本文選題：形態(tài)濾波 + 頻率切片小波變換　； 參考：《武漢科技大學(xué)》2014年博士論文

【摘要】：研究旋轉(zhuǎn)機(jī)械的故障診斷技術(shù)，對于保障設(shè)備安全運(yùn)行、減少重大的經(jīng)濟(jì)損失和避免災(zāi)難性事故的發(fā)生具有十分重要的意義。大多數(shù)旋轉(zhuǎn)機(jī)械的振動信號是非平穩(wěn)信號，時頻分析方法能同時提取振動信號時域和頻域的局部信息，適用于旋轉(zhuǎn)機(jī)械故障診斷。但是短時傅立葉變換(Short Time Fourier Transform，STFT)、Winger-Ville分布、小波變換和Hilbert-Huang變換等時頻分析方法都存在各自的缺陷，故迫切需要研究新的旋轉(zhuǎn)機(jī)械故障診斷方法。本文對頻率切片小波變換、局域均值分解、本征時間尺度分解方法的理論及其在旋轉(zhuǎn)機(jī)械故障診斷中的應(yīng)用進(jìn)行了深入的研究。其主要內(nèi)容如下: 1.信號中的噪聲會降低頻率切片小波變換分析的頻率分辨率，為此，提出了基于形態(tài)濾波、自相關(guān)分析和頻率切片小波變換的軸承故障診斷方法。提出一種多結(jié)構(gòu)元素差值形態(tài)濾波器，它比單一結(jié)構(gòu)元素的差值形態(tài)濾波器降噪效果好，仿真信號與軸承故障診斷實(shí)例的分析驗(yàn)證了該方法的有效性。提出了基于時延自相關(guān)和頻率切片小波變換的齒輪故障診斷方法，對齒輪故障信號進(jìn)行頻率切片小波變換分析前，進(jìn)行自相關(guān)降噪處理能突出故障特征，提高頻率分辨率。 2.論述了LMD和1.5維譜原理，針對信號中混入的噪聲對局域均值分解結(jié)果造成影響的問題，提出了一種局域均值分解和1.5維譜相結(jié)合的故障診斷方法。針對局域均值分解方法計(jì)算效率低的問題，提出了一種基于B樣條插值的局域均值分解(B-spline LocalMean Decomposition，BLMD)方法，在此基礎(chǔ)上，提出了基于BLMD的時頻分析方法并應(yīng)用到軸承和齒輪的故障診斷中。提出了基于BLMD與倒雙譜的故障診斷方法并應(yīng)用到軸承與齒輪故障診斷中，仿真信號的分析與軸承和齒輪故障診斷實(shí)例驗(yàn)證了該方法的有效性。 3.針對常用的非平穩(wěn)信號處理方法的局限性以及本征時間尺度分解的失真問題，提出了B樣條改進(jìn)的本征時間尺度分解(BITD)方法，在此基礎(chǔ)上，提出了基于BITD的局部能量譜方法。針對齒輪故障振動信號的非平穩(wěn)特征，提出了B樣條插值的本征時間尺度分解和同態(tài)濾波解調(diào)相結(jié)合的故障診斷方法。首先采用BITD方法對齒輪振動信號進(jìn)行分解，將其分解為若干個合理旋轉(zhuǎn)(Proper Rotation，PR)分量之和，然后用相關(guān)系數(shù)篩選出最能表征故障信息的PR分量進(jìn)行同態(tài)濾波解調(diào)提取故障特征。仿真信號與齒輪故障診斷工程實(shí)例的分析驗(yàn)證了該方法的有效性。提出了基于BITD、能量算子和對角切片譜的旋轉(zhuǎn)機(jī)械故障診斷方法，通過對仿真和實(shí)驗(yàn)信號的分析驗(yàn)證了該方法的有效性。 4.論述了隨機(jī)共振降噪的原理，并結(jié)合BITD方法，提出將隨機(jī)共振與BITD相結(jié)合的特征提取方法，并通過仿真信號與實(shí)驗(yàn)信號的分析驗(yàn)證了方法的有效性；研究基于EMD的信號降噪方法，在分析已有基于EMD降噪方法不足的基礎(chǔ)上，提出兩種基于BITD的閾值消噪方法，，并將其用于滾動軸承故障信號的降噪和特征提取技術(shù)中。通過仿真信號與實(shí)驗(yàn)信號的分析驗(yàn)證了該方法的有效性。 5.在論述排列熵(Permutation Entropy，PE)和基本尺度熵(Base-scale Entropy，BE)原理的基礎(chǔ)上，提出了基于BITD和排列熵的滾動軸承障診斷方法，采用BITD方法對滾動軸承振動信號進(jìn)行分解，再對得到的前4個合理旋轉(zhuǎn)分量計(jì)算其排列熵，并將熵值作為特征向量輸入支持向量機(jī)分類器，從而實(shí)現(xiàn)滾動軸承故障類別的診斷，實(shí)驗(yàn)數(shù)據(jù)分析結(jié)果表明，該方法能有效地實(shí)現(xiàn)滾動軸承故障類型的診斷。針對齒輪振動信號的非線性、非平穩(wěn)特征和難以獲取大量故障樣本的問題，提出了BITD和基本尺度熵的齒輪故障診斷方法。首先采用BITD方法對齒輪振動信號進(jìn)行分解，再對得到的第一個有意義的合理旋轉(zhuǎn)分量計(jì)算其基本尺度熵，并將熵值作為特征向量輸入支持向量機(jī)分類器，從而實(shí)現(xiàn)齒輪故障類別的診斷，實(shí)驗(yàn)數(shù)據(jù)分析的結(jié)果表明，該方法能有效地實(shí)現(xiàn)齒輪故障類型的診斷。
[Abstract]:The study of the fault diagnosis technology of rotating machinery is of great significance for ensuring the safe operation of the equipment, reducing the major economic losses and avoiding the occurrence of catastrophic accidents. Rotating machinery fault diagnosis. But short time Fu Liye transform (Short Time Fourier Transform, STFT), Winger-Ville distribution, wavelet transform and Hilbert-Huang transform have their own defects. Therefore, it is urgent to study the new method of rotating machinery fault diagnosis. In this paper, the frequency slice wavelet transform, local mean mean decomposition, The theory of intrinsic time scale decomposition and its application in fault diagnosis of rotating machinery are studied in detail.
