare J. (Eds.), EuroHaptics:International Conference on Human Haptic Sensing and Touch Enabled Computer Applications. Tampere, Finland, 2012: 169-174,
Vashisht R K, Peng Q. Crack detection in the rotor ball bearing system using switching control strategy and Short Time Fourier Transform. Journal of Sound and Vibration 2018; 432: 502-529,
Wang J, Gao R X, Yan R. Multi-scale enveloping order spectrogram for rotating machine health diagnosis. Mechanical Systems and Signal Processing 2014; 46(1): 28-44,
Waqar T, Demetgul M. Thermal analysis MLP neural network based fault diagnosis on worm gears. Measurement 2016; 86: 56-66,
Younus A M, Yang B S, Intelligent fault diagnosis of rotating machinery using infrared thermal image. Expert Systems with Applications 2012; 39(2): 2082-2091,
Zhang X, Kang J, Bechhoefer E, Zhao J. A new feature extraction method for gear fault diagnosis and prognosis. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2014; 16(2): 295-300.
Zhang X, Zhao J. Compound fault detection in gearbox based on time synchronous resample and adaptive variational mode decomposition. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2020; 22(1): 161-169,
Zhang Q, Zhao W, Xiao S G. Fault Diagnosis of Gear Based on Singular Value Decomposition and RBF Neural Network. 2nd International Conference on Frontiers of Sensors Technologies 2017; 470-474,
Zhao R, Wang D Z, Yan R Q, Mao K Z, Shen F, Wang J J. Machine Health Monitoring Using Local Feature-Based Gated Recurrent Unit Networks. IEEE Transactions on Industrial Electronics 2018; 65: 1539-1548,
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