本文選題：齒輪故障診斷 + 局部均值分解��； 參考：《湖南大學(xué)》2012年碩士論文

【摘要】：齒輪是機械設(shè)備中一種常見的通用零部件，它通常負責連接和傳遞動力。由于工作環(huán)境惡劣等因素，齒輪極易發(fā)生故障而直接影響到相關(guān)機械設(shè)備的運行狀態(tài)，因此，對齒輪進行故障監(jiān)測和診斷具有重要意義。 齒輪故障診斷的核心和關(guān)鍵就是提取齒輪故障特征，對齒輪的故障位置和損壞程度作出判斷，而齒輪振動信號與齒輪工作狀態(tài)密切相關(guān)，其中往往包含大量的故障信息。本文正是針對齒輪故障振動信號的非平穩(wěn)性及其多為多分量的調(diào)制信號之和等特性，將局部均值分解（Local mean decomposition，簡稱LMD）引入齒輪故障診斷，進一步將LMD方法與能量算子解調(diào)、循環(huán)頻率解調(diào)、譜峭度方法相結(jié)合應(yīng)用于齒輪故障診斷，并在LMD的基礎(chǔ)上提出了局部特征尺度分解（Local characteristic-scale decomposition，簡稱LCD）方法。本文的主要研究內(nèi)容如下： 1、針對齒輪故障振動信號大多數(shù)為若干的調(diào)幅調(diào)頻信號之和這一特點，將LMD方法應(yīng)用于齒輪故障診斷中。LMD是一種新的自適應(yīng)時頻分析方法，它能將復(fù)雜的非平穩(wěn)多分量調(diào)幅調(diào)頻信號從高頻到低頻分解為有限個單分量的調(diào)幅調(diào)頻信號之和，因此非常適合齒輪故障信號分析。通過仿真信號對比分析和實際的齒輪故障信號分析可知，LMD方法具有更好的自適應(yīng)性和時頻聚集性，能夠更精確地獲得信號的瞬時頻率和瞬時幅值，能夠得到更加清晰和完整的時頻分布。 2、針對齒輪故障信號的調(diào)制特性，且從調(diào)制信息中通常能提取出故障特征這一特點，提出了基于LMD的能量算子解調(diào)和循環(huán)頻率解調(diào)齒輪故障診斷方法。該方法先利用LMD將信號分解為一系列的乘積函數(shù)（Product function，簡稱PF），然后分別利用能量算子解調(diào)和循環(huán)頻率解調(diào)獲得相關(guān)PF分量的幅值調(diào)制信息和相位調(diào)制信息，從而提取出故障特征進行故障診斷。將該方法運用于齒輪振動信號故障診斷中，并和LMD直接法進行對比分析，證明了該方法的優(yōu)越性。 3、針對齒輪故障振動信號在解調(diào)分析之前一般要通過濾波來確定包含故障信息的最佳頻段，而濾波參數(shù)又無法準確確定，只能依靠歷史數(shù)據(jù)和人工經(jīng)驗這一問題，，提出了基于LMD的譜峭度齒輪故障診斷方法。該方法在LMD時頻分析的基礎(chǔ)上獲得信號峭度圖，根據(jù)最大峭度原則在峭度圖上選取最佳濾波頻段從而獲得最佳濾波參數(shù)對原始信號進行濾波，再對濾波后的信號進行包絡(luò)解調(diào)分析提取出故障特征。實驗結(jié)果證明，該方法具有有效性。 4、針對LMD計算量大，運算速度慢，不適于在線監(jiān)測的特點，采用基于極值點的局部特征尺度參數(shù)，定義了另一種瞬時頻率具有物理意義的單分量信號——內(nèi)稟尺度分量（Intrinsic scale component，簡稱ISC），在此基礎(chǔ)上提出了LCD方法。LCD方法也是一種自適應(yīng)的分解方法，能夠?qū)⒍喾至康男盘柗纸鉃閱畏至啃盘栔�，通過分別與EMD和LMD方法對比證明其具有不會產(chǎn)生包絡(luò)誤差，且運算速度較快等優(yōu)點。同時，本文還將LCD方法成功地運用于齒輪箱故障診斷。
[Abstract]:Gear is a common common component in mechanical equipment. It is usually responsible for connecting and transmitting power. Because of bad working environment and other factors, the gear is easily malfunction and affects the operating state of the related machinery directly. Therefore, it is of great significance to monitor and diagnose the gear fault.
The core and key of gear fault diagnosis is to extract the feature of gear fault and judge the fault position and damage degree of the gear, and the gear vibration signal is closely related to the working state of the gear, which often contains a large number of fault information. This paper is aimed at the non stationarity of the vibration signal of the gear fault and its multi component modulation. The Local mean decomposition (LMD) is introduced into the gear fault diagnosis, and the LMD method is combined with the energy operator demodulation, the cyclic frequency demodulation, and the spectral kurtosis method is applied to the gear fault diagnosis, and the local feature scale decomposition (Local characteristic-sc) is put forward on the basis of LMD. Ale decomposition, referred to as LCD). The main contents of this paper are as follows:
1, in view of the characteristic that most of the gear fault vibration signals are the sum of amplitude modulation and frequency modulation signals, the LMD method is applied to the gear fault diagnosis..LMD is a new adaptive time-frequency analysis method. It can decompose the complex nonstationary multicomponent FM signal from high frequency to low frequency into a limited single component FM signal. It is very suitable for the analysis of the gear fault signal. Through the analysis of the simulation signal contrast analysis and the actual gear fault signal analysis, it is known that the LMD method has better adaptability and time frequency aggregation. It can get the instantaneous frequency and instantaneous amplitude of the signal more accurately, and can get a clearer and complete time frequency distribution.
2, in view of the modulation characteristics of the gear fault signal and the feature that usually can extract the fault feature from the modulation information, a method of gear fault diagnosis based on LMD's Energy Operator Demodulation and cyclic frequency demodulation is proposed. The method first uses LMD to decompose the signal into a series of product functions (Product function, for short, PF), and then profit separately. The amplitude modulation information and phase modulation information of the related PF components are obtained by using the Energy Operator Demodulation and the cyclic frequency demodulation. The fault features are extracted and the fault diagnosis is extracted. The method is applied to the fault diagnosis of the gear vibration signal and is compared with the LMD direct method, and the superiority of the method is proved.
3, before the demodulation analysis of the gear fault vibration signal, the best frequency band which contains the fault information is usually determined by filtering, and the filter parameters can not be accurately determined. It can only rely on the problem of historical data and artificial experience. The method of diagnosis of the spectral kurtosis of gear barrier based on LMD is put forward. This method is based on the basis of LMD time-frequency analysis. On the basis of the maximum kurtosis principle, the best filter band is selected on the kurtosis map to obtain the best filter parameters to filter the original signal, and then the signal is extracted and analyzed by the envelope demodulation analysis. The experimental results show that the method is effective.
4, in view of the characteristics of LMD with large computation, slow computing speed and unsuitable for on-line monitoring, a single component signal, an intrinsic scale component (Intrinsic scale component, simply called ISC), is defined by using local feature scale parameters based on extreme points, and based on this, a LCD method.LCD method is also proposed. An adaptive decomposition method can be used to decompose the multicomponent signals into the sum of the single component signals. By comparing with the EMD and LMD methods, it is proved that it has the advantages of no envelope error and faster operation speed. At the same time, this paper also successfully applied the LCD method to the gear box fault diagnosis.
【學(xué)位授予單位】：湖南大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：TH165.3

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