齒輪箱故障振動(dòng)信號(hào)去噪及特征提取算法研究
發(fā)布時(shí)間：2018-07-17 00:23
【摘要】：齒輪箱作為機(jī)械設(shè)備中一種必不可少的連接和傳遞動(dòng)力的通用零部件,在金屬切削機(jī)床、航空、電力系統(tǒng)、農(nóng)業(yè)機(jī)械、運(yùn)輸機(jī)械、冶金機(jī)械等現(xiàn)代工業(yè)設(shè)備中得到了廣泛的應(yīng)用。作為傳動(dòng)機(jī)械,其運(yùn)行狀態(tài)好壞與否,直接影響到整個(gè)機(jī)械傳動(dòng)組的工作性能。因此研究齒輪箱故障診斷技術(shù)和方法,對(duì)齒輪箱進(jìn)行狀態(tài)檢測(cè)及故障分析診斷、確保齒輪箱正常工作具有重要意義。 齒輪箱故障診斷技術(shù)是通過分析處理齒輪箱運(yùn)行時(shí)的狀態(tài)信息。定量識(shí)別其技術(shù)狀態(tài),并診斷異常故障狀態(tài)的一門多學(xué)科交叉的綜合技術(shù)。齒輪箱振動(dòng)信號(hào)中包含了大量的工作狀態(tài)信息,利用齒輪箱振動(dòng)信號(hào)診斷故障是一種行之有效的方法。應(yīng)用振動(dòng)信號(hào)分析法對(duì)齒輪箱進(jìn)行故障診斷的過程中,濾波去噪處理、故障特征提取是其中尤為重要的兩個(gè)問題,一直被公認(rèn)為是故障診斷中的關(guān)鍵環(huán)節(jié)。本文從工程實(shí)際應(yīng)用的角度出發(fā),分別以風(fēng)力發(fā)電機(jī)行星齒輪箱和通用工業(yè)齒輪箱故障振動(dòng)信號(hào)為具體研究對(duì)象,綜合應(yīng)用譜峭度、黃金分割、下山單純形、小波分析、快速傅里葉變換、粒子群優(yōu)化、沖擊響應(yīng)譜、瞬態(tài)分析等理論,基于混合優(yōu)化理念,對(duì)齒輪箱故障振動(dòng)信號(hào)去噪及故障特征提取算法兩個(gè)關(guān)鍵問題進(jìn)行了系統(tǒng)地研究,為齒輪箱故障診斷技術(shù)的開發(fā)研究提供了一定的理論支持。 本文重在研究齒輪箱故障振動(dòng)信號(hào)的優(yōu)化濾波去噪和故障脈沖瞬態(tài)特征提取算法。 (1)分析了齒輪箱振動(dòng)信號(hào)常用分析方法的基本原理及適用范圍,為后續(xù)的振動(dòng)信號(hào)去噪、故障特征提取算法研究提供一定的理論支持。 (2)為了解決單一的黃金分割法優(yōu)化速度較慢的問題,將黃金分割和拋物線插值兩種算法相結(jié)合構(gòu)成加速一維搜索算法。 (3)為了解決單一的傳統(tǒng)多維優(yōu)化算法收斂速度較慢,且收斂時(shí)容易陷入局部極值的問題。以峭度最大值為目標(biāo)函數(shù),根據(jù)振動(dòng)信號(hào)頻域分析和小波分析的原理,基于一維搜索-多維搜索(參數(shù)粗調(diào)-參數(shù)微調(diào))混合優(yōu)化的自適應(yīng)濾波算法,分別采用了兩種不同的濾波手段。切比雪夫帶通濾波和Morlet小波濾波。應(yīng)用混合優(yōu)化算法,即譜峭度-加速一維搜索算法-下山單純形法分別優(yōu)化切比雪夫帶通濾波器和Morlet小波濾波器的設(shè)計(jì)參數(shù),對(duì)齒輪箱故障振動(dòng)信號(hào)進(jìn)行濾波去噪仿真處理。 (4)為了對(duì)比不同類型混合優(yōu)化算法的優(yōu)劣性,將以下四種優(yōu)化算法：譜峭度、黃金分割、下山單純形、遺傳算法,按一維搜索-多維搜索模式混合優(yōu)化切比雪夫帶通濾波器參數(shù),并進(jìn)行去噪仿真實(shí)驗(yàn)。 (5)在前期研究的基礎(chǔ)上將粒子群優(yōu)化算法應(yīng)用到齒輪箱故障振動(dòng)信號(hào)去噪中,將基于加速一維搜索-粒子群優(yōu)化的混合優(yōu)化算法用于優(yōu)化切比雪夫帶通濾波器和Morlet小波濾波器的相關(guān)參數(shù),并對(duì)齒輪箱故障振動(dòng)信號(hào)進(jìn)行濾波去噪仿真處理。 (6)為了提取能夠反映齒輪箱工作信息變化情況以及故障未來發(fā)展趨勢(shì)的故障脈沖瞬態(tài)特征,將沖擊響應(yīng)譜分析和瞬態(tài)分析法應(yīng)用到齒輪箱齒輪的故障特征提取中,用于提取衡量齒輪箱故障嚴(yán)重程度的三個(gè)瞬態(tài)特征指標(biāo)：沖擊響應(yīng)譜指標(biāo)SRS以及齒輪嚙合系統(tǒng)的固有頻率ωn、振蕩阻尼比ζ。
[Abstract]:As a necessary part of the mechanical equipment to connect and transmit power, the gear box is widely used in the modern industrial equipment such as metal cutting machine, aviation, power system, agricultural machinery, transportation machinery and metallurgical machinery. As a transmission machine, its running state is directly affected by the whole machine transmission. Therefore, it is important to study the fault diagnosis technology and method of the gear box, to detect the gear box and to diagnose the fault, so as to ensure the normal work of the gear box.
The gear box fault diagnosis technology is a multidisciplinary and interdisciplinary technology which can identify the state of the gear box and diagnose the state of the gear box, and diagnose the abnormal state of the fault. The vibration signal of the gear box contains a lot of work state information. It is effective to diagnose the fault by using the vibration signal of the gear box. In the process of fault diagnosis of gear box by vibration signal analysis, filtering de-noising and fault feature extraction are two important problems, which have always been