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研究生: 李羿
Lee, I
論文名稱: MEMS智慧感測器邊際運算系統設計暨感應電動機之缺陷診斷與辨識應用
MEMS Smart Sensors Edge Computing System Design and Application for Induction Motor Fault Diagnosis and Recognition
指導教授: 戴政祺
Tai, Cheng-Chi
學位類別: 碩士
Master
系所名稱: 電機資訊學院 - 電機工程學系
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: 82
中文關鍵詞: 邊際運算微機電技術瑕疵馬達缺陷檢測智慧辨識模型
外文關鍵詞: Edge Computing, MEMS, defective motor detection, intelligent identification model
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  • 在物聯網(IoT)技術的革新下,一種新型的運算方式因應誕生,其名為「邊際運算(Edge Computing)」,大幅降低感測器節點傳輸資料所致之頻寬不足問題。隨著MEMS感測器技術的成熟與AI智慧辨識的普及之下,具邊際運算功能之MEMS智慧感測器應運而生。工業上常見的旋轉電機,因其應用性極廣,其中的預知診斷技術也須要不斷革新。在這樣的背景下,本文提出一種邊際運算系統:以具低成本且高穩定度優勢的MEMS智慧感測器模組之硬體系統,與微處理機之韌體程式相互搭配,於感測器終端先行計算時頻域等特徵資料以降低頻寬之負載,並與人機介面等電腦端軟體相互結合,除了呈現接收之特徵資料之外,更輔以機器學習的演算法作為預測模型,亦根據交叉驗證確保模型之穩定性。此外,本文以旋轉電機作為實驗驗證平台,針對馬達的故障檢測與軸承及轉子的損壞作為系統之驗證評估,透過使用者與人機介面之互動,可立即於軟體上呈現出馬達相應的健康狀況,除了探討相應的時頻域特徵之外,亦能夠以預設之機器學習模型進行故障診斷,並且判定可能的缺陷類型,以進行後續的採取措施。本文結合MEMS智慧感測器模組與人機介面為一邊際運算系統,並且實測相關的馬達平台參數,進一步分析其特徵,得到相對應之理論與成果,且將其用於所建立之辨識模型上,並經實測其為準確率高達95%之高穩定性模型。

    A new type of computing method, named “Edge Computing”, was born under the innovation of the Internet of Things (IoT), which greatly reduces the problem of insufficient bandwidth happening when sensor nodes transmit data. With the well-developed technology of MEMS and the popularization of smart recognition with AI, MEMS smart sensors with edge computing are generated. The rotating electrical machines, which is common in this industry, has a wide range of applications, and the related predictive diagnosis technology also needs continual innovation. In the background, we propose an edge computing system: a hardware system with a low-cost and high-stability MEMS smart sensor modules. This kind of modules are used as the sensor terminals which match with firmware of the microcontrollers. With this system, time domain and frequency domain features will be calculated in advance to reduce the bandwidth load and the modules will be combined with the software in computer end such as human-machine interface. In addition to showing the received features from MCU, we also use machine learning algorithms as the predictive model and ensure the stability of the model with cross validation. Also, the rotating electrical machines are used as the experimental verification platform, and this system is evaluated and validated by the fault diagnosis of motors and the damages of the bearings and rotors. With the interaction between the users and human-machine interfaces, the corresponding health conditions of motors will be immediately displayed on the software. Aside from studying the features in the time domain and frequency domain, the system is able to do fault diagnosis with the preset machine learning model and find out the possible defect types, supporting the following measures. In conclusion, we combine MEMS smart sensor modules and the human-machine interface as the edge computing system, measure the related parameters of the motor platform, analyze the features and obtain the corresponding the theories and results. Those are used for the established models and it’s a highly stable model with an accuracy rate of 95%.

    摘 要 I Extended Abstract II 誌謝 XII 目錄 XIII 表目錄 XVI 圖目錄 XVIII 第一章 緒論 1 1-1 研究背景 1 1-2 國內外文獻回顧 3 1-3 研究動機與目的 8 1-4 論文架構 10 第二章 相關技術原理 11 2-1 前言 11 2-2 MEMS震動感測器介紹 11 2-2-1 加速規原理 11 2-2-2 MEMS電容式加速規 14 2-2-3 MEMS智慧感測器模組 15 2-3 智慧感測器模組相關技術 17 2-3-1 ITRI 感測器 17 2-3-2 ADI ADXL355 23 2-4 馬達缺陷檢測方法 26 2-4-1 鼠籠式感應電動機 26 2-4-2 故障診斷 27 2-4-3 時域分析 28 2-4-4 頻域分析 30 2-5 機器學習演算法 35 2-5-1 演算法流程 35 2-5-2 交叉驗證 37 第三章 邊際運算系統設計與演算法開發 39 3-1 前言 39 3-2 設計發想與系統架構 39 3-3 系統規格比較 41 3-3-1 智慧感測器模組之比較 41 3-3-2 微處理機之比較 42 3-4 韌體開發與設計 44 3-4-1 韌體演算法之開發 44 3-4-2 韌體設計流程圖 46 3-5 人機系統之設計流程與架構 49 3-5-1系統設計流程圖 49 3-5-2 主系統區 52 3-5-3 機器學習辨識缺陷區 55 3-5-4 缺陷頻率估算區 57 第四章 實驗架構與結果討論 58 4-1 實驗動機與目的 58 4-2 實驗架構 59 4-2-1 實驗流程 59 4-2-2 實驗平台之架設 59 4-2-3 實驗平台規格 60 4-3 實驗結果 62 4-3-1 時域結果分析 62 4-3-2 頻域結果分析 69 4-3-3 特徵辨識之模型穩定度探討 72 4-4 討論 74 第五章 結論與未來展望 76 5-1 結論 76 5-2 未來展望 77 參考文獻 79

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