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研究生: 蔡宗諺
Tsung-Yen Tsai
論文名稱: 基於卷積及長短期記憶神經網路之主動式高壓馬達運轉狀態監測系統
Proactive Operation Condition Monitoring System of High-Voltage Motors Based on CNN and LSTM
指導教授: 張宏展
Hong-Chan Chang
口試委員: 陳鴻誠
郭政謙
黃維澤
李俊耀
學位類別: 碩士
Master
系所名稱: 電資學院 - 電機工程系
Department of Electrical Engineering
論文出版年: 2021
畢業學年度: 109
語文別: 中文
論文頁數: 69
中文關鍵詞: 高壓馬達深度學習運轉狀態監測系統卷積神經網路長短期記憶神經網路
外文關鍵詞: high-voltage motor, deep learning, monitoring system for operation condition, convolutional neural network, long short-term memory neural network
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  • 在現今社會裡,高壓馬達已成為不可或缺的動力來源之一,高壓馬達雖具有效率高、壽命長及高穩定等特性,但仍需保持長期運轉以確保經濟效能,因此關於高壓馬達的維護便成為一項重要議題。現階段實務上大都採用預防性維修保養的定期性維修保養的方式,此方式雖可降低故障發生機率,但容易因為超量維修導致資源的浪費。若能提早預測高壓馬達運轉狀態,事先預防,不僅能避免意外的發生,也能大大地降低維護成本。
    本研究致力於建置一套基於深度學習之主動式高壓馬達運轉狀態監測系統,本系統具有線上即時更新、自動特徵擷取及預測未來30分鐘運轉狀態趨勢等優點。首先利用量測平台擷取在某電廠中運轉之高壓馬達電氣、振動及溫度原始資料,計算高壓馬達之六項核心監測指標並儲存於資料庫中,接著從資料庫抓取其歷史運轉資料搭配本研究案例分析所選定之卷積及長短期記憶神經網路對六項核心監測指標分別做預測,最後藉由國際標準規範來判斷高壓馬達的運轉狀態,並做出最適合之維護決策。


    In today’s society, high-voltage motors have become an indispensable power source. Characterized by high efficiency, long life, and high stability, high-voltage motors still need to maintain long-term operation to ensure economic efficiency. Therefore, maintenance of high-voltage motors has become essential. At present, high-voltage motors mostly rely on preventive and predetermined maintenance, which reduces the probability of motor faults, but the excessive amount of maintenance easily wastes resources. Predicting the operation condition of high-voltage motors in advance allows for preemptive action to be taken, thereby preventing accidents as well as considerably reducing maintenance costs.
    This study endeavored to build a deep-learning-based proactive condition monitoring system for high-voltage motors, which boasts advantages of online real-time updates, automatic feature extraction, and the ability to predict the trend of operation condition in the following 30 minutes. First, a measurement platform was used to acquire the raw electrical, vibration, and temperature data of a high-voltage motor operating in a power plant. Specifically, six core monitoring indicators of the high-voltage motor were calculated and stored in a database. The motor’s historical operation data from the database were used in a convolutional and long-short-term memory neural network—selected for the case study—to predict values of the six core monitoring indicators. The operation condition of the high-voltage motor was then evaluated according to international standards to determine the optimal maintenance decision.

    中文摘要 II Abstract III 誌謝 V 目錄 VI 圖目錄 VIII 表目錄 X 第一章 緒論 1 1.1 研究背景與動機 1 1.2 研究方法與架構 3 1.3 文獻探討 5 1.4 章節概述 6 第二章 高壓馬達運轉狀態監測 7 2.1 前言 7 2.2 量測平台介紹與訊號量測 7 2.3 運轉狀態監測指標 12 2.3.1 電流變動率(Current Variation) 12 2.3.2 電壓變動率 (Voltage Variation) 12 2.3.3 電壓不平衡因數 (Voltage Unbalance Factor, VUF) 13 2.3.4 速度有效值 (Velocity rms) 14 2.3.5 位移有效值 (Displacement rms) 16 2.3.6 溫度 (Temperature) 16 2.4 高壓馬達標準國際規範 17 2.5 運轉狀態策略演進 19 第三章 基於深度學習之運轉狀態監測系統 20 3.1 前言 20 3.2 運轉狀態監測方法 21 3.2.1 卷積神經網路(CNN) 22 3.2.2 長短期記憶神經網路(LSTM) 26 3.2.3 複合型神經網路(CNN+LSTM) 28 3.3 預測流程與系統建置 30 第四章 實際案例分析與討論 33 4.1 實驗案例設計 33 4.2 實驗案例分析 34 4.2.1 案例一 模型初篩 34 4.2.2 案例二 選定合適機器模型 41 4.2.3 案例三 各種預測方法比較及國際標準調整 43 4.2.4 案例四 主動式運轉狀態監測系統功能展示 45 4.3 實驗結果討論 52 第五章 結論及未來展望 54 5.1 結論 54 5.2 未來展望 55 參考文獻 56

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