The noise in the 1. signal will reduce the frequency resolution of the frequency slice wavelet transform analysis. Therefore, a bearing fault diagnosis method based on morphological filtering, autocorrelation analysis and frequency slice wavelet transform is proposed. A multi structure element difference morphological filter is proposed, which is better than the difference form filter of a single structure element. The validity of this method is verified by the analysis of real signal and bearing fault diagnosis example. A gear fault diagnosis method based on time delay autocorrelation and frequency slice wavelet transform is proposed. Before the frequency slice wavelet transform analysis of the gear fault signal, the autocorrelation noise reduction processing can highlight the fault features and improve the frequency resolution.
2. the principle of LMD and 1.5 dimensional spectrum is discussed. In view of the problem that the noise mixed in the signal affects the local mean decomposition results, a fault diagnosis method combining local mean mean decomposition and 1.5 dimensional spectrum is proposed. A local mean decomposition (B) spline interpolation based on local mean decomposition (local mean mean decomposition) is proposed. B-spline LocalMean Decomposition, BLMD) method, and on this basis, a time-frequency analysis method based on BLMD is proposed and applied to the fault diagnosis of bearing and gear. A fault diagnosis method based on BLMD and inverted bispectrum is proposed and applied to the fault diagnosis of bearing and gear, the analysis of the imitation true signal and the fault diagnosis of bearing and gear. The validity of the method is verified.
3. in view of the limitation of the commonly used nonstationary signal processing methods and the distortion of eigentime scale decomposition, the improved eigentime scale decomposition (BITD) method of B spline is proposed. On this basis, a local energy spectrum method based on BITD is proposed. The B spline interpolation is proposed for the non stationary characteristics of the vibration signal of the gear fault. The fault diagnosis method combined with eigentime scale decomposition and homomorphic filtering demodulation. First, the BITD method is used to decompose the gear vibration signal and decompose it into several reasonable rotation (Proper Rotation, PR) components, and then the PR component which can characterize the fault information is selected by the correlation coefficient to extract the homomorphic filter demodulation. The effectiveness of the method is verified by the analysis of simulation signals and gear fault diagnosis engineering examples. A fault diagnosis method for rotating machinery based on BITD, energy operator and diagonal slice spectrum is proposed. The effectiveness of the method is verified by the analysis of simulation and experimental signals.
4. the principle of random resonance noise reduction is discussed, and combined with BITD method, a feature extraction method combining random resonance with BITD is proposed, and the effectiveness of the method is verified by the analysis of the simulation signal and the experimental signal. The method of signal noise reduction based on EMD is studied and two kinds of methods are proposed on the basis of the analysis of the shortcomings of the existing EMD denoising methods. The method of threshold de-noising based on BITD is used in the technology of noise reduction and feature extraction of rolling bearing fault signals. The effectiveness of the method is verified by the analysis of simulation and experimental signals.
5. on the basis of the principle of Permutation Entropy (PE) and basic scale entropy (Base-scale Entropy, BE), the diagnosis method of rolling bearing barrier based on BITD and permutation entropy is proposed. The vibration signal of rolling bearing is decomposed by BITD method, and then the entropy is calculated by the first 4 reasonable rotating components and entropy value is used as a method. The feature vector input support vector machine classifier is used to diagnose the rolling bearing fault classification. The results of experimental data analysis show that the method can effectively diagnose the fault types of rolling bearings. In view of the nonlinear, non stationary characteristics of the gear vibration signal and the difficulty of obtaining a large number of fault samples, the BITD and the basic method are proposed. The gear fault diagnosis method of scale entropy. First, the BITD method is used to decompose the gear vibration signal, and then the first meaningful and reasonable rotation component is used to calculate its basic scale entropy, and the entropy value is used as the eigenvector to input the support vector machine classifier, thus the diagnosis of the gear fault classification is realized and the experimental data analysis is made. The results show that the method can effectively diagnose gear fault types.
【學(xué)位授予單位】：武漢科技大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2014
【分類號】：TH165.3

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