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研究生:劉家豐
研究生(外文):LIU, JIA-FENG
論文名稱:取樣分析及深度學習方法於內藏式永磁同步馬達退磁診斷之研究
論文名稱(外文):Research On Sampling Analysis And Deep Learning Methods For Demagnetization Diagnosis Of Interior Permanent Magnet Synchronous Motors
指導教授:陳金聖陳金聖引用關係
指導教授(外文):CHEN, CHIN-SHENG
口試委員:蔡孟伸劉孟昆陳金聖
口試委員(外文):TSAI, MEN-SHENLIU, MENG-KUNCHEN, CHIN-SHENG
口試日期:2024-01-22
學位類別:碩士
校院名稱:國立臺北科技大學
系所名稱:自動化科技研究所
學門:工程學門
學類:機械工程學類
論文種類:學術論文
論文出版年:2024
畢業學年度:112
語文別:中文
論文頁數:81
中文關鍵詞:故障診斷局部退磁均勻退磁深度學習取樣分析
外文關鍵詞:Partial demagnetizationFault diagnosisUniform demagnetizationDeep learningSampling analysis
相關次數:
  • 被引用被引用:0
  • 點閱點閱:51
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永磁同步馬達(Permanent Magnet Synchronous Motor, PMSM)可能發生退磁故障的原因有很多,常見的有:高溫、過電流、設計缺陷等。在馬達發生退磁的情況下,馬達內部磁通鏈產生變化,間接影響馬達產生之電氣量,如:電壓、電流、反電勢等。發生局部退磁、均勻退磁與健康狀態比較,使這些特徵產生差異性。
本文依據工研院提供的內藏式永磁同步馬達退磁實驗數據,針對不同負載和轉速條件搭配下,提出深度學習的監督學習中的卷積神經網路(Convolutional Neural Network, CNN)以及無監督學習中卷積自編碼器(Convolutional Auto-Encoder, CAE)兩種方法,健康與兩種退磁錯誤能夠成功的分類,達到故障診斷的目的。
另外本文亦探討了取樣頻率的問題,因工研院資料使用外接擷取卡提供高取樣率,使深度學習容易辨認。但如僅使用馬達驅動器輸出的資訊,則會因為馬達驅動器資源限制,導致無法取得高取樣率的數據,造成在深度學習中難以辨識。本文分析在超低取樣率的條件下,重新組合馬達驅動器的取樣數據,使還原與高取樣率的數據類似,並成功讓深度學習進行辨識。

There are many reasons why permanent magnet synchronous motor (PMSM) may have demagnetization failure. Common ones include: high temperature, overcurrent, design defects, etc. When the motor demagnetizes, the internal flux linkage of the motor changes, which indirectly affects the electrical quantities generated by the motor, such as voltage, current, back electromotive force, etc. The occurrence of local demagnetization, uniform demagnetization and comparison with the healthy state make these characteristics different.
Based on the demagnetization experimental data of the built-in permanent magnet synchronous motor provided by ITRI, this article proposes the supervised learning convolutional neural network (CNN) and unsupervised learning (Auto-Encoder, AE) deep learning in under different load and speed conditions, health and two demagnetization errors can be successfully classified to achieve the purpose of predictive maintenance.
In addition, this article also discusses the issue of sampling frequency, because ITRI data uses an external capture card to provide a high sampling rate, making deep learning easy to identify. However, if only the information output by the motor driver is used, high sampling rate data will not be obtained due to the resource limitations of the motor driver, making it difficult to identify in deep learning. This article analyzes how to recombine the sampling data of the motor driver under ultra-low sampling rate conditions, so that the restoration is similar to the data with a high sampling rate, and successfully allows deep learning to perform identification.

摘要 i
ABSTRACT ii
誌謝 iv
目錄 v
表目錄 vii
圖目錄 viii
1 第一章 緒論 1
1.1 研究背景與動機 1
1.2 文獻回顧 3
1.3 研究方法 6
1.4 文章架構 6
2 第二章 馬達與錯誤模型 8
2.1 馬達模型 8
2.1.1 三相馬達模型 8
2.1.2 固定軸馬達模型 11
2.1.3 同步軸馬達模型 13
2.2 退磁模型及特徵 17
2.2.1 單槽磁通鏈與反電勢 18
2.2.2 單相磁通鏈與反電勢 19
3 第三章 數據取樣問題與重建方法 22
3.1 數據取樣問題 22
3.2 數據重建方法 27
3.2.1 取樣點數搜尋 27
3.2.2 弦波特性還原 34
4 第四章 診斷方法 39
4.1 卷積類神經網路 39
4.1.1 卷積層 39
4.1.2 池化層 41
4.1.3 卷積神經網路架構&分類任務損失函數 42
4.2 自動編碼器 43
4.2.1 卷積自編碼器 44
5 第五章實驗結果 48
5.1 實驗平台&數據介紹 48
5.1.1 錯誤模型與退磁特徵 50
5.2 數據重建結果 51
5.3 深度學習診斷結果 65
5.3.1 CAE訓練&測試 66
5.3.2 分類結果與分析 71
6 第六章結論 77
參考文獻 78


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