多尺度分析方法在旋转机械状态监测中的应用研究
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摘要
多尺度思想是人们在对世界认识的不断加深的过程中逐渐产生和发展的,与单尺度的观点相比,多尺度方法更贴近人们认识事物的习惯,也更加符合事物的本质。本论文立足多尺度分析方法在旋转机械状态监测与故障诊断中的应用,以多尺度分析方法为主要研究内容,深入研究了四种典型的多尺度方法在旋转机械状态监测中的原理、方法和效果,并最后将它们引入高速列车轴承轨边声学诊断系统的研究中。研究包括：
根据旋转机械状态信号包含有特征频率及高频谐振的特征,将小波方差的能量特性引入到旋转机械状态信号的频谱特征提取中,提出了旋转机械多尺度小波方差拟合特征的分析方法。即根据不同工作状态下旋转机械状态信号的“小波方差对数—尺度”最小二乘法拟合直线呈现出的与设备状态相关的特点,通过拟合直线斜率对状态信号进行分类。通过滚动轴承三状态振动测试和齿轮箱三状态测试两个独立实验对该方法进行了验证和分析,证明小波方差拟合特征作为一种新的信号多尺度分析参数,可以有效的反映旋转机械的不同故障和同一故障的不同故障阶段,同时计算简便,易于计算机实现实时监测。
针对状态监测中经常要遇到的设备状态突变现象,将信息熵理论与小波变换结合,研究了多尺度小波熵对设备状态信号突变的检测能力,提出基于小波熵的旋转机械状态监测方法。该方法通过特征频率的选择,消除低信噪比对信号整体熵的影响,利用小波熵对系统参数突变的敏感性,实现对设备故障的预警。齿轮箱全寿命实验表明小波时间熵和小波奇异熵可以有效的检测齿轮箱状态突变情况。
齿轮发生磨损时,轮对故障点周期性啮合时将会产生附加振动而激励齿轮共振,这种共振信号具有较强的尺度行为。根据该特征研究了标度分析在旋转机械状态监测中的应用。并以去趋势波动分析为基础,将去趋势波动分析中子区间概念细化到每一个采样点,通过权重函数调节计算不同尺度下的滑动平均和二阶中心距,可以得到每一个采样点的波动函数,发展了一种新的局域标度指数方法。该方法比去趋势波动分析更加重视信号局部的精细结构。齿轮箱磨损试验清晰的表明局域标度指数可以滤除其他频率成分,有效直观的检测出故障齿啮合时信号的微弱故障频率成分。
分析了单重分形刻画信号的特征时只能从整体上反映信号的不规则性,缺乏对局部奇异性刻画的缺点,研究了多重分形对旋转机械状态信号局部尺度行为的表征能力。并将去趋势多重分形方法引入到旋转机械状态监测中,并定义了多重分形谱的形态学特征,以此定量提取了信号的状态参数进行故障分类。齿轮箱磨损试验的去趋势多重分形分析证明了该方法具有良好的故障分类效果。
在高速铁路快速发展的背景下,分析了我国列车轴承轨边声学诊断系统的研究现状,设计制造了高速列车轴承的声学测试平台,并利用本文提出的多尺度分析方法,对多种工作状态下的高速列车轴承声学信号进行了深入的分析,进一步确认了多尺度方法在旋转机械故障诊断中的有效性,也给出了高速列车轴承声学诊断的一个可行的研究方向。
The origin and development of multi-scale analysis is on the basis of the people's deepening understanding of the world. Multi-scale thought is much more abutted the natures of things and much more in line with the habit of people exploring new things than the view of single scale. On the basis of multi-scale analysis, rotating machinery condition monitoring and fault diagnosis is deeply studied on four kinds of multi-scale methods, and finally these multi-scale analysis methords are introduced to the high-speed train bearing acoustics diagnostic systems.
