The Fault Diagnosis based on Deep Long Short-Term Memory Model from the Vibration Signals in the Computer Numerical Control Machines

Kemal Polat

Department of Electrical and Electronics Engineering, Faculty of Engineering, Bolu Abant Izzet Baysal University, 14280, Bolu, Turkey
E-mail: [email protected]

Rotating machines have become indispensable in every field of life. Because of people's commitment to machines, the machine's faults have devastating consequences. Fault analysis of devices is common in the industry. Studies in the literature mostly work on simple models. These simple models become inefficient as data complexity increases. In this paper, three types of faults are detected in the drill bit of the CNC (Computer Numerical Control) machine. Deep LSTM (Long Short-Term Memory)-based classification model has been proposed for fault diagnosis of CNC machines. Different structures can be combined to obtain higher accuracy rates. The proposed method consists of two stages: (I) the feature extraction including time, frequency domain, and Morlet wavelet coefficients information from vibration signals from CNC machines, (II) the classification of fault types using deep LSTM model based on the extracted features. LSTM structures having two layers, three layers, four layers, and five layers have been proposed and then compared with each other with respect to the classification accuracy of fault diagnosis. The highest accuracy rate obtained in this study was 99.53%. It can be seen that deep LSTM gives outstanding results in the structures with sequential data input and the proposed system gives promising results in the field of fault analysis of machines.

LSTM (Long Short-Term Memory) classification method, Deep learning, Machine based Fault Diagnosis, Drill Bit Fault

Kemal Polat (2020). The Fault Diagnosis based on Deep Long Short-Term Memory Model from the Vibration Signals in the Computer Numerical Control Machines. Journal of the Institute of Electronics and Computer, 2, 72-92. https://doi.org/10.33969/JIEC.2020.21006.

