應用電流特徵時頻分析與深度學習於馬達故障診斷

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：74

、訪客IP：3.133.126.46

姓名

許家豪(Chia-Hao Hsu) 查詢紙本館藏

畢業系所

機械工程學系

論文名稱

應用電流特徵時頻分析與深度學習於馬達故障診斷
(Application of Current Signature Time-Frequency Analysis and Deep Learning for Motor Fault Diagnosis)

相關論文

★ 基於深度學習之人員追蹤辨識系統

★ 整合模糊控制與多感測器之智慧助步車

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 (2026-8-1以後開放)

摘要(中)

本論文提出一種旋轉機械的軸承故障診斷方法，運用電機電流特徵分析(Motor Current Signature Analysis, MCSA)結合時頻分析(Time-Frequency Analysis, TFA)和深度學習(Deep Learning, DL)技術，目的是提升診斷的準確性和效率，同時降低檢測成本。由於實際故障數據取得不易，我們使用開源資料庫中的數據集進行分析，該資料集以25.6 kHz的取樣頻率收集溫度和電流數據，包含內環故障、外環故障、正常狀態、轉軸錯位和轉子不平衡五種狀態。在數據預處理階段，我們使用低通濾波器濾除高頻雜訊，並應用短時傅立葉轉換(Short-Time Fourier Transform, STFT)、連續小波轉換(Continuous Wavelet Transform, CWT)和希爾伯特-黃轉換(Hilbert-Huang Transform, HHT)等時頻分析方法，提取與故障相關的低頻特徵頻率，這些時頻分析方法提供更全面的信號時域和頻域信息，使故障特徵的識別更加準確。在模型訓練方面，我們採用英國牛津大學視覺幾何研究群(Visual Geometry Group, VGG)提出的VGG-16及VGG-19模型架構，還有由微軟提出的殘差神經網絡(Residual Neural Network, ResNet)架構ResNet-50，以及自定義的Modified VGG-16進行分類。通過混淆矩陣和多種評估指標對模型進行全面評估，如準確度、F1-Score以及自定義評估指標，該指標採用權重加權的方式，對模型的分類錯誤進行加權，更準確地反映不同類型錯誤的嚴重程度，目的是更全面地反映模型在實際應用中的性能和可靠性。研究結果表明，我們的優化模型在保持高準確度的同時，大幅減少模型參數量和大小，並且在某些情況下達到更好的分類效果，這使得我們的模型在資源受限的硬體環境中更具適應性。

摘要(英)

This paper proposes a method for diagnosing bearing faults in rotating machine using motor current signature analysis (MCSA) combined with time-frequency analysis (TFA) and deep learning (DL) techniques. The purpose is to improve diagnostic accuracy and efficiency while reducing detection costs. Due to the difficulty in obtaining actual fault data, we utilize a dataset from an open-source repository, which collects temperature and current data at a sampling frequency of 25.6 kHz, including five states: inner race fault, outer race fault, normal, shaft misalignment, and rotor Imbalance. In the data preprocessing stage, we employ a low-pass filter to remove high-frequency noise and apply short-time Fourier transform (STFT), continuous wavelet transform (CWT), and Hilbert-Huang transform (HHT) for time-frequency analysis. These methods extract low-frequency characteristic frequencies related to faults, providing comprehensive information in both time and frequency domains, enhancing fault feature identification accuracy. For model training, we adopt VGG-16 and VGG-19 architectures proposed by the Visual Geometry Group (VGG) at the University of Oxford, as well as ResNet-50 architecture proposed by Microsoft, and a Customized Modified VGG-16 for classification. Through confusion matrix and various evaluation metrics such as accuracy, F1-Score, and a Customized evaluation metric weighted to reflect the severity of different types of classification errors, we comprehensively evaluate the models, aiming to accurately reflect the performance and reliability of the models in real-world applications. The results demonstrate that our optimized model maintains high accuracy while significantly reducing model parameters and size, and in some cases, achieves better classification performance, making our model more adaptable to resource-constrained hardware environments.

