基于分段欢稳态随机共振模型的微弱信号检测方法的研究
发布时间：2018-08-13 09:00
【摘要】：本论文研究的内容得到了国家自然科学基金“机械故障诊断中基于非线性理论的微弱信号检测与处理技术研究（50875070）”项目的资助。论文在研究随机共振理论的基础上，设计出了参数可调的分段随机共振系统。通过对该系统进行建模、仿真、计算和电路设计，验证了该系统对淹没在强噪声背景下的中低频微弱信号检测的有效性。以及基于DSP技术设计出一种可实现自动调节参数的自适应分段双稳态随机共振硬件电路。 全文主要内容如下： 第1章介绍了随机共振理论在国内外发展动态和研究现状，提出了本课题需要解决的问题，确定本课题主要研究内容、方法及其创新点。 第2章概括性地介绍了随机共振基本理论，分析了具有代表性的随机共振系统的动态特性、产生随机共振现象的原因和条件以及利用随机共振原理的测量方法。 第3章针对经典的连续双稳态随机共振模型只适用于小参数条件，而对于工程中常见的中低频微弱信号的测量容易出现饱和而难以测量的问题，提出了一种分段双稳态随机共振模型，对该系统进行了理论分析、频带检测及信噪比SNR（signal-to-noise ratio）论证，并进行了验证性的计算和仿真。可以看到该模型对噪声和频率变化具有更好的适应性。 第4章主要针对机械故障中常见的中低频微弱信号检测，，讨论了基于分段双稳态随机共振模型的调制随机共振方法并对该方法进行了验证性的计算和仿真。 第5章介绍了自适应随机共振理论的相关概念，然后结合自适应参数调节原理设计出基于DSP技术的自适应调制随机共振系统。通过对DSP芯片特性的综合分析，设计出了基于TMS320F2812芯片的自适应调制随机共振系统的硬件电路和软件系统。 第6章对比本文所设计的分段双稳态随机共振检测技术与其它一些经典微弱信号检测技术，分析了本文所设计的检测系统的优势与不足，为进一步的研究作了一些展望。 本文的创新之处在于针对工程中常见的中低频率的微弱信号难以测量的问题，提出了一种分段双稳态随机共振模型，并进行了相关的理论分析和仿真试验。该模型改善了绝热近似理论下随机共振只能满足小参数下测试的条件，应用该模型和基于调制随机共振的跟踪扫频方法相结合，可以提高检测效率，有效地从强噪声背景中提取工程实际中的中低频率微弱信号，从而达到了机械故障信号的检测。同时本文对该模型做了验证性的计算、编程仿真和电路设计，并在此基础上探讨了基于DSP技术TMS320F2812芯片的自适应检测。
[Abstract]:This paper is supported by the National Natural Science Foundation of China "Research on weak signal Detection and processing Technology based on nonlinear Theory in Mechanical Fault diagnosis (50875070)". Based on the study of stochastic resonance theory, a piecewise stochastic resonance system with adjustable parameters is designed. Through modeling, simulation, calculation and circuit design of the system, the effectiveness of the system for detecting the weak and middle frequency signals submerged in the background of strong noise is verified. An adaptive bistable stochastic resonance hardware circuit is designed based on DSP technology. The main contents of this paper are as follows: in Chapter 1, the development and research status of stochastic resonance theory at home and abroad are introduced, the problems to be solved are put forward, and the main research contents, methods and innovations of this subject are determined. In chapter 2, the basic theory of stochastic resonance is introduced, the dynamic characteristics of the representative stochastic resonance system, the causes and conditions of the stochastic resonance phenomenon and the measurement method using the stochastic resonance principle are analyzed. In chapter 3, the classical continuous bistable stochastic resonance model is only suitable for small parameter conditions, but the measurement of weak signals in middle and low frequency is easy to be saturated and difficult to measure. A piecewise bistable stochastic resonance model is proposed. The theoretical analysis, frequency band detection and SNR (signal-to-noise ratio) demonstration of signal-to-noise ratio (SNR) of the system are carried out, and the verifiability calculation and simulation are carried out. It can be seen that the model has better adaptability to noise and frequency change. In chapter 4, the modulation stochastic resonance (MSRM) method based on piecewise bistable stochastic resonance model is discussed for the detection of weak signals of middle and low frequency, which is common in mechanical faults, and the method is verified by calculation and simulation. In chapter 5, the concepts of adaptive stochastic resonance theory are introduced, and then the adaptive modulation stochastic resonance system based on DSP technology is designed based on the adaptive parameter regulation principle. By synthetically analyzing the characteristics of DSP chip, the hardware circuit and software system of adaptive modulation stochastic resonance system based on TMS320F2812 chip are designed. Chapter 6 compares the segmented bistable stochastic resonance detection technique and some other classical weak signal detection techniques, analyzes the advantages and disadvantages of the detection system designed in this paper, and makes some prospects for further research. The innovation of this paper is that a piecewise bistable stochastic resonance model is proposed to solve the problem that weak signals at low and medium frequencies are difficult to measure in engineering, and relevant theoretical analysis and simulation experiments are carried out. The model improves the condition that stochastic resonance can only satisfy the test condition under small parameters in adiabatic approximation theory. The detection efficiency can be improved by combining the model with the tracking frequency sweep method based on modulation stochastic resonance. The weak signals with low and medium frequencies are extracted from the background of strong noise effectively, and the detection of mechanical fault signals is achieved. At the same time, this paper has done the verification calculation, the programming simulation and the circuit design to this model, and has discussed the adaptive detection based on the DSP technology TMS320F2812 chip.
