臺灣博碩士論文加值系統

雲端和伺服器服務與人們生活已密不可分，儲存裝置發生故障可能會造成系統停機、資料遺失或是高花費修復。雖然S.M.A.R.T.是一種常見的硬碟自我檢測技術，但是多數故障硬碟，無法在S.M.A.R.T.發現異常。若能夠增加一種S.M.A.R.T.以外的硬碟檢測方法，盡早預測硬碟故障，能夠提升系統穩定性與可靠度。因此，本研究提出一種硬碟預測式維護系統，透過感測器蒐集硬碟數據，使用快速傅立葉轉換與線性預測倒頻譜係數擷取特徵，以此特徵訓練自編碼器與遞迴神經網路進行異常偵測。實驗結果得知，傳統使用ReLU的自編碼器準確率76.42%，改用GELU能夠學習更複雜特徵的自編碼器準確率77.33%，結合遞迴神經網路，能夠學習資料時間關係的RNN自編碼器準確率84.75%，可以學習資料長期關係的LSTM自編碼器準確率85.08 %，輕量化LSTM的GRU自編碼器準確率87.08%。本研究以低成本與體積小的微控制器與感測器，不需要大幅度變動既有設備，即可佈署硬碟預測式維護系統，提供一種硬碟異常偵測方法。

Cloud and server services have become inseparable from people's lives. Failure of storage devices may cause system downtime, data loss or costly repairs. Although S.M.A.R.T. is a common hard disk drive self-testing technology, most faulty hard disk drive cannot detect failure through S.M.A.R.T.. If a hard disk drive detection method other than S.M.A.R.T. can be added to predict hard disk drive failures as early as possible, system stability and reliability can be improved. Therefore, this thesis proposes a hard disk drive predictive maintenance system that collects hard disk drive data through sensors and use FFT and LPCC to extract features, utilizes these features to train recurrent neural networks and autoencoder for anomaly detection. Experimental results show that the accuracy of traditional ReLU-Activated autoencoder is 76.42%. Switch ReLU to GELU, which can learn more complex features, the accuracy of GELU-Activated autoencoder is 77.33%. Import the time series neural network to help the model learn the time relationships and features in the data. The accuracy of RNN-Based autoencoder is 84.75%. The accuracy of LSTM-Based autoencoder that can help learn the long-term relationship of the data is 85.08%. GRU-Based autoencoder is not only a lightweight version of LSTM-autoencoder but also achieves an accuracy to 87.08%. This thesis employs low-cost and small-size microcontrollers and sensors to implement predictive maintenance for hard disk drive, which can provide a hard disk drive anomaly detection method. It is suitable existing systems without significantly changing the equipment.

摘要 I
Abstract II
誌謝 III
目錄 IV
圖目錄 VII
表目錄 IX
第一章、緒論 1
1.1 研究背景 1
1.2 研究目的 3
1.3 論文架構 3
第二章、文獻回顧 4
2.1 傅立葉分析 4
2.1.1 傅立葉級數與傅立葉轉換 5
2.1.2 離散傅立葉轉換 6
2.2 快速傅立葉轉換 7
2.2.1 蝶形網路 7
2.2.2 庫利－圖基快速傅立葉轉換演算法 8
2.3 線性預測編碼 10
2.3.1 線性預測倒頻譜係數 11
2.4 自編碼器 12
2.4.1 激勵函數 14
2.5 時間序列神經網路模型 16
2.5.1 遞迴神經網路 17
2.5.2 長短期記憶 18
2.5.3 門控循環單元 21
2.6 標準差 23
第三章、系統設計 25
3.1 MIAT系統設計方法論 25
3.1.1 IDEF0階層式與模組化設計 26
3.1.2 GRAFCET離散事件建模 28
3.2 IDEF0硬碟預測式維護系統 31
3.2.1 IDEF0感測器數據蒐集模組 32
3.2.2 IDEF0異常偵測模組 33
3.3 GRAFCET硬碟預測式維護系統 34
3.3.1 GRAFCET感測器數據蒐集模組 35
3.3.2 GRAFCET異常偵測模組 37
3.4 硬碟預測式維護系統整合驗證 38
3.4.1 感測器數據蒐集整合驗證 39
3.4.2 異常偵測整合驗證 40
第四章、實驗與結果分析 41
4.1 嵌入式系統實驗平台 41
4.1.1 微控制器開發板 41
4.1.2 聲音感測器 42
4.1.3 九軸慣性量測單元 43
4.1.4 軟體工具 45
4.1.5 硬碟樣本 46
4.2 感測器數據讀取 47
4.3 快速傅立葉轉換實驗 49
4.4 數據儲存 50
4.5 深度學習實驗平台 51
4.5.1 深度學習框架 52
4.5.2 模型評估指標 53
4.6 線性預測倒頻譜係數實驗 55
4.7 訓練模型 58
4.8 數據分類 60
4.9 結果與討論 62
第五章、結論與未來展望 66
5.1 結論 66
5.2 未來展望 67
參考文獻 68

[1] R. Pinciroli, L. Yang, J. Alter, and E. Smirni, “Lifespan and Failures of SSDs and HDDs: Similarities, Differences, and Prediction Models,” IEEE Trans Dependable Secure Comput, vol. 20, no. 1, pp. 256–272, Jan. 2023.