關鍵字(中)

★ 時頻分析
★ 深度學習
★ 電機電流特徵分析
★ 故障診斷

關鍵字(英)

★ Time-Frequency Analysis
★ Deep Learning
★ Motor Current Signature Analysis
★ Fault Diagnosis

論文目次

摘要 i
Abstract ii
誌謝 iii
目錄 iv
圖目錄 vi
表目錄 viii
第一章緒論 1
1.1研究背景 1
1.2研究動機與目標 1
1.3文獻回顧 2
1.4論文架構 5
第二章系統架構與軟硬體介紹 6
2.1系統架構 6
2.2硬體介紹 7
2.3軟體介紹 8
2.3.1 TensorFlow 8
2.3.2 SciPy 8
2.3.3 PyWavelets 8
2.3.4 PyEMD 8
2.4數據集 9
第三章時頻分析及特徵頻率提取 13
3.1短時傅立葉轉換 13
3.1.1快速傅立葉轉換 14
3.1.2加伯轉換 15
3.2小波轉換 17
3.3希爾伯特-黃轉換 19
3.3.1本質模態函數 19
3.3.2經驗模態分解 21
3.4特徵頻率提取 23
3.5濾波器 24
第四章深度學習模型架構與評估指標 26
4.1深度學習 26
4.1.1 VGGNet 27
4.1.2 ResNet 29
4.1.3自定義模型Modified VGG-16 30
4.2模型評估指標 32
4.2.1混淆矩陣 33
4.2.2 Accuracy 34
4.2.3 F1-Score 34
4.2.4自定義模型評估指標Customized Score 34
第五章實驗結果 37
5.1時頻分析結果 37
5.1.1短時傅立葉轉換結果 38
5.1.2連續小波轉換結果 41
5.1.3希爾伯特-黃轉換結果 43
5.2深度學習故障分類結果 46
5.3模型評估結果 60
第六章結論與未來展望 61
6.1結論 61
6.2未來展望 61
參考文獻 63

參考文獻

[1] N. Bhole and S. Ghodke, “Motor Current Signature Analysis for Fault Detection of Induction Machine–A Review,” 2021 4th Biennial International Conference on Nascent Technologies in Engineering (ICNTE), Navi Mumbai, India, 2021, pp. 1–6, doi: 10.1109/ICNTE51185.2021.9487715.
[2] R. B. Randall and J. Antoni, “Rolling Element Bearing Diagnostics—A Tutorial,” Mechanical Systems and Signal Processing, vol. 25, no. 2, pp. 485–520, Feb. 2011, doi: 10.1016/j.ymssp.2010.07.017.
[3] C. P. Mbo’o, T. Herold, and K. Hameyer, “Impact of the Load in the Detection of Bearing Faults by Using the Stator Current in PMSM′s,” 2014 International Conference on Electrical Machines (ICEM), Berlin, Germany, 2014, pp. 1621–1627, doi: 10.1109/ICELMACH.2014.6960399.
[4] G. Niu, X. Dong, and Y. Chen, “Motor Fault Diagnostics Based on Current Signatures: A Review,” IEEE Transactions on Instrumentation and Measurement, vol. 72, pp. 1–19, 2023, doi: 10.1109/TIM.2023.3285999.
[5] I. Atoui, H. Meradi, R. Boulkroune, R. Saidi, and A. Grid, “Fault Detection and Diagnosis in Rotating Machinery by Vibration Monitoring Using FFT and Wavelet Techniques,” 2013 8th International Workshop on Systems, Signal Processing and their Applications (WoSSPA), Algiers, Algeria, 2013, pp. 401–406, doi: 10.1109/ WoSSPA.2013.6602399.
[6] L. Ciabattoni, F. Ferracuti, A. Freddi, and A. Monteriù, “Statistical Spectral Analysis for Fault Diagnosis of Rotating Machines,” IEEE Transactions on Industrial Electronics, vol. 65, no. 5, pp. 4301–4310, May 2018, doi: 10.1109/TIE. 2017.2762623.
[7] N. Sikder, K. Bhakta, A. A. Nahid, and M. M. M. Islam, “Fault Diagnosis of Motor Bearing Using Ensemble Learning Algorithm with FFT-based Preprocessing,” 2019 International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST), Jan. 2019, doi: 10.1109/icrest.2019.8644089.