According to the characteristic frequency and high-frequency resonance frequency that typical rotating machinery state signals contained, the wavelet coefficient variance feature is proposed with wavelet coefficient variance's energy property. The wavelet-based multi-scale slope features which can identify the different working conditions of rotating machinery are estimated from the slope of logarithmic variances after the DWT analysis of the signals. The effectiveness of the proposed feature was verified by two experiments on bearing defect identification and gear wear diagnosis. Experimental results show that the wavelet-based multiscale slope features have the merits of high accuracy and stability in classifying different conditions of both bearings and gearbox, and thus are valuable for machinery fault diagnosis.
The status mutations phenomenon often encountered in the equipment condition monitoring, and the multi-scale wavelet entropy analysis is studied to solve this problem. This method combines information entropy theory and wavelet transform, and eliminate the noise in low SNR signals by selecting the characteristic scale. Experimental results show that the wavelet time entropy, the wavelet singular entropy and the wavelet scale entropy are sensitive to the early mutation of gearbox vibration signal and can make warning of equipment failure.
During the gearbox experiences wear process, there is a structure resonance phenomenon when the failure point of the gear periodic meshing. This resonance signal has a strong scale behavior and there is a strong correlation between adjacent signal points. Based on detrended fluctuation analysis, local scaling exponent method is proposed var refinement the concept of subspace to each sampling point, and adjusting the weighting function to calculate the moving average and second-order center distance in different scales. The new local scaling exponent method pays more attention to the fine local structure of the signal than the detrended fluctuation analysis. A gearbox wear test verified the local scaling exponent analysis can filter out other frequency components or noise, and effectively monitor the weak fault component.
The lack of local singularity characterization make the single fractal only can reflect the overall analysis of the signals, when multi fractal analysis can characterize the signal local-scale behavior of rotating machinery more effectively. The detrended trend multifractal method is introduced to the condition monitoring of rotating machinery, and the morphological characteristics of the multifractal spectrum are proposed for quantitative extraction of the state parameters of the signal for fault classification. A gearbox wear test verifies the multi fractal analysis has a good effect in condition classification.
In the context of the rapid development of high-speed railway, the Trackside Acoustic Diagnostic System (TADS) receives much more attention. A test platform is designed and manufactured for high-speed train bearing acoustic diagnosis and take advantage of the proposed multi-scale analysis method by this paper for high speed train bearing acoustic diagnosis. The analysis results further confirm the validity of multi-scale method and suggest a feasible research direction for acoustic diagnostics of high-speed train bearings.
根据旋转机械状态信号包含有特征频率及高频谐振的特征,将小波方差的能量特性引入到旋转机械状态信号的频谱特征提取中,提出了旋转机械多尺度小波方差拟合特征的分析方法。即根据不同工作状态下旋转机械状态信号的“小波方差对数—尺度”最小二乘法拟合直线呈现出的与设备状态相关的特点,通过拟合直线斜率对状态信号进行分类。通过滚动轴承三状态振动测试和齿轮箱三状态测试两个独立实验对该方法进行了验证和分析,证明小波方差拟合特征作为一种新的信号多尺度分析参数,可以有效的反映旋转机械的不同故障和同一故障的不同故障阶段,同时计算简便,易于计算机实现实时监测。
针对状态监测中经常要遇到的设备状态突变现象,将信息熵理论与小波变换结合,研究了多尺度小波熵对设备状态信号突变的检测能力,提出基于小波熵的旋转机械状态监测方法。该方法通过特征频率的选择,消除低信噪比对信号整体熵的影响,利用小波熵对系统参数突变的敏感性,实现对设备故障的预警。齿轮箱全寿命实验表明小波时间熵和小波奇异熵可以有效的检测齿轮箱状态突变情况。
齿轮发生磨损时,轮对故障点周期性啮合时将会产生附加振动而激励齿轮共振,这种共振信号具有较强的尺度行为。根据该特征研究了标度分析在旋转机械状态监测中的应用。并以去趋势波动分析为基础,将去趋势波动分析中子区间概念细化到每一个采样点,通过权重函数调节计算不同尺度下的滑动平均和二阶中心距,可以得到每一个采样点的波动函数,发展了一种新的局域标度指数方法。该方法比去趋势波动分析更加重视信号局部的精细结构。齿轮箱磨损试验清晰的表明局域标度指数可以滤除其他频率成分,有效直观的检测出故障齿啮合时信号的微弱故障频率成分。
分析了单重分形刻画信号的特征时只能从整体上反映信号的不规则性,缺乏对局部奇异性刻画的缺点,研究了多重分形对旋转机械状态信号局部尺度行为的表征能力。并将去趋势多重分形方法引入到旋转机械状态监测中,并定义了多重分形谱的形态学特征,以此定量提取了信号的状态参数进行故障分类。齿轮箱磨损试验的去趋势多重分形分析证明了该方法具有良好的故障分类效果。