recognized as the key link in fault diagnosis. This paper, from the angle of practical application of the engineering, uses the planetary gearbox of wind turbine and general purpose respectively. The fault vibration signal of industrial gear box is a specific research object, and two key questions, such as spectral kurtosis, gold segmentation, downhill simplex, wavelet analysis, fast Fourier transform, particle swarm optimization, impact response spectrum, transient analysis, etc., are used to denoise and extract fault characteristics of gearbox vibration signal based on mixed optimization idea. A systematic study is carried out to provide some theoretical support for the development and research of gearbox fault diagnosis technology.
This paper focuses on the research of gearbox fault vibration signal optimization filter denoising and fault pulse transient feature extraction algorithm.
(1) the basic principle and application range of the common analysis method of the vibration signal of the gear box are analyzed. It provides a certain theoretical support for the subsequent vibration signal denoising and the research of the fault feature extraction algorithm.
(2) in order to solve the problem of slow optimization of the single gold segmentation method, the gold segmentation and parabolic interpolation are combined to form the accelerated one dimension search algorithm.
(3) in order to solve the problem that the convergence speed of the single traditional multi-dimensional optimization algorithm is slow and the convergence is easy to fall into the local extremum, the adaptive filtering algorithm based on one dimension search multidimensional search (parameter coarse tuning and parameter tuning) is based on the principle of the frequency domain analysis and the wavelet analysis of the vibration signal, with the maximum kurtosis as the objective function. Two different filtering methods are adopted. Chebyshev bandpass filter and Morlet wavelet filter are used respectively. The design parameters of the Chebyshev bandpass filter and Morlet wavelet filter are optimized by using the hybrid optimization algorithm, namely the spectral kurtosis acceleration one dimension search algorithm - the downhill simplex method, respectively, to filter the noise of the gear box fault vibration signal. Simulation processing.
(4) in order to compare the advantages and disadvantages of different types of hybrid optimization algorithms, the following four optimization algorithms: spectral kurtosis, gold segmentation, downhill simplex, genetic algorithm, and one dimensional search - multidimensional search model are used to optimize the Chebyshev bandpass filter parameters and carry out noise removal experiments.
(5) the particle swarm optimization algorithm is applied to the de-noising of the gear box fault vibration signal on the basis of the previous research, and the hybrid optimization algorithm based on the acceleration one dimension search particle swarm optimization is used to optimize the related parameters of the Chebyshev bandpass filter and Morlet wavelet filter, and the vibration signal of the gear box fault vibration is filtered to imitate the noise. Deal with it.