[8] A. Prudhom, J. Antonino-Daviu, H. Razik, and V. Climente-Alarcon, “Time-Frequency Vibration Analysis for the Detection of Motor Damages Caused by Bearing Currents,” Mechanical Systems and Signal Processing, vol. 84, pp. 747–762, Feb. 2017, doi: 10.1016/j.ymssp.2015.12.008.
[9] G. Yu, “A Concentrated Time–Frequency Analysis Tool for Bearing Fault Diagnosis,” IEEE Transactions on Instrumentation and Measurement, vol. 69, no. 2, pp. 371–381, Feb. 2020, doi: 10.1109/tim.2019.2901514.
[10] Ahmadpour, A. Dejamkhooy, and H. Shayeghi, “Fault Diagnosis of HTS–SLIM Based on 3D Finite Element Method and Hilbert–Huang Transform,” IEEE Access, vol. 10, pp. 35736–35749, Jan. 2022, doi: 10.1109/access.2022.3159693.
[11] I.-H. Kao, W.-J. Wang, Y.-H. Lai, and J.-W. Perng, “Analysis of Permanent Magnet Synchronous Motor Fault Diagnosis Based on Learning,” IEEE Transactions on Instrumentation and Measurement, vol. 68, no. 2, pp. 310–324, Feb. 2019, doi: 10.1109/tim.2018.2847800.
[12] M. Singh and A. G. Shaik, “Incipient Fault Detection in Stator Windings of an Induction Motor Using Stockwell Transform and SVM,” IEEE Transactions on Instrumentation and Measurement, vol. 69, no. 12, pp. 9496–9504, Dec. 2020, doi: 10.1109/tim.2020.3002444.
[13] K.-J. Shih, M.-F. Hsieh, B.-J. Chen, and S.-F. Huang, “Machine Learning for Inter-Turn Short-Circuit Fault Diagnosis in Permanent Magnet Synchronous Motors,” IEEE Transactions on Magnetics, vol. 58, no. 8, pp. 1–7, Aug. 2022, doi: 10.1109/ tmag.2022.3169173.
[14] M. Skowron, T. Orlowska-Kowalska, and C. T. Kowalski, “Detection of Permanent Magnet Damage of PMSM Drive Based on Direct Analysis of the Stator Phase Currents Using Convolutional Neural Network,” IEEE Transactions on Industrial Electronics, vol. 69, no. 12, pp. 13665–13675, Dec. 2022, doi: 10.1109/tie.2022. 3146557.
[15] K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” 3rd International Conference on Learning Representations (ICLR), 2015.

[16] K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770–778.
[17] W. Jung, S.-H. Kim, S.-H. Yun, J. Bae, and Y.-H. Park, “Vibration, Acoustic, Temperature, and Motor Current Dataset of Rotating Machine Under Varying Operating Conditions for Fault Diagnosis,” Data in Brief, vol. 48, p. 109049, Jun. 2023, doi: 10.1016/j.dib.2023.109049.
[18] J. Allen, “Short Term Spectral Analysis, Synthesis, and Modification by Discrete Fourier Transform,” IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 25, no. 3, pp. 235–238, Jun. 1977, doi: 10.1109/TASSP.1977. 1162950.
[19] J. W. Cooley and J. W. Tukey, “An Algorithm for the Machine Calculation of Complex Fourier Series,” Mathematics of Computation, vol. 19, no. 90, p. 297, Apr. 1965, doi: 10.2307/2003354.
[20] D. Gabor, “Theory of Communication. Part 3: Frequency Compression and Expansion,” Journal of the Institution of Electrical Engineers - Part III: Radio and Communication Engineering, vol. 93, no. 26, pp. 445–457, Nov. 1946, doi: 10.1049/ji-3-2.1946.0076.
[21] ‌A. Grossmann and J. Morlet, “Decomposition of Hardy Functions into Square Integrable Wavelets of Constant Shape,” SIAM Journal on Mathematical Analysis, vol. 15, no. 4, pp. 723–736, Jul. 1984, doi: 10.1137/0515056.
[22] N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N.-C. Yen, C. C. Tung, and H. H. Liu, “The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Non-stationary Time Series Analysis,” Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, vol. 454, no. 1971, pp. 903–995, Mar. 1998, doi: 10.1098/rspa.1998.0193.
[23] J. Hang, J. Zhang, M. Cheng, and Z. Wang, “Fault Diagnosis of Mechanical Imbalance for Permanent Magnet Synchronous Motor Drive System Under Nonstationary Condition,” 2013 IEEE Energy Conversion Congress and Exposition, Denver, CO, USA, 2013, pp. 3556–3562, doi: 10.1109/ECCE.2013.6647169.

[24] J. Lee, Y. Lee, and N. Kim, “Detection and Analysis of Shaft Misalignment in Application of Production and Logistics Systems Using Motor Current Signature Analysis,” Expert Systems with Applications, vol. 217, p. 119463, May 2023, doi: 10.1016/j.eswa.2022.119463.
[25] M.-Q. Tran, M.-K. Liu, Q.-V. Tran, and T.-K. Nguyen, “Effective Fault Diagnosis Based on Wavelet and Convolutional Attention Neural Network for Induction Motors,” IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1–13, Jan. 2022, doi: 10.1109/tim.2021.3139706.

指導教授

陳翔傑(Hsiang-Chieh Chen)

審核日期

2024-7-27

推文