在高速铁路快速发展的背景下,分析了我国列车轴承轨边声学诊断系统的研究现状,设计制造了高速列车轴承的声学测试平台,并利用本文提出的多尺度分析方法,对多种工作状态下的高速列车轴承声学信号进行了深入的分析,进一步确认了多尺度方法在旋转机械故障诊断中的有效性,也给出了高速列车轴承声学诊断的一个可行的研究方向。
The origin and development of multi-scale analysis is on the basis of the people's deepening understanding of the world. Multi-scale thought is much more abutted the natures of things and much more in line with the habit of people exploring new things than the view of single scale. On the basis of multi-scale analysis, rotating machinery condition monitoring and fault diagnosis is deeply studied on four kinds of multi-scale methods, and finally these multi-scale analysis methords are introduced to the high-speed train bearing acoustics diagnostic systems.
According to the characteristic frequency and high-frequency resonance frequency that typical rotating machinery state signals contained, the wavelet coefficient variance feature is proposed with wavelet coefficient variance's energy property. The wavelet-based multi-scale slope features which can identify the different working conditions of rotating machinery are estimated from the slope of logarithmic variances after the DWT analysis of the signals. The effectiveness of the proposed feature was verified by two experiments on bearing defect identification and gear wear diagnosis. Experimental results show that the wavelet-based multiscale slope features have the merits of high accuracy and stability in classifying different conditions of both bearings and gearbox, and thus are valuable for machinery fault diagnosis.
The status mutations phenomenon often encountered in the equipment condition monitoring, and the multi-scale wavelet entropy analysis is studied to solve this problem. This method combines information entropy theory and wavelet transform, and eliminate the noise in low SNR signals by selecting the characteristic scale. Experimental results show that the wavelet time entropy, the wavelet singular entropy and the wavelet scale entropy are sensitive to the early mutation of gearbox vibration signal and can make warning of equipment failure.
During the gearbox experiences wear process, there is a structure resonance phenomenon when the failure point of the gear periodic meshing. This resonance signal has a strong scale behavior and there is a strong correlation between adjacent signal points. Based on detrended fluctuation analysis, local scaling exponent method is proposed var refinement the concept of subspace to each sampling point, and adjusting the weighting function to calculate the moving average and second-order center distance in different scales. The new local scaling exponent method pays more attention to the fine local structure of the signal than the detrended fluctuation analysis. A gearbox wear test verified the local scaling exponent analysis can filter out other frequency components or noise, and effectively monitor the weak fault component.
The lack of local singularity characterization make the single fractal only can reflect the overall analysis of the signals, when multi fractal analysis can characterize the signal local-scale behavior of rotating machinery more effectively. The detrended trend multifractal method is introduced to the condition monitoring of rotating machinery, and the morphological characteristics of the multifractal spectrum are proposed for quantitative extraction of the state parameters of the signal for fault classification. A gearbox wear test verifies the multi fractal analysis has a good effect in condition classification.
In the context of the rapid development of high-speed railway, the Trackside Acoustic Diagnostic System (TADS) receives much more attention. A test platform is designed and manufactured for high-speed train bearing acoustic diagnosis and take advantage of the proposed multi-scale analysis method by this paper for high speed train bearing acoustic diagnosis. The analysis results further confirm the validity of multi-scale method and suggest a feasible research direction for acoustic diagnostics of high-speed train bearings.
引文
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