Skip to main content
Erschienen in:

15.11.2017 | Original Article

Motor Fault Diagnosis Based on Short-time Fourier Transform and Convolutional Neural Network

verfasst von: Li-Hua Wang, Xiao-Ping Zhao, Jia-Xin Wu, Yang-Yang Xie, Yong-Hong Zhang

Erschienen in: Chinese Journal of Mechanical Engineering | Ausgabe 6/2017

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

With the rapid development of mechanical equipment, the mechanical health monitoring field has entered the era of big data. However, the method of manual feature extraction has the disadvantages of low efficiency and poor accuracy, when handling big data. In this study, the research object was the asynchronous motor in the drivetrain diagnostics simulator system. The vibration signals of different fault motors were collected. The raw signal was pretreated using short time Fourier transform (STFT) to obtain the corresponding time-frequency map. Then, the feature of the time-frequency map was adaptively extracted by using a convolutional neural network (CNN). The effects of the pretreatment method, and the hyper parameters of network diagnostic accuracy, were investigated experimentally. The experimental results showed that the influence of the preprocessing method is small, and that the batch-size is the main factor affecting accuracy and training efficiency. By investigating feature visualization, it was shown that, in the case of big data, the extracted CNN features can represent complex mapping relationships between signal and health status, and can also overcome the prior knowledge and engineering experience requirement for feature extraction, which is used by traditional diagnosis methods. This paper proposes a new method, based on STFT and CNN, which can complete motor fault diagnosis tasks more intelligently and accurately.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Literatur
1.
Zurück zum Zitat Chuan Li, R V Sanchez, G Zurita, et al. Gearbox fault diagnosis based on deep random forest fusion of acoustic and vibratory signals. Mechanical Systems and Signal Processing, 2016, 76–77: 283–293. Chuan Li, R V Sanchez, G Zurita, et al. Gearbox fault diagnosis based on deep random forest fusion of acoustic and vibratory signals. Mechanical Systems and Signal Processing, 2016, 76–77: 283–293.
2.
Zurück zum Zitat Ya-Guo Lei, Nai-Peng Li, Jing Lin, et al. Two new features for condition monitoring and fault diagnosis of planetary gearboxes. Journal of Vibration & Control, 2013, 21(4): 755–764. Ya-Guo Lei, Nai-Peng Li, Jing Lin, et al. Two new features for condition monitoring and fault diagnosis of planetary gearboxes. Journal of Vibration & Control, 2013, 21(4): 755–764.
3.
Zurück zum Zitat Jun-Jian Hou, Wei-Kang Jiang, Wen-Bo Lu. Application of a near-field acoustic holography-based diagnosis technique in gearbox fault diagnosis. Journal of Vibration & Control, 2013, 19(1): 3–13. Jun-Jian Hou, Wei-Kang Jiang, Wen-Bo Lu. Application of a near-field acoustic holography-based diagnosis technique in gearbox fault diagnosis. Journal of Vibration & Control, 2013, 19(1): 3–13.
4.
Zurück zum Zitat A M D Younus, B S Yang. Intelligent fault diagnosis of rotating machinery using infrared thermal image. Expert Systems with Applications, 2012, 39(2): 2082–2091. A M D Younus, B S Yang. Intelligent fault diagnosis of rotating machinery using infrared thermal image. Expert Systems with Applications, 2012, 39(2): 2082–2091.
5.
Zurück zum Zitat J. R Ottewill, M Orkisz. Condition monitoring of gearboxes using synchronously averaged electric motor signals. Mechanical Systems & Signal Processing, 2013, 38(2): 482–498. J. R Ottewill, M Orkisz. Condition monitoring of gearboxes using synchronously averaged electric motor signals. Mechanical Systems & Signal Processing, 2013, 38(2): 482–498.
6.
Zurück zum Zitat A Contin, S D’orlando, G Fenu, et al. Experiments on actuator fault diagnosis: the case of a nonlinearly controlled AC motor. Control Conference, 2015: 2747–2752. A Contin, S D’orlando, G Fenu, et al. Experiments on actuator fault diagnosis: the case of a nonlinearly controlled AC motor. Control Conference, 2015: 2747–2752.
7.
Zurück zum Zitat A Glowacz. DC motor fault analysis with the use of acoustic signals, Coiflet wavelet transform, and K-nearest neighbor classifier. Archives of Acoustics, 2015, 40(3): 321–327. A Glowacz. DC motor fault analysis with the use of acoustic signals, Coiflet wavelet transform, and K-nearest neighbor classifier. Archives of Acoustics, 2015, 40(3): 321–327.
8.