[1] Liu, R., Yang, B., Zio, E., & Chen, X. (2018). Artificial intelligence for fault diagnosis of rotating machinery: A review. Mechanical Systems and Signal Processing, 108, 33-47.
[2] Peter, W. T., Yang, W. X., & Tam, H. Y. (2004). Machine fault diagnosis through an effective exact wavelet analysis. Journal of sound and vibration, 277(4-5), 1005-1024.
[3] Benbouzid, M. E. H. (2000). A review of induction motors signature analysis as a medium for faults detection. IEEE transactions on industrial electronics, 47(5), 984-993.
[4] Tavner, P. J. (2008). Review of condition monitoring of rotating electrical machines. IET Electric Power Applications, 2(4), 215-247.
[5] Verma, N. K., Sevakula, R. K., Dixit, S., & Salour, A. (2015). Data-driven approach for drill bit monitoring. IEEE Reliab. Mag, 19-26.
[6] Wang, D. (2016). K-nearest neighbors based methods for identification of different gear crack levels under different motor speeds and loads: Revisited. Mechanical Systems and Signal Processing, 70, 201-208.
[7] Muralidharan, V., & Sugumaran, V. (2012). A comparative study of Naïve Bayes classifier and Bayes net classifier for fault diagnosis of monoblock centrifugal pump using wavelet analysis. Applied Soft Computing, 12(8), 2023-2029.
[8] Widodo, A., & Yang, B. S. (2007). Support vector machine in machine condition monitoring and fault diagnosis. Mechanical systems and signal processing, 21(6), 2560-2574.
[9] Haykin, S., & Network, N. (2004). A comprehensive foundation. Neural networks, 2(2004), 41.
[10] Yang, B. S., Han, T., & Hwang, W. W. (2005). Fault diagnosis of rotating machinery based on multi-class support vector machines. Journal of Mechanical Science and Technology, 19(3), 846-859.
[11] https://www.emco-world.com/en/products/industrial-training/machines/milling/cat/26/d/2/p/1000045%2C26/pr/concept-mill-105.html (last accessed: February, 2019)
[12] Kanai, M. (1978). Statistical characteristics of drill wear and drill life for the standardized performance tests. Ann. CIRP, 27(1), 61.
[13] Kumar, A., Ramkumar, J., Verma, N. K., & Dixit, S. (2014, June). Detection and classification for faults in drilling process using vibration analysis. In 2014 International Conference on Prognostics and Health Management (pp. 1-6). IEEE.
[14] https://deeplearning4j.org/docs/latest/deeplearning4j-quickstart (last accessed: February ,2019)
[15] https://deeplearning.cms.waikato.ac.nz/user-guide/getting-started/ (last accessed: February, 2019)
[16] https://deeplearning4j.org/tutorials/10-layers-and-preprocessors (last accessed: February, 2019)
[17] Jing, L., Zhao, M., Li, P., & Xu, X. (2017). A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox. Measurement, 111, 1-10.
[18] Thirukovalluru, R., Dixit, S., Sevakula, R. K., Verma, N. K., & Salour, A. (2016, June). Generating feature sets for fault diagnosis using denoising stacked auto-encoder. In 2016 IEEE International Conference on Prognostics and Health Management (ICPHM) (pp. 1-7). IEEE.
[19] Zarekar, J., Khajavi, M. N., & Payganeh, G. Roller Bearıng Fault Detectıon Usıng Empırıcal Mode Decomposıtıon And Artıfıcıal Neural Network Methods.
[20] Nerella, M. J., Ratnam, C., & Rao, V. V. (2018). Fault Dıagnosıs Of A Rollıng Element Bearıngs Usıng Acoustıc Condıtıon Monıtorıng And Artıfıcıal Neural Network.
[21] Yang, B. S., Han, T., & Hwang, W. W. (2005). Fault diagnosis of rotating machinery based on multi-class support vector machines. Journal of Mechanical Science and Technology, 19(3), 846-859.
[22] Konar, P., & Chattopadhyay, P. (2011). Bearing fault detection of induction motor using wavelet and Support Vector Machines (SVMs). Applied Soft Computing, 11(6), 4203-4211.
[23] Li, B., Chow, M. Y., Tipsuwan, Y., & Hung, J. C. (2000). Neural-network-based motor rolling bearing fault diagnosis. IEEE transactions on industrial electronics, 47(5), 1060-1069.
[24] Janssens, O., Slavkovikj, V., Vervisch, B., Stockman, K., Loccufier, M., Verstockt, S., ... & Van Hoecke, S. (2016). Convolutional neural network based fault detection for rotating machinery. Journal of Sound and Vibration, 377, 331-345.
[25] Sathish, T., & Karthick, S. (2018). HAIWF-based fault detection and classification for industrial machine condition monitoring. Progress in Industrial Ecology, an International Journal, 12(1-2), 46-58.
[26] Li, H., Huang, J., & Ji, S. (2019). Bearing Fault Diagnosis with a Feature Fusion Method Based on an Ensemble Convolutional Neural Network and Deep Neural Network. Sensors, 19(9), 2034.
[27] Saravanan N, Cholairajan S & Ramachandran KI (2009) “Vibration-Based Fault diagnosis of Spur Bevel Gear Box Using Fuzzy Technique”, Expert systems with applications, 36(2): 3119-3135.
[28] Sakthivel NR, Sugumaran V & Babudevasenapati S (2010) “Vibration Based Fault Diagnosis of Monoblock Centrifugal Pump Using Decision Tree”, Expert Systems with Applications, 37(6): 4040-4049.
[29] Pandya DH, Upadhyay S & Harsha SP (2012) “ANN Based Fault Diagnosis of Rolling Element Bearing Using Time-Frequency Domain Feature”, International Journal of Engineering Science and Technology, 4(6): 2878-2886.
[30] Seshadrinath J, Singh B & Panigrahi BK (2013) “Investigation of Vibration Signatures For Multiple Fault Diagnosis in Variable Frequency Drives Using Complex Wavelets”, IEEE Transactions on Power Electronics, 29(2): 936-945.
[31] Jafari SM, Mehdigholi H & Behzad M (2014) “Valve Fault Diagnosis in Internal Combustion Engines Using Acoustic Emission and Artificial Neural Network”, Shock and Vibration, 2014.
[32] Ali JB, Fnaiech N, Saidi L, Chebel-Morello B & Fnaiech F (2015) “Application of Empirical Mode Decomposition and Artificial Neural Network For Automatic Bearing Fault Diagnosis Based on Vibration Signals”, Applied Acoustics, 89: 16-27.
[33] Janssens O, Slavkovikj V, Vervisch B, Stockman K, Loccufier M, Verstockt S, deWalle RV & Van Hoecke S (2016) “Convolutional Neural Network Based Fault Detection For Rotating Machinery”, Journal of Sound and Vibration 377: 331-345.
[34] Liu H, Zhou J, Zheng Y, Ji,  Zhang Y (2018) “Fault Diagnosis of Rolling Bearings with Recurrent Neural Network-Based Autoencoders”, ISA transactions,77: 167-178.
[35] Jing L, Zhao M, Li P & Xu X (2017) “A Convolutional Neural Network Based Feature Learning and Fault Diagnosis Method For the Condition Monitoring of Gearbox”, Measurement, 111: 1-10.
[36] S. Kiranyaz, A. Gastli, L. Ben-Brahim, N. Al-Emadi and M. Gabbouj, "Real-Time Fault Detection and Identification for MMC Using 1-D Convolutional Neural Networks," in IEEE Transactions on Industrial Electronics, vol. 66, no. 11, pp. 8760-8771, Nov. 2019.
[37] Kemal Polat, Kaan Onur Koc (2020). Detection of Skin Diseases from Dermoscopy Image Using the combination of Convolutional Neural Network and One-versus-All. Journal of Artificial Intelligence and Systems, 2, 80–97. ttps://doi.org/10.33969/AIS.2020.21006.
[38] Murat Arican, Kemal Polat (2020). Binary particle swarm optimization (BPSO) based channel selection in the EEG signals and its application to speller systems. Journal of Artificial Intelligence and Systems, 2, 27–37. https://doi.org/10.33969/AIS.2020.21003.
[39] D. Jude Hemanth (2020). EEG signal based Modified Kohonen Neural Networks for Classification of Human Mental Emotions. Journal of Artificial Intelligence and Systems, 2, 1–13. https://doi.org/10.33969/AIS.2020.21001.
[40] Jatin Arora, Utkarsh Agrawal, and Prerna Sharma (2020). Classification of Maize leaf diseases from healthy leaves using Deep Forest. Journal of Artificial Intelligence and Systems, 2, 14–26. https://doi.org/10.33969/AIS.2020.21002.