A Deep Learning Approach for Motor Fault Detection using Mobile Accelerometer Data

Öz

Electrical machines, which provide many conveniences in our daily life, may experience malfunctions that may adversely affect their performance and the general functioning of the industrial processes in which they are used. These failures often require maintenance or repair work, which can be expensive and time consuming. Therefore, minimizing the risk of malfunctions and failures and ensuring that these machines operate reliably and efficiently play a critical role for the industry. In this study, a one-dimensional convolutional neural network (1D-CNN) based fault diagnosis model is proposed for electric motor fault detection. Motor vibration data was chosen as the input data of the 1D-CNN model. Motor vibration data was obtained from a mobile application developed by using the three-axis accelerometer of the mobile phone. Three-axis data (X-axis, Y-axis and Z-axis) were fed to the model, both separately and together, to perform motor fault detection. The results showed that even a single axis data provides error-free diagnostics. With this fault detection method, which does not require any connection on or inside the motor, the fault condition in an electric motor has been detected with high accuracy.

Anahtar Kelimeler

• Motor fault diagnosis
• deep learning
• 1D-CNN

Kaynakça

1. [1] D. Neupane and J. Seok, "Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review," in IEEE Access, vol. 8, pp. 93155-93178, 2020, doi: 10.1109/ACCESS.2020.2990528.
2. [2] S. Zhang, S. Zhang, B. Wang and T. G. Habetler, "Deep Learning Algorithms for Bearing Fault Diagnostics—A Comprehensive Review," in IEEE Access, vol. 8, pp. 29857-29881, 2020, doi: 10.1109/ACCESS.2020.2972859.
3. [3] M. Talo, U. B. Baloglu, O Yildirim, and U. R. Acharya, “Application of deep transfer learning for automated brain abnormality classification using MR images,” in Cognitive Systems Research, vol. 54, pp. 176-188, 2019.
4. [4] U. B. Baloglu, M. Talo, O. Yildirim, R. S. Tan, and U. R. Acharya, “Classification of myocardial infarction with multi-lead ECG signals and deep CNN,” in Pattern recognition letters, vol. 122, pp. 23-30, 2019.
5. [5] O. Yildirim, P. Pławiak, R. S. Tan, and U. R. Acharya, “Arrhythmia detection using deep convolutional neural network with long duration ECG signals.,” in Computers in biology and medicine, vol. 102, pp. 411-420, 2018.
6. [6] M. Coşkun, A. Uçar, O. Yildirim and Y. Demir, "Face recognition based on convolutional neural network," 2017 International Conference on Modern Electrical and Energy Systems (MEES), Kremenchuk, Ukraine, 2017, pp. 376-379, doi: 10.1109/MEES.2017.8248937.
7. [7] G. Hinton et al., "Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups," in IEEE Signal Processing Magazine, vol. 29, no. 6, pp. 82-97, Nov. 2012, doi: 10.1109/MSP.2012.2205597.
8. [8] T. Ince, S. Kiranyaz, L. Eren, M. Askar and M. Gabbouj, "Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks," in IEEE Transactions on Industrial Electronics, vol. 63, no. 11, pp. 7067-7075, Nov. 2016, doi: 10.1109/TIE.2016.2582729.

9. [9] Y. Li, Y. Wang, Y. Zhang, and J. Zhang, “Diagnosis of inter-turn short circuit of permanent magnet synchronous motor based on deep learning and small fault samples,.” in Neurocomputing, vol. 442, pp. 348-358, 2021.
10. [10] R. N. Toma, A. E. Prosvirin, and J. M. Kim, “Bearing fault diagnosis of induction motors using a genetic algorithm and machine learning classifiers,” in Sensors, vol. 20(7), 1884, 2020.
11. [11] F. Jia, Y. Lei, N. Lu, and S. Xing, “Deep normalized convolutional neural network for imbalanced fault classification of machinery and its understanding via visualization,” in Mechanical Systems and Signal Processing, vol. 110, pp. 349-367, 2018.
12. [12] S. Asutkar, C. Chalke, K. Shivgan, and S. Tallur, “TinyML-enabled edge implementation of transfer learning framework for domain generalization in machine fault diagnosis,” in Expert Systems with Applications, vol. 213, 119016, 2023.
13. [13] M. Ertargin , O. Yildirim and A. Orhan , "Motor Yataklarında Meydana Gelen Arızaları Tespit Etmek için Yeni Bir Tek Boyutlu Konvolüsyonel Sinir Ağı Modeli", Fırat Üniversitesi Mühendislik Bilimleri Dergisi, vol. 35, no. 2, pp. 669-678, Sep. 2023, doi:10.35234/fumbd.1292390.
14. [14] S. Shen, et al., “A physics-informed deep learning approach for bearing fault detection,” in Engineering Applications of Artificial Intelligence, vol. 103, 104295, 2021.
15. [15] K. Xu, X. Kong, Q. Wang, S. Yang, N. Huang, and J. Wang, “A bearing fault diagnosis method without fault data in new working condition combined dynamic model with deep learning,” in Advanced Engineering Informatics, vol. 54, 101795, 2022.
16. [16] D. K. Soother, S. M. Ujjan, K. Dev, S. A. Khowaja, N. A. Bhatti, and T. Hussain, “Towards soft real-time fault diagnosis for edge devices in industrial IoT using deep domain adaptation training strategy,” in Journal of Parallel and Distributed Computing, vol. 160, pp. 90-99, 2022.