Zurück zum Zitat Hui-Min Zhao, Cai-Hua Fang, Deng Wu. Research on motor fault diagnosis model for support vector machine based on Intelligent optimization methods. Journal of Dalian Jiaotong University, 2016, 37(1): 92–96. (in Chinese). Hui-Min Zhao, Cai-Hua Fang, Deng Wu. Research on motor fault diagnosis model for support vector machine based on Intelligent optimization methods. Journal of Dalian Jiaotong University, 2016, 37(1): 92–96. (in Chinese).
9.
Zurück zum Zitat Ping Li, Xue-Jun Li, Ling-Li Jiang, et al. Fault diagnosis of asynchronous motor based on KPCA and PSOSVM. Journal of Vibration Measurement & Diagnosis, 2014, 34(4): 616–620. (in Chinese). Ping Li, Xue-Jun Li, Ling-Li Jiang, et al. Fault diagnosis of asynchronous motor based on KPCA and PSOSVM. Journal of Vibration Measurement & Diagnosis, 2014, 34(4): 616–620. (in Chinese).
10.
Zurück zum Zitat D H Pandya, S H Upadhyay, S P Harsha. Fault diagnosis of rolling element bearing by using multinomial logistic regression and wavelet packet transform. Soft Computing, 2014, 18(2): 255–266. D H Pandya, S H Upadhyay, S P Harsha. Fault diagnosis of rolling element bearing by using multinomial logistic regression and wavelet packet transform. Soft Computing, 2014, 18(2): 255–266.
11.
Zurück zum Zitat M Khazaee, H Ahmadi, M Omid, et al. Classifier fusion of vibration and acoustic signals for fault diagnosis and classification of planetary gears based on dempster-shafer evidence theory. ARCHIVE Proceedings of the Institution of Mechanical Engineers Part E Journal of Process Mechanical Engineering, 2014, 228(1): 21–32. M Khazaee, H Ahmadi, M Omid, et al. Classifier fusion of vibration and acoustic signals for fault diagnosis and classification of planetary gears based on dempster-shafer evidence theory. ARCHIVE Proceedings of the Institution of Mechanical Engineers Part E Journal of Process Mechanical Engineering, 2014, 228(1): 21–32.
12.
Zurück zum Zitat G E Hinton, S Osindero, Y W Teh. A fast learning algorithm for deep belief nets. Neural Computation, 2006, 18(7): 1527–1554. G E Hinton, S Osindero, Y W Teh. A fast learning algorithm for deep belief nets. Neural Computation, 2006, 18(7): 1527–1554.
13.
Zurück zum Zitat G Hinton, Deng Li, Yu Dong, et al. Deep neural networks for acoustic modeling in speech recognition: the shared views of fourresearch groups. IEEE Signal Processing Magazine, 2012, 29(6): 82–97. G Hinton, Deng Li, Yu Dong, et al. Deep neural networks for acoustic modeling in speech recognition: the shared views of fourresearch groups. IEEE Signal Processing Magazine, 2012, 29(6): 82–97.
14.
Zurück zum Zitat A Krizhevsky, I Sutskever, G E Hinton. Imagenet classification with deep convolutional neural networks. International Conference on Neural Information Processing Systems, 2012, 25(2): 1097–1105. A Krizhevsky, I Sutskever, G E Hinton. Imagenet classification with deep convolutional neural networks. International Conference on Neural Information Processing Systems, 2012, 25(2): 1097–1105.
15.
Zurück zum Zitat Yan-Feng Li, Xin-Qing Wang, Mei-Jun Zhang, et al. An approach to fault diagnosis of rolling bearing using SVD and multiple DBN classifiers. Journal of Shanghai Jiaotong University, 2015, 49(5): 681–686. (in Chinese). Yan-Feng Li, Xin-Qing Wang, Mei-Jun Zhang, et al. An approach to fault diagnosis of rolling bearing using SVD and multiple DBN classifiers. Journal of Shanghai Jiaotong University, 2015, 49(5): 681–686. (in Chinese).
16.
Zurück zum Zitat Jia Feng, Ya-Guo Lei, Jing Lin, et al. Deep neural networks: a promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data. Mechanical Systems & Signal Processing, 2016, 72–73: 303–315. Jia Feng, Ya-Guo Lei, Jing Lin, et al. Deep neural networks: a promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data. Mechanical Systems & Signal Processing, 2016, 72–73: 303–315.
17.
Zurück zum Zitat I Arel, D C Rose, T P Karnowski. Research frontier: deep machine learning–a new frontier in artificial intelligence research. IEEE Computational Intelligence Magazine, 2010, 5(4): 13–18. I Arel, D C Rose, T P Karnowski. Research frontier: deep machine learning–a new frontier in artificial intelligence research. IEEE Computational Intelligence Magazine, 2010, 5(4): 13–18.
18.
Zurück zum Zitat P Tamilselvan, Ping-Peng Wang. Failure diagnosis using deep belief learning based health state classification. Reliability Engineering & Systems Safety, 2013, 115(7): 124–135. P Tamilselvan, Ping-Peng Wang. Failure diagnosis using deep belief learning based health state classification. Reliability Engineering & Systems Safety, 2013, 115(7): 124–135.
19.
Zurück zum Zitat P Vincent, H Larochelle, I Lajoie, et al. Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. Journal of Machine Learning Research, 2010, 11(12): 3371–3408. P Vincent, H Larochelle, I Lajoie, et al. Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. Journal of Machine Learning Research, 2010, 11(12): 3371–3408.
