微弱信号检测及机械故障诊断系统研究
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摘要
在机械设备故障诊断中,信号处理算法起着至关重要的作用,如何从含有噪声的混合信号中尽可能还原真实信号、如何从背景噪声中检测微弱故障特征信号等都是需要重点研究的课题。故障诊断系统完成信号采集、信号分析等任务,为设备维护人员作出诊断决策提供直接的依据。所以,故障诊断系统的发展直接影响到故障诊断行业的发展。本论文以机电设备为对象,对滤波、微弱信号检测以及故障诊断系统进行了研究。
对于被色噪声污染的信号,如果产生色噪声的源头是白噪声,并且白噪声是可以测量的,此时在不知道从白噪声转变到色噪声的这个非线性中间环节的情况下,应用ANFIS可以对色噪声进行逼近,从而达到滤波的目的。EMD可以将混合信号分解为一系列的IMF分量,从而将非平稳信号分解为近似平稳信号。将含噪信号先用EMD进行处理,再对IMF分量所含的色噪声应用ANFIS实现非线性逼近,仿真结果表明,这种结合可以提高滤波的精度。而且由于EMD也是一种独立于信号特性的自适应方法,所以不会影响滤波的整体自适应性。
非均匀采样可以突破采样定理的限制,但是由于采样时刻的伪随机性,幅值低于强信号幅值10%的弱信号的频率在非均匀采样的频谱中不能被识别出来。利用独立分量分析对信号幅值不敏感而只对信号是否正交敏感的特性,在非均匀采样的频谱识别中引入FastICA算法,通过构造虚拟通道信号的方法,成功的在非均匀采样的频谱中提取出了弱信号的频率。
针对非均匀采样的缺点,将一种新的从均匀分布白噪声中提取弱正弦信号的算法引入,在非均匀采样的频谱中成功检测出了弱信号的频率。
开发了两套便携式故障诊断系统,一个基于片上系统和实时操作系统,具有较高的性价比;一个基于DSP和FPGA以及LabVIEW,具有接口类型较多、可以实现无线通讯、系统功能在线可重构等特点。
在便携式故障诊断系统中,分别在片上系统和FPGA中,实现了EMD算法和变步长随机共振算法,为这两种算法的工程应用打下了良好的基础。在EMD算法的实现中,采用了“降阶分解”的方法,有效的解决了嵌入式系统存储空间的问题。
Signal processing algorithms are very important to mechanical fault diagnosis. These are important research fields to study how to correct signal from noise-polluted signals, and how to detect weak characteristic signals of mechanical fault from background noise. Fault diagnosis system could be used to acquire signals and analyze signals, and the system could guide maintenance personnel to make decision of fault diagnosis. Thus the development of fault diagnosis system directly affects fault diagnosis field. Aiming at the electric-mechanical equipments, this dissertation focuses on the research on filtering, weak feature extraction and fault diagnosis system.
If useful signal is contaminated by colored noise which is formed from white noise, and the white noise could be measured, using Adaptive Neuro-Fuzzy Inference System (ANFIS) can approach that colored noise through nonlinear training. Empirical Mode Decomposition (EMD) could decompose mixed signal into a series of Intrinsic Mode Functions (IMFs), then the non-stationary mixed signal is decomposed into approximate stationary signals. This dissertation introduces a new signal processing method which proved effective in improving filter precision. Firstly, the method using EMD to decompose mixed noise-polluted into IMFs; Secondly, the method using ANFIS to approach colored noise which be contained in each IMF. The filter’s entire adaptability of the new method will not be damaged, because EMD also is an adaptive method which is independent from signal itself.
Non-uniform sampling could break the limit of sampling theorem. But if amplitude of one signal is more than 10% smaller than the other’s when two signals are sampled by non-uniform sampling, the weak signal couldn’t be detected from the mixed signal’s spectrum because of sample time’s pseudo-randomness. Using a character that Independent Component Analysis (ICA) is immune to signal’s amplitude but sensitive to orthogonality, the weak signal’s frequency is detected by FastICA through construct virtual signal.
This dissertation also gives another method to solve the non-uniform sampling’s problem. The method was also used to detected weak sinusoidal signals embedded in a uniformly distributed white noise.
This dissertation introduces two portable fault diagnosis systems, one is based on System on Chip (SoC) and Real Time Operation System (RTOS), and it has higher performance price ratio; the other is based on DSP, FPGA and LabVIEW, it has several interfaces, the ability of wireless communication, and it has on-line reconfigurable software functions.