[2] R. Chianese, L. Cicala, C. V. Angelino, F. Gargiulo, and D. Matarazzo, “A Risk and Priority Model for Cost-Benefit Analysis and Work Scheduling within Predictive Maintenance Scenarios,” in IEEE International Conference on Emerging Technologies and Factory Automation, ETFA, Institute of Electrical and Electronics Engineers Inc., 2021.
[3] E. Pinheiro, W.-D. Weber, and L. A. Barroso, “Failure Trends in a Large Disk Drive Population,” in Proceeding of 5th USENIX Conference on File and Storage Technologies, pp. 17–23, 2007.
[4] G. Wang, L. Zhang, and W. Xu, “What Can We Learn from Four Years of Data Center Hardware Failures?,” in Proceedings - 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks, DSN 2017, Institute of Electrical and Electronics Engineers Inc., pp. 25–36, Aug. 2017.
[5] K. Wang, G. Dai, and L. Guo, “Intelligent Predictive Maintenance (IPdM) for Elevator Service- Through CPS, IOT&S and Data Mining,” in Proceedings of the 6th International Workshop of Advanced Manufacturing and Automation, Atlantis Press, pp. 1–6, Nov. 2016.
[6] Z. Li, K. Wang, and Y. He, “Industry 4.0 - Potentials for Predictive Maintenance,” in Proceedings of the 6th International Workshop of Advanced Manufacturing and Automation, Atlantis Press, pp. 42–46, Nov. 2016.
[7] I. Kaitovic and M. Malek, “Impact of Failure Prediction on Availability: Modeling and Comparative Analysis of Predictive and Reactive Methods,” IEEE Trans Dependable Secure Comput, vol. 17, no. 3, pp. 493–505, May 2020.
[8] A. Bousdekis, D. Apostolou, and G. Mentzas, “Predictive Maintenance in the 4th Industrial Revolution: Benefits, Business Opportunities, and Managerial Implications,” IEEE Engineering Management Review, vol. 48, no. 1, pp. 57–62, Jan. 2020.
[9] H. Toumi, A. Meddaoui, and M. Hain, “The influence of predictive maintenance in industry 4.0: A systematic literature review,” in 2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology, IRASET 2022, Institute of Electrical and Electronics Engineers Inc., 2022.
[10] M. H. Abidi, M. K. Mohammed, and H. Alkhalefah, “Predictive Maintenance Planning for Industry 4.0 Using Machine Learning for Sustainable Manufacturing,” Sustainability (Switzerland), vol. 14, no. 6, Mar. 2022.
[11] S. Jakovlev and M. Voznak, “Auto-Encoder-Enabled Anomaly Detection in Acceleration Data: Use Case Study in Container Handling Operations,” Machines, vol. 10, no. 9, Sep. 2022.
[12] D. Gong et al., “Memorizing Normality to Detect Anomaly: Memory-Augmented Deep Autoencoder for Unsupervised Anomaly Detection,” in 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 1705–1714, 2019.
[13] Z. Chen, C. K. Yeo, B. S. Lee, and C. T. Lau, “Autoencoder-based network anomaly detection,” in 2018 Wireless Telecommunications Symposium (WTS), pp. 1–5, 2018.
[14] R. C. Aygun and A. G. Yavuz, “Network Anomaly Detection with Stochastically Improved Autoencoder Based Models,” in Proceedings - 4th IEEE International Conference on Cyber Security and Cloud Computing, CSCloud 2017 and 3rd IEEE International Conference of Scalable and Smart Cloud, SSC 2017, Institute of Electrical and Electronics Engineers Inc., pp. 193–198, Jul. 2017.
[15] N. Shvetsova, B. Bakker, I. Fedulova, H. Schulz, and D. V Dylov, “Anomaly Detection in Medical Imaging With Deep Perceptual Autoencoders,” IEEE Access, vol. 9, pp. 118571–118583, 2021.
[16] O. I. Provotar, Y. M. Linder, and M. M. Veres, “Unsupervised Anomaly Detection in Time Series Using LSTM-Based Autoencoders,” in 2019 IEEE International Conference on Advanced Trends in Information Theory (ATIT), pp. 513–517, 2019.
[17] V. K. Kukkala, S. V Thiruloga, and S. Pasricha, “INDRA: Intrusion Detection Using Recurrent Autoencoders in Automotive Embedded Systems,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 39, no. 11, pp. 3698–3710, 2020.