20.
Zurück zum Zitat Zhao-Feng Zhang, Long-Biao Wang, A Kai, et al. Deep neural network-based bottleneck feature and denoising autoencoder-based dereverberation for distant-talking speaker identification. Eurasip Journal on Audio, Speech, and Music Processing, 2015, 2015(1): 1–13. Zhao-Feng Zhang, Long-Biao Wang, A Kai, et al. Deep neural network-based bottleneck feature and denoising autoencoder-based dereverberation for distant-talking speaker identification. Eurasip Journal on Audio, Speech, and Music Processing, 2015, 2015(1): 1–13.
21.
Zurück zum Zitat Chen Lu, Zhen-Ya Wang, Wei-Li Qin, et al. Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification. Signal Processing, 2017, 130: 377–388. Chen Lu, Zhen-Ya Wang, Wei-Li Qin, et al. Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification. Signal Processing, 2017, 130: 377–388.
22.
Zurück zum Zitat S H Nawab, T F Quatieri. Short-time fourier transform. Advanced Topics in Signal Processing, 1988, 32(2): 289–337. S H Nawab, T F Quatieri. Short-time fourier transform. Advanced Topics in Signal Processing, 1988, 32(2): 289–337.
23.
Zurück zum Zitat S Lawrence, C L Giles, A C Tsoi, et al. Face recognition: a convolutional neural-network approach. IEEE Transactions on Neural Networks, 1997, 8(1): 98–113. S Lawrence, C L Giles, A C Tsoi, et al. Face recognition: a convolutional neural-network approach. IEEE Transactions on Neural Networks, 1997, 8(1): 98–113.
24.
Zurück zum Zitat Wei-Yang Liu, Yan-Dong Wen, Zhi-Ding Wen, et al. Large-margin softmax loss for convolutional neural networks. International Conference on International Conference on Machine Learning, 2016: 507–516. Wei-Yang Liu, Yan-Dong Wen, Zhi-Ding Wen, et al. Large-margin softmax loss for convolutional neural networks. International Conference on International Conference on Machine Learning, 2016: 507–516.
25.
Zurück zum Zitat D Erhan, Y Bengio, A Courville, et al. Why does unsupervised pre-training help deep learning? Journal of Machine Learning Research, 2010, 11(3): 625–660. D Erhan, Y Bengio, A Courville, et al. Why does unsupervised pre-training help deep learning? Journal of Machine Learning Research, 2010, 11(3): 625–660.
26.
Zurück zum Zitat I Daubechies. The wavelet transform, time-frequency localisation and signal analysis. IEEE Transactions on Information Theory, 1990, 36(5): 961–1005. I Daubechies. The wavelet transform, time-frequency localisation and signal analysis. IEEE Transactions on Information Theory, 1990, 36(5): 961–1005.
27.
Zurück zum Zitat S Ioffe, C Szegedy. Batch normalization: accelerating deep network training by reducing internal covariate shift. Computer Science, 2015: 448–456. S Ioffe, C Szegedy. Batch normalization: accelerating deep network training by reducing internal covariate shift. Computer Science, 2015: 448–456.
28.
Zurück zum Zitat P Ahlgren, B Jarneving, P B R Ahlgren. Requirements for a cocitation similarity measure, with special reference to Pearson’s correlation coefficient. Journal of the American Society for Information Science & Technology, 2003, 54(6): 550–560. P Ahlgren, B Jarneving, P B R Ahlgren. Requirements for a cocitation similarity measure, with special reference to Pearson’s correlation coefficient. Journal of the American Society for Information Science & Technology, 2003, 54(6): 550–560.
29.
Zurück zum Zitat Shu-Fang Li, Wei-Dong Zhou, Qi Yuan, et al. Feature extraction and recognition of ictal EEG using EMD and SVM. Computers in Biology & Medicine, 2013, 43(7): 807–816. Shu-Fang Li, Wei-Dong Zhou, Qi Yuan, et al. Feature extraction and recognition of ictal EEG using EMD and SVM. Computers in Biology & Medicine, 2013, 43(7): 807–816.
30.
Zurück zum Zitat Neng Ren. PCA-SVM-based automated fault detection and diagnosis (AFDD) for vapor-compression refrigeration systems. Hvac & R Research, 2010, 16(3): 295–313. Neng Ren. PCA-SVM-based automated fault detection and diagnosis (AFDD) for vapor-compression refrigeration systems. Hvac & R Research, 2010, 16(3): 295–313.
Metadaten
Titel
Motor Fault Diagnosis Based on Short-time Fourier Transform and Convolutional Neural Network
verfasst von
Li-Hua Wang
Xiao-Ping Zhao
Jia-Xin Wu
Yang-Yang Xie
Yong-Hong Zhang
Publikationsdatum
15.11.2017
Verlag
Chinese Mechanical Engineering Society
Erschienen in
Chinese Journal of Mechanical Engineering / Ausgabe 6/2017
Print ISSN: 1000-9345
Elektronische ISSN: 2192-8258
DOI
https://doi.org/10.1007/s10033-017-0190-5

    Marktübersichten

    Die im Laufe eines Jahres in der „adhäsion“ veröffentlichten Marktübersichten helfen Anwendern verschiedenster Branchen, sich einen gezielten Überblick über Lieferantenangebote zu verschaffen.