This dissertation finally realizes EMD and step-changed Stochastic Resonance on SoC and FPGA, respectively. The realization of those two algorithms lays a good foundation for engineering application. During the realization of EMD, new drop order decomposition is introduced to meet the limit of storage space in embedded system.
对于被色噪声污染的信号,如果产生色噪声的源头是白噪声,并且白噪声是可以测量的,此时在不知道从白噪声转变到色噪声的这个非线性中间环节的情况下,应用ANFIS可以对色噪声进行逼近,从而达到滤波的目的。EMD可以将混合信号分解为一系列的IMF分量,从而将非平稳信号分解为近似平稳信号。将含噪信号先用EMD进行处理,再对IMF分量所含的色噪声应用ANFIS实现非线性逼近,仿真结果表明,这种结合可以提高滤波的精度。而且由于EMD也是一种独立于信号特性的自适应方法,所以不会影响滤波的整体自适应性。
非均匀采样可以突破采样定理的限制,但是由于采样时刻的伪随机性,幅值低于强信号幅值10%的弱信号的频率在非均匀采样的频谱中不能被识别出来。利用独立分量分析对信号幅值不敏感而只对信号是否正交敏感的特性,在非均匀采样的频谱识别中引入FastICA算法,通过构造虚拟通道信号的方法,成功的在非均匀采样的频谱中提取出了弱信号的频率。
针对非均匀采样的缺点,将一种新的从均匀分布白噪声中提取弱正弦信号的算法引入,在非均匀采样的频谱中成功检测出了弱信号的频率。
开发了两套便携式故障诊断系统,一个基于片上系统和实时操作系统,具有较高的性价比;一个基于DSP和FPGA以及LabVIEW,具有接口类型较多、可以实现无线通讯、系统功能在线可重构等特点。
在便携式故障诊断系统中,分别在片上系统和FPGA中,实现了EMD算法和变步长随机共振算法,为这两种算法的工程应用打下了良好的基础。在EMD算法的实现中,采用了“降阶分解”的方法,有效的解决了嵌入式系统存储空间的问题。
Signal processing algorithms are very important to mechanical fault diagnosis. These are important research fields to study how to correct signal from noise-polluted signals, and how to detect weak characteristic signals of mechanical fault from background noise. Fault diagnosis system could be used to acquire signals and analyze signals, and the system could guide maintenance personnel to make decision of fault diagnosis. Thus the development of fault diagnosis system directly affects fault diagnosis field. Aiming at the electric-mechanical equipments, this dissertation focuses on the research on filtering, weak feature extraction and fault diagnosis system.
If useful signal is contaminated by colored noise which is formed from white noise, and the white noise could be measured, using Adaptive Neuro-Fuzzy Inference System (ANFIS) can approach that colored noise through nonlinear training. Empirical Mode Decomposition (EMD) could decompose mixed signal into a series of Intrinsic Mode Functions (IMFs), then the non-stationary mixed signal is decomposed into approximate stationary signals. This dissertation introduces a new signal processing method which proved effective in improving filter precision. Firstly, the method using EMD to decompose mixed noise-polluted into IMFs; Secondly, the method using ANFIS to approach colored noise which be contained in each IMF. The filter’s entire adaptability of the new method will not be damaged, because EMD also is an adaptive method which is independent from signal itself.
Non-uniform sampling could break the limit of sampling theorem. But if amplitude of one signal is more than 10% smaller than the other’s when two signals are sampled by non-uniform sampling, the weak signal couldn’t be detected from the mixed signal’s spectrum because of sample time’s pseudo-randomness. Using a character that Independent Component Analysis (ICA) is immune to signal’s amplitude but sensitive to orthogonality, the weak signal’s frequency is detected by FastICA through construct virtual signal.
This dissertation also gives another method to solve the non-uniform sampling’s problem. The method was also used to detected weak sinusoidal signals embedded in a uniformly distributed white noise.
This dissertation introduces two portable fault diagnosis systems, one is based on System on Chip (SoC) and Real Time Operation System (RTOS), and it has higher performance price ratio; the other is based on DSP, FPGA and LabVIEW, it has several interfaces, the ability of wireless communication, and it has on-line reconfigurable software functions.
This dissertation finally realizes EMD and step-changed Stochastic Resonance on SoC and FPGA, respectively. The realization of those two algorithms lays a good foundation for engineering application. During the realization of EMD, new drop order decomposition is introduced to meet the limit of storage space in embedded system.
引文
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