[18] Y. Ma, A. Maqsood, K. Corzine, and D. Oslebo, “Long Short-Term Memory Autoencoder Neural Networks Based DC Pulsed Load Monitoring Using Short-Time Fourier Transform Feature Extraction,” in 2020 IEEE 29th International Symposium on Industrial Electronics (ISIE), pp. 912–917, 2020.
[19] M. Said Elsayed, N. A. Le-Khac, S. Dev, and A. D. Jurcut, “Network Anomaly Detection Using LSTM Based Autoencoder,” in Q2SWinet 2020 - Proceedings of the 16th ACM Symposium on QoS and Security for Wireless and Mobile Networks, Association for Computing Machinery, Inc, pp. 37–45, Nov. 2020.
[20] J. S. L. Senanayaka, H. Van Khang, and K. G. Robbersmyr, “Autoencoders and Recurrent Neural Networks Based Algorithm for Prognosis of Bearing Life,” in 2018 21st International Conference on Electrical Machines and Systems (ICEMS), pp. 537–542, 2018.
[21] L. Zhu, S. Shen, H. Li, G. Zhang, and S. Wu, “Simulation of aerodynamic noise and vibration noise in hard disk drives,” in 2016 Asia-Pacific Magnetic Recording Conference Digest (APMRC), pp. 1–2, 2016.
[22] K. A. Loparo, M. L. Adams, W. Lin, M. F. Abdel-Magied, and N. Afshari, “Fault detection and diagnosis of rotating machinery,” IEEE Transactions on Industrial Electronics, vol. 47, no. 5, pp. 1005–1014, 2000.
[23] D. Mu and L. Sheng, “Intelligent Fault Diagnosis Method for Coupling Rotating Machinery Based on Deep Convolutional Neural Network,” in 2019 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), pp. 64–69, 2019.
[24] J. W. Cooley and J. W. Tukey, “An Algorithm for the Machine Calculation of Complex Fourier Series,” Math Comput, vol. 19, no. 90, pp. 297–301, 1965.
[25] E. O. Brigham and R. E. Morrow, “The fast Fourier transform,” IEEE Spectr, vol. 4, no. 12, pp. 63–70, 1967.
[26] W. T. Cochran et al., “What is the fast Fourier transform?,” Proceedings of the IEEE, vol. 55, no. 10, pp. 1664–1674, 1967.
[27] S. Davis and P. Mermelstein, “Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences,” IEEE Trans Acoust, vol. 28, no. 4, pp. 357–366, 1980.
[28] H. Gupta and D. Gupta, “LPC and LPCC method of feature extraction in Speech Recognition System,” in 2016 6th International Conference - Cloud System and Big Data Engineering (Confluence), pp. 498–502, 2016.
[29] U. Bhattacharjee, “A Comparative Study Of LPCC And MFCC Features For The Recognition Of Assamese Phonemes,” International Journal of Engineering and Technical Research, vol. 2, Jan. 2013.
[30] D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors,” Nature, vol. 323, no. 6088, pp. 533–536, 1986.
[31] S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Comput, vol. 9, no. 8, pp. 1735–1780, 1997.
[32] J. Chung, C. Gulcehre, K. Cho, and Y. Bengio, “Empirical evaluation of gated recurrent neural networks on sequence modeling,” NIPS 2014 Workshop on Deep Learning, December 2014. 2014.
[33] O. Koren, M. Koren, and O. Peretz, “A procedure for anomaly detection and analysis,” Eng Appl Artif Intell, vol. 117, p. 105503, 2023.
[34] R. Saborido, J. Ferrer, F. Chicano, and E. Alba, “Automatizing Software Cognitive Complexity Reduction,” IEEE Access, vol. 10, pp. 11642–11656, 2022.
[35] L. S. Nair and J. Swaminathan, “Towards Reduction of Software Maintenance Cost through Assignment of Critical Functionality Scores,” in 2020 5th International Conference on Communication and Electronics Systems (ICCES), pp. 199–204, 2020.
[36] S. M. H. Dehaghani and N. Hajrahimi, “Which factors affect software projects maintenance cost more?,” Acta Informatica Medica, vol. 21, no. 1, pp. 63–66, 2013.
[37] N. Limam and R. Boutaba, “Assessing Software Service Quality and Trustworthiness at Selection Time,” IEEE Transactions on Software Engineering, vol. 36, no. 4, pp. 559–574, 2010.
[38] B. Hunt, B. Turner, and K. McRitchie, “Software Maintenance Implications on Cost and Schedule,” in 2008 IEEE Aerospace Conference, pp. 1–6, 2008.
[39] C.-H. Chen, M.-Y. Lin, and X.-C. Guo, “High-level modeling and synthesis of smart sensor networks for Industrial Internet of Things,” Comput. Electr. Eng., vol. 61, pp. 48–66, 2017.