(6) in order to extract the transient characteristics of the fault pulse which can reflect the change of the working information of the gear box and the future development trend of the fault, the impact response spectrum analysis and the transient analysis method are applied to the fault feature extraction of the gear box gear, which is used to extract the three transient characteristic indexes of the severity of the gear box fault: the impact response spectrum The index SRS and the natural frequency of gear meshing system are n, damping ratio.
【學(xué)位授予單位】：東北林業(yè)大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類號(hào)】：TH132.41;TH165.3
本文編號(hào)：2128205
[Abstract]:As a necessary part of the mechanical equipment to connect and transmit power, the gear box is widely used in the modern industrial equipment such as metal cutting machine, aviation, power system, agricultural machinery, transportation machinery and metallurgical machinery. As a transmission machine, its running state is directly affected by the whole machine transmission. Therefore, it is important to study the fault diagnosis technology and method of the gear box, to detect the gear box and to diagnose the fault, so as to ensure the normal work of the gear box.
The gear box fault diagnosis technology is a multidisciplinary and interdisciplinary technology which can identify the state of the gear box and diagnose the state of the gear box, and diagnose the abnormal state of the fault. The vibration signal of the gear box contains a lot of work state information. It is effective to diagnose the fault by using the vibration signal of the gear box. In the process of fault diagnosis of gear box by vibration signal analysis, filtering de-noising and fault feature extraction are two important problems, which have always been recognized as the key link in fault diagnosis. This paper, from the angle of practical application of the engineering, uses the planetary gearbox of wind turbine and general purpose respectively. The fault vibration signal of industrial gear box is a specific research object, and two key questions, such as spectral kurtosis, gold segmentation, downhill simplex, wavelet analysis, fast Fourier transform, particle swarm optimization, impact response spectrum, transient analysis, etc., are used to denoise and extract fault characteristics of gearbox vibration signal based on mixed optimization idea. A systematic study is carried out to provide some theoretical support for the development and research of gearbox fault diagnosis technology.
This paper focuses on the research of gearbox fault vibration signal optimization filter denoising and fault pulse transient feature extraction algorithm.
(1) the basic principle and application range of the common analysis method of the vibration signal of the gear box are analyzed. It provides a certain theoretical support for the subsequent vibration signal denoising and the research of the fault feature extraction algorithm.
(2) in order to solve the problem of slow optimization of the single gold segmentation method, the gold segmentation and parabolic interpolation are combined to form the accelerated one dimension search algorithm.
(3) in order to solve the problem that the convergence speed of the single traditional multi-dimensional optimization algorithm is slow and the convergence is easy to fall into the local extremum, the adaptive filtering algorithm based on one dimension search multidimensional search (parameter coarse tuning and parameter tuning) is based on the principle of the frequency domain analysis and the wavelet analysis of the vibration signal, with the maximum kurtosis as the objective function. Two different filtering methods are adopted. Chebyshev bandpass filter and Morlet wavelet filter are used respectively. The design parameters of the Chebyshev bandpass filter and Morlet wavelet filter are optimized by using the hybrid optimization algorithm, namely the spectral kurtosis acceleration one dimension search algorithm - the downhill simplex method, respectively, to filter the noise of the gear box fault vibration signal. Simulation processing.
(4) in order to compare the advantages and disadvantages of different types of hybrid optimization algorithms, the following four optimization algorithms: spectral kurtosis, gold segmentation, downhill simplex, genetic algorithm, and one dimensional search - multidimensional search model are used to optimize the Chebyshev bandpass filter parameters and carry out noise removal experiments.
(5) the particle swarm optimization algorithm is applied to the de-noising of the gear box fault vibration signal on the basis of the previous research, and the hybrid optimization algorithm based on the acceleration one dimension search particle swarm optimization is used to optimize the related parameters of the Chebyshev bandpass filter and Morlet wavelet filter, and the vibration signal of the gear box fault vibration is filtered to imitate the noise. Deal with it.
(6) in order to extract the transient characteristics of the fault pulse which can reflect the change of the working information of the gear box and the future development trend of the fault, the impact response spectrum analysis and the transient analysis method are applied to the fault feature extraction of the gear box gear, which is used to extract the three transient characteristic indexes of the severity of the gear box fault: the impact response spectrum The index SRS and the natural frequency of gear meshing system are n, damping ratio.
【學(xué)位授予單位】：東北林業(yè)大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類號(hào)】：TH132.41;TH165.3
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