V型皮帶通常應用於工業驅動設備中，在所有傳動皮帶類型中最為廣泛使用。目前透過定期檢查與維護去發現V型皮帶上的故障與失效問題，因此早期發現故障成為一個重要的議題，目的在於避免停機以及降低維護成本。本篇論文研究了沖床中馬達驅動V型皮帶的失效問題。透過計算原始資料的基本統計量，對皮帶不同張力下的特性進行有效的分類與識別。利用特徵選擇方法找出馬達參數與此皮帶故障預測問題的關聯性，取得其中影響性較大的特徵並使用Isolation Forest (iForest)訓練預測模型。除此之外，也設計並實作出一個配置人工智慧的控制器，此控制器在可程式化邏輯控制器 (PLC)運行訓練好的預測模型去做V型皮帶故障的即時預測。在控制器的設計上需要考慮到PLC有限的運算效能以及模型本身的預測效能。實驗結果顯示，本篇論文使用的方法相比於其它異常檢測演算法具有更少的計算複雜度，使得其能夠在不佔用過多資源且不影響PLC原有的控制程序下進行異常檢測的功能。

V-belt is the most widely used of all transmission belt types and is usually applied in industrial drive equipment. Regular inspection and maintenance are generally used to discover the faults in the V-belt, and early detection of faults becomes necessary to avoid downtime and reduce maintenance costs. This thesis investigated the prognosis of a motor-driven V-belt failure problem in a press machine. Calculating basic statistics of raw data is used to classify and identify the characteristics under different tension of the belt. The features that have a greater impact on the diagnosis of V-belt faults are selected, and Isolation Forest (iForest) is used to train a predictive model. We also design and implement an AI-equipped controller which runs a trained model on the programmable logic controller (PLC) to perform a real-time prediction of V-belt failure. In the design of the AI-equipped PLC, we consider the trade-off between the execution time of the PLC and the performance of the anomaly detection. The experimental results show that our proposed method has less computational complexity than other anomaly detection algorithms, which allowing it to run more efficiently on the PLC. Furthermore, the AI-equipped PLC can perform anomaly detection functions without affecting the original control process and occupying excessive resources.

[1] W. J. Lee, H. Wu, H. Yun, H. Kim, M. B. Jun, and J. W. Sutherland, “Predictive maintenance of machine tool systems using artificial intelligence techniques applied to machine
condition data,” Procedia Cirp, vol. 80, pp. 506–511, 2019.
[2] K. A. Kaiser and N. Z. Gebraeel, “Predictive maintenance management using sensor-based
degradation models,” IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, vol. 39, no. 4, pp. 840–849, 2009.
[3] S. D. Coleman, Chris and E. Deuel, “Predictive maintenance and the smart factory,” tech.
rep., Deloitte, 2017.
[4] M. Riazi, O. Zaiane, T. Takeuchi, A. Maltais, J. Günther, and M. Lipsett, “Detecting the
onset of machine failure using anomaly detection methods,” in International Conference
on Big Data Analytics and Knowledge Discovery, pp. 3–12, Springer, 2019.
[5] F.-Z. Qing, “Intelligent anomaly detection and real time monitoring of press machine,”
Master’s thesis, National Taiwan Normal University, 2019.
[6] R. Brown, “The development of an electronic system to continually monitor, indicate and
control,’belt slippage’in industrial friction’v’belt drive transmission systems,” in Journal
of Physics: Conference Series, vol. 364, p. 012055, IOP Publishing, 2012.
[7] A. Picot, E. Fournier, J. Régnier, M. TientcheuYamdeu, J.-M. Andréjak, and P. Maussion,
“Statistic-based method to monitor belt transmission looseness through motor phase currents,” IEEE Transactions on Industrial Informatics, vol. 13, no. 3, pp. 1332–1340, 2017.
[8] T.-J. Kang, C. Yang, Y. Park, D. Hyun, S. B. Lee, and M. Teska, “Electrical monitoring of
mechanical defects in induction motor-driven v-belt–pulley speed reduction couplings,”
IEEE Transactions on Industry Applications, vol. 54, no. 3, pp. 2255–2264, 2018.
[9] M. K. Das and K. Rangarajan, “Performance monitoring and failure prediction of industrial equipments using artificial intelligence and machine learning methods: A survey,” in
2020 Fourth International Conference on Computing Methodologies and Communication
(ICCMC), pp. 595–602, IEEE, 2020.
