WEKO3
アイテム
Research on Intelligent Diagnosis Technology of Rotating Machinery by Fault Feature Extraction of Vibration Signal
http://hdl.handle.net/10076/00020952
http://hdl.handle.net/10076/000209524f513b51-ac1c-4363-abff-8f28676e16b6
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
2022DB0902 (6.0 MB)
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| Item type | 学位論文 / Thesis or Dissertation(1) | |||||||||||
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| 公開日 | 2022-12-08 | |||||||||||
| タイトル | ||||||||||||
| タイトル | Research on Intelligent Diagnosis Technology of Rotating Machinery by Fault Feature Extraction of Vibration Signal | |||||||||||
| 言語 | en | |||||||||||
| 言語 | ||||||||||||
| 言語 | eng | |||||||||||
| 資源タイプ | ||||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_db06 | |||||||||||
| 資源タイプ | doctoral thesis | |||||||||||
| アクセス権 | ||||||||||||
| アクセス権 | open access | |||||||||||
| アクセス権URI | http://purl.org/coar/access_right/c_abf2 | |||||||||||
| 著者 |
段, 棠少
× 段, 棠少
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| 抄録 | ||||||||||||
| 内容記述タイプ | Abstract | |||||||||||
| 内容記述 | In rotating machinery, the rolling element bearing is one of the most widely used pieces of mechanical equipment, and meanwhile is also very easily destroyed. Over 40% faults of induction machines are caused by bearing component faults. Therefore, Fault Detection and Diagnosis (FDD) of potential bearing faults are very important and necessary when it comes to reliable operation of a given system. When fault occurs, such as fatigue, flake crack, and so on, since rolling element bearing is in a periodical rotating working model, in each rotation the excitation appears to be periodicity. So when a bearing is destroyed, the signal we measured, especially the vibration signal, generally causes strong periodicity in signal. However, due to the complex working environment of bearing, the low signal-to-noise ratio (SNR) of vibration signals causes difficulties in fault diagnosis. To dynamically track bearing failure degradation，a generative fault diagnosis model based on state-space principal component tracking filtering (SPCTF) is proposed. The proposed method can dynamically track the bearing fault status and detect bearing faults in real time. Experiments on bearing data of three different speeds (500RPM,1000RPM, and 1500RPM) and three fault states (inner race fault, outer race fault and roller fault) show the effectiveness of the proposed method. For the weak features of low-speed rolling bearing fault characteristics, an improved Teager energy operator (ITEO) is proposed. ITEO overcomes the shortcomings of sensitive to noise and vibration interference of the traditional Teager energy operator (TEO), through the method of amplifying the shock energy of the vibration signal, the fault features of the low-speed bearing are extracted. Then, an intelligent diagnosis model of low-speed bearings based on ITEO-AE is proposed, which can realize the fault diagnosis of low-speed bearings. The effectiveness of the proposed method is proved by the diagnostic experiments of bearing vibration signals for two different speeds (70RPM and 100RPM) and three fault states (inner race fault, outer race fault and roller fault). In order to prevent major accidents, early diagnosis of bearing faults is crucial. Therefore, the early fault diagnosis model of rolling bearing based on CRBM-PCA is proposed，which can adaptively extract the early faults of rolling bearings, excellent for diagnosing faulty bearings with small dimensions. In order to more realistically simulate the early failure of bearings, three different sizes of faulty bearings (0.6W*0.3D, 1.2W*0.3D and 2.0W*0.3D) were used in the experiments. The major contributions of this thesis are a variety of rolling bearing fault diagnosis methods for different speeds and different fault periods are proposed. All of proposed methods proposed in this thesis have been verified during the experiments, under different kinds of fault rolling bearings. |
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| 言語 | en | |||||||||||
| 内容記述 | ||||||||||||
| 内容記述タイプ | Other | |||||||||||
| 内容記述 | 本文/三重大学大学院 生物資源学研究科 共生環境学専攻 環境・生産科学講座 環境情報システム工学教育研究分野 | |||||||||||
| 内容記述 | ||||||||||||
| 内容記述タイプ | Other | |||||||||||
| 内容記述 | 93p | |||||||||||
| 書誌情報 |
発行日 2022-09-21 |
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| フォーマット | ||||||||||||
| 内容記述タイプ | Other | |||||||||||
| 内容記述 | application/pdf | |||||||||||
| 著者版フラグ | ||||||||||||
| 出版タイプ | VoR | |||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||||||
| その他の言語のタイトル | ||||||||||||
| その他のタイトル | 振動信号の故障特徴抽出による回転機械の知的診断技術に関する研究 | |||||||||||
| 言語 | ja | |||||||||||
| 出版者 | ||||||||||||
| 出版者 | 三重大学 | |||||||||||
| 出版者（ヨミ） | ||||||||||||
| ミエダイガク | ||||||||||||
| 学位名 | ||||||||||||
| 学位名 | 博士(学術) | |||||||||||
| 学位授与機関 | ||||||||||||
| 学位授与機関識別子Scheme | kakenhi | |||||||||||
| 学位授与機関識別子 | 14101 | |||||||||||
| 学位授与機関名 | 三重大学 | |||||||||||
| 学位授与年月日 | ||||||||||||
| 学位授与年月日 | 2022-09-21 | |||||||||||
| 学位授与番号 | ||||||||||||
| 学位授与番号 | 甲学術第2157号 | |||||||||||
| 資源タイプ（三重大） | ||||||||||||
| Doctoral Dissertation / 博士論文 | ||||||||||||
