Bearing Fault Diagnosis Method by Multilevel Spectral Segmentation Theory and Signal Fusion

• 資料種別: 博士論文
• タイトル: Bearing Fault Diagnosis Method by Multilevel Spectral Segmentation Theory and Signal Fusion
• 著者・編者: 張, 坤
• 著者標目: 張, 坤
• 出版事項: 三重大学
• 出版年月日等: 2022-09-21
• 出版年（W3CDTF）: 2022-09-21
• 並列タイトル等: マルチレベルスペクトルセグメンテーション理論と信号融合による軸受故障診断法
• 授与機関名: 三重大学
• 授与年月日: 2022-09-21
• 授与年月日（W3CDTF）: 2022-09-21
• 報告番号: 甲学術第2160号
• 学位: 博士(学術)
• 本文の言語コード: eng
• 著者別名: ZHANG, KUN
• 対象利用者: 一般
• 一般注記: application/pdf; Effectively identifying the health status of rolling bearings can reduce the maintenance costs of rotating mechanical components. With the development and improvement of various signal processing theories, the mode of extracting fault information from the frequency domain has gradually replaced the mode from the time domain. In this paper, by optimizing the singlelevel spectral segmentation methods such as analytical mode decomposition, frequency slice wavelet transform, empirical wavelet transform, and quaternion, the corresponding multi-level spectral segmentation method and variable tower boundary distribution diagram and feature screening index are designed. The detailed research content is as follows:(1) An adaptive Ailinggram that uses variable spectral segmentation framework to optimize analytical mode decomposition to automatically decompose the mode information in rotating machinery signals was proposed. The framework relies on the variability of the window width and envelope estimation characteristics of order statistics filter to increase the diversity of the center frequencies and bandwidth. A novel harmonic correlation index is designed to identify the characteristics of rotating machinery faults from various levels of results, and to improve the usability in mechanical equipment fault diagnosis. The method can be applied to fault diagnosis of rotating machinery under high speed/dynamic load conditions.(2) Fast Entrogram was proposed to segment the spectrum and accurately filtering fault information from the frequency domain. The fluctuation state of the Fourier spectrum is of key importance in distinguishing the distribution of different components in the signal at each frequency. After the Fourier transform of the spectrum is intercepted and reconstructed, the minimum points of the new sequence can separate different components in the signal. Subsequently, the frequency slice function is used to extract each frequency band to obtainbetter filtering effects than the finite impulse response filter. Finally, the proposed novel correlation spectral negentropy is sensitive to periodic pulses and can be used to screen the component that contains the most fault information. The simulation results show that the proposed Fast Entrogram can effectively extract periodic pulses. It is verified by experimental signals that the method can be applied to fault diagnosis of rotating machinery under low speed/heavy load conditions.(3) The power spectral density will be calculated and used to segment the spectrum, which can reduce the number of extreme points and the dependence on them. According to the variability of the PSD window width, a tower boundaries distribution diagram (W-Autogram) and weighted unbiased autocorrelation would be used to extract specific information is proposed. Simulation signals and experimental results verify that the proposed method can be applied to the fault diagnosis of rolling bearings in rotating machinery.(4) In order to extract the periodic pulse information in the signal and weaken the influence of the interference signal, we proposed Harmonic spectral kurtosis which can extract the harmonic information in the envelope spectrum, quantify the periodic pulses in the signal, and suppress the influence of interference such as random pulse. The simulation signal shows that the proposed method is accurate and effective. The data of bearing inner ring, outer ring and compound faults prove that the method can be applied to bearing fault diagnosis.(5) Quaternion analytical mode decomposition (QAMD) is proposed to process multiple acoustic signals and extract fault information in industrial machinery systems with high sampling frequency, low speed, and heavy load. QAMD can separate characteristic information from frequency domain and extend it to the fault diagnosis of rotating industrial machinery. The multi-signal fusion method based on quaternion can process multiple sets of longer digital signals at the same time, which provides a new idea for the synchronous processing of big data. The proposed quaternion Fourier trend spectral segmentation method can not only automatically obtain bisecting frequencies and divide the signal into several frequency bands, but also realize the fusion and modal decomposition of multiple sets of digital signals in frequency domain. Experimental results show that the proposed method can effectively extract useful information from acoustic signals and apply it to bearing fault diagnosis.; 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; 107p
• 国立国会図書館永続的識別子: info:ndljp/pid/12651370; https://dl.ndl.go.jp/pid/12651370
• コレクション（共通）: 障害者向け資料
• コレクション（障害者向け資料：レベル1）: テキストデータ
• コレクション（個別）: 国立国会図書館デジタルコレクション > デジタル化資料 > 博士論文; https://dl.ndl.go.jp/collections/A00014
• 収集根拠: 博士論文（自動収集）
• 受理日（W3CDTF）: 2023-03-02T11:18:10+09:00
• 記録形式（IMT）: application/pdf
• オンライン閲覧公開範囲: 国立国会図書館内限定公開
• デジタル化資料送信: 図書館・個人送信対象外
• 遠隔複写可否（NDL）: 可
• 掲載誌（URI）: http://hdl.handle.net/10076/00020954; https://mie-u.repo.nii.ac.jp/records/15336
• 連携機関・データベース: 国立国会図書館 : 国立国会図書館デジタルコレクション; https://dl.ndl.go.jp