[10] W. Zhang, D. Yang, and H. Wang, “Data-driven methods for predictive maintenance of
industrial equipment: A survey,” IEEE Systems Journal, vol. 13, no. 3, pp. 2213–2227,
2019.
[11] A. Bonci, S. Longhi, G. Nabissi, and F. Verdini, “Predictive maintenance system using
motor current signal analysis for industrial robot,” in 2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), pp. 1453–1456, IEEE,
2019.
[12] H.-Y. Lee, J.-l. Lee, S.-O. Kwon, and S.-W. Lee, “Performance estimation of induction
motor using artificial neural network,” in 2018 25th International Conference on Systems,
Signals and Image Processing (IWSSIP), pp. 1–3, IEEE, 2018.
[13] K. Peta and J. Żurek, “Prediction of air leakage in heat exchangers for automotive applications using artificial neural networks,” in 2018 9th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), pp. 721–725, IEEE,
2018.
[14] S. E. Pandarakone, Y. Mizuno, and H. Nakamura, “Evaluating the progression and orientation of scratches on outer-raceway bearing using a pattern recognition method,” IEEE
Transactions on Industrial Electronics, vol. 66, no. 2, pp. 1307–1314, 2018.
[15] M. T. Tsuruta Kosuke and H. Yuki, “Development of ai technology for machine automation controller: Anomaly detection method for manufacturing equipment by utilizing machine data,” tech. rep., OMRON, 2019.
[16] S. N. Abe Yasuaki, Ueyama Yuhki and F. Takashi, “Development of ai technology for
machine automation controller: The insight gained through implementation of anomaly
detection ais to the machine controller,” tech. rep., OMRON, 2019.
[17] M. Kota and K. Shinsuke, “Development of ai technology for machine automation controller: Generation of anomaly detection models at manufacturing site,” tech. rep., OMRON, 2020.
[18] M. Takamasa and D. Jintaro, “Development of ai-equipped machine automation controller: Implementation of ai system usable by maintenance personnel and installation
of ai function in machine automation controller,” tech. rep., OMRON, 2020.
[19] O. Masanori and N. Yoshihide, “Development of ai-equipped machine automation controller: Data collection synchronized with machine control and realization of time-series
database,” tech. rep., OMRON, 2020.
[20] I. Amihai, R. Gitzel, A. M. Kotriwala, D. Pareschi, S. Subbiah, and G. Sosale, “An industrial case study using vibration data and machine learning to predict asset health,” in 2018
IEEE 20th Conference on Business Informatics (CBI), vol. 1, pp. 178–185, IEEE, 2018.
[21] E. Lejon, P. Kyösti, and J. Lindström, “Machine learning for detection of anomalies in
press-hardening: Selection of efficient methods,” Procedia Cirp, vol. 72, pp. 1079–1083,
2018.
[22] C. Zhang, Y. Zhu, Z. Ren, and K. Chen, “An unsupervised anomaly detection approach
based on industrial big data,” in 2019 2nd World Conference on Mechanical Engineering
and Intelligent Manufacturing (WCMEIM), pp. 703–709, IEEE, 2019.
[23] Y. Qin, X. Wang, and J. Zou, “The optimized deep belief networks with improved logistic
sigmoid units and their application in fault diagnosis for planetary gearboxes of wind turbines,” IEEE Transactions on Industrial Electronics, vol. 66, no. 5, pp. 3814–3824, 2018.
[24] Y. Wang, Z. Wei, and J. Yang, “Feature trend extraction and adaptive density peaks search
for intelligent fault diagnosis of machines,” IEEE Transactions on Industrial Informatics,
vol. 15, no. 1, pp. 105–115, 2018.
[25] R. Zhao, D. Wang, R. Yan, K. Mao, F. Shen, and J. Wang, “Machine health monitoring
using local feature-based gated recurrent unit networks,” IEEE Transactions on Industrial
Electronics, vol. 65, no. 2, pp. 1539–1548, 2017.
[26] F. T. Liu, K. M. Ting, and Z.-H. Zhou, “Isolation forest,” in 2008 eighth ieee international
conference on data mining, pp. 413–422, IEEE, 2008.