【学位授予单位】：杭州电子科技大学
【学位级别】：硕士
【学位授予年份】：2012
【分类号】：TH165.3
本文编号：2180504
[Abstract]:This paper is supported by the National Natural Science Foundation of China "Research on weak signal Detection and processing Technology based on nonlinear Theory in Mechanical Fault diagnosis (50875070)". Based on the study of stochastic resonance theory, a piecewise stochastic resonance system with adjustable parameters is designed. Through modeling, simulation, calculation and circuit design of the system, the effectiveness of the system for detecting the weak and middle frequency signals submerged in the background of strong noise is verified. An adaptive bistable stochastic resonance hardware circuit is designed based on DSP technology. The main contents of this paper are as follows: in Chapter 1, the development and research status of stochastic resonance theory at home and abroad are introduced, the problems to be solved are put forward, and the main research contents, methods and innovations of this subject are determined. In chapter 2, the basic theory of stochastic resonance is introduced, the dynamic characteristics of the representative stochastic resonance system, the causes and conditions of the stochastic resonance phenomenon and the measurement method using the stochastic resonance principle are analyzed. In chapter 3, the classical continuous bistable stochastic resonance model is only suitable for small parameter conditions, but the measurement of weak signals in middle and low frequency is easy to be saturated and difficult to measure. A piecewise bistable stochastic resonance model is proposed. The theoretical analysis, frequency band detection and SNR (signal-to-noise ratio) demonstration of signal-to-noise ratio (SNR) of the system are carried out, and the verifiability calculation and simulation are carried out. It can be seen that the model has better adaptability to noise and frequency change. In chapter 4, the modulation stochastic resonance (MSRM) method based on piecewise bistable stochastic resonance model is discussed for the detection of weak signals of middle and low frequency, which is common in mechanical faults, and the method is verified by calculation and simulation. In chapter 5, the concepts of adaptive stochastic resonance theory are introduced, and then the adaptive modulation stochastic resonance system based on DSP technology is designed based on the adaptive parameter regulation principle. By synthetically analyzing the characteristics of DSP chip, the hardware circuit and software system of adaptive modulation stochastic resonance system based on TMS320F2812 chip are designed. Chapter 6 compares the segmented bistable stochastic resonance detection technique and some other classical weak signal detection techniques, analyzes the advantages and disadvantages of the detection system designed in this paper, and makes some prospects for further research. The innovation of this paper is that a piecewise bistable stochastic resonance model is proposed to solve the problem that weak signals at low and medium frequencies are difficult to measure in engineering, and relevant theoretical analysis and simulation experiments are carried out. The model improves the condition that stochastic resonance can only satisfy the test condition under small parameters in adiabatic approximation theory. The detection efficiency can be improved by combining the model with the tracking frequency sweep method based on modulation stochastic resonance. The weak signals with low and medium frequencies are extracted from the background of strong noise effectively, and the detection of mechanical fault signals is achieved. At the same time, this paper has done the verification calculation, the programming simulation and the circuit design to this model, and has discussed the adaptive detection based on the DSP technology TMS320F2812 chip.
【学位授予单位】：杭州电子科技大学
【学位级别】：硕士
【学位授予年份】：2012
【分类号】：TH165.3
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