發(fā)布時間：2018-05-28 10:47

  本文選題：故障診斷 + 能量耗損　； 參考：《華南理工大學》2013年博士論文

【摘要】：目前，機械設(shè)備故障診斷方法主要有振動分析和油液分析等方法。無論是基于振動分析的設(shè)備故障診斷，還是基于油液分析（磨損信息）的設(shè)備故障診斷，它們有一個共同點，就是設(shè)備故障時都會伴隨有系統(tǒng)能量耗損變化。針對機械設(shè)備在發(fā)生故障時都伴隨能量耗損變化這一特征，開創(chuàng)性地提出了一種基于能量耗損的機械設(shè)備故障診斷理論與方法。這種方法通過獲取摩擦學系統(tǒng)的能量耗損信息，建立能量耗損信息的相關(guān)性，提取能量耗損信息特征并進行故障模式識別，建立基于能量耗損的故障規(guī)則。 首先，論文提出基于能量耗損的機械設(shè)備故障診斷新方法。研究摩擦學系統(tǒng)的能量耗損理論與摩擦過程的能量耗損信息流，研究輸入能量耗損信息特征、磨損信息特征、振動信號特征。提出了能量耗損信息的相對標度和能量耗損信息的累計相對標度，輸入能量耗損采用功率或者油耗等特征量，磨損能量耗損采用光譜元素指標；振動耗能采用振動速度信號時域均方值與振動加速度的峭度等指標，建立能量耗損信息的特征集。研究能量耗損信息的相關(guān)性，建立基于能量耗損的機械設(shè)備相關(guān)性模型。 其次，論證了基于能量耗損的機械設(shè)備故障診斷方法是可行的。齒輪模擬故障實驗研究表明，齒輪發(fā)生點蝕、剝落、斷齒等不同故障時，輸入的功率耗損波動特性不同；磨損能耗信息磨損量和嚴重磨損指數(shù)表明故障的劇烈程度，振動時域信號通過小波包分析提取了各頻帶的能量分布。齒輪疲勞故障診斷相關(guān)性研究表明，輸入功率耗損與磨損特征信息與振動特征信息變化規(guī)律具有一致性，具有較強的相關(guān)性。柴油機活塞缸套疲勞性實驗研究表明，瞬時油耗隨著活塞磨損故障程度的增加而增加，磨損能耗信息磨損量和嚴重磨損量指數(shù)一直遞增。振動能量的變化具有隨故障程度增加而增大的趨勢，三者能量耗損信息表現(xiàn)規(guī)律具有一致性。從而驗證本文提出的基于能量耗損的機械設(shè)備故障診斷理論和方法是可行的。 再次，提出一種基于流形學習算法與支持向量機結(jié)合的故障模式識別方法。研究局部線性嵌入LLE、局部切空間排列算法LTSA流形學習算法，并對算法進行了改進。采用流形學習算法對齒輪和柴油機能量耗損數(shù)據(jù)降維，然后采用多類分類器進行分類，通過分類識別率來判斷模式識別的效果。仿真和實驗表明流形學習是一種有效的非線性特征提取方法，改進的算法使鄰域較好保持了曲面數(shù)據(jù)的原有對應(yīng)關(guān)系，使得投影后的特征保持了樣本間的差異信息和同類樣本之間的相似信息。改進的流形學習算法的識別率得到了提高。流形學習與支持向量機結(jié)合的模式識別方法是一種有效的特征提取和模式識別方法。 然后，建立了能量耗損信息的故障診斷規(guī)則。研究了粗糙集與模糊理論的故障規(guī)則提取方法，利用粗糙集理論中的不可分辨關(guān)系把齒輪能量耗損信息的故障論域劃分等價類，生成粗糙集的上近似關(guān)系和下近似關(guān)系，通過屬性重要性分析和屬性約簡導(dǎo)出故障決策知識和故障分類規(guī)則，建立了齒輪能量耗損信息的故障規(guī)則；采用模糊理論與神經(jīng)網(wǎng)絡(luò)結(jié)合的方法，應(yīng)用自適應(yīng)模糊控制規(guī)則提取方法，輸入柴油機能量耗損信息的模糊量，能自動對模糊控制規(guī)則進行修改，建立了能量耗損信息的柴油機活塞磨損模糊的故障規(guī)則。 最后，，研究了能量耗損信息監(jiān)測與診斷系統(tǒng)的基本結(jié)構(gòu)。設(shè)計了基于虛擬儀器技術(shù)的能量耗損信息監(jiān)測與診斷系統(tǒng)結(jié)構(gòu)，使用LabVIEW虛擬化圖形化用圖標代替文本創(chuàng)建應(yīng)用程序的計算機編程語言，開發(fā)了能量耗損信息在線故障診斷監(jiān)測系統(tǒng)，包括數(shù)據(jù)采集系統(tǒng)，信號分析系統(tǒng)，實現(xiàn)了能量耗損信息的采集與分析，初步建立了能量耗損信息的監(jiān)測與診斷系統(tǒng)。
[Abstract]:At present , the fault diagnosis method of mechanical equipment is mainly characterized by vibration analysis and oil - liquid analysis . Whether it is based on vibration analysis equipment fault diagnosis or oil - liquid analysis ( wear information ) equipment fault diagnosis , they have a common point , which is the system energy consumption change when equipment failure occurs .

First , a new method for fault diagnosis of mechanical equipment based on energy consumption is put forward . The energy dissipation theory and energy loss information flow of the friction process are studied , and the characteristics of the input energy loss information , the characteristics of the wear information and the characteristics of the vibration signal are studied .
In this paper , the characteristic set of energy loss information is established by using the time - domain mean square value of the vibration velocity signal and the frequency of the vibration acceleration , and the correlation of energy consumption information is studied , and the correlation model of the mechanical equipment based on energy consumption is established .

Secondly , it is proved that the fault diagnosis method based on energy consumption is feasible . The experimental study of gear simulation shows that the power consumption fluctuation is different when the gear has different faults such as pitting , spalling , tooth breaking , etc .
The wear and wear index of wear energy consumption information indicates the severity of failure , and the vibration time domain signal is used to extract the energy distribution of each frequency band by wavelet packet analysis . The research on the correlation between input power consumption and wear characteristic information is consistent with the change rule of vibration characteristic information .

This paper presents a fault pattern recognition method based on manifold learning algorithm combined with support vector machine . The local linear embedding LLE , local tangent space arrangement algorithm LTSA manifold learning algorithm is studied , and the algorithm is improved . The method of manifold learning is used to classify the energy consumption data of gears and diesel engines , and the recognition rate of pattern recognition is improved by classification recognition rate . Simulation and experiment show that manifold learning is an effective nonlinear feature extraction method . The improved algorithm makes the recognition rate of the improved manifold learning algorithm improved . The pattern recognition method combined with the manifold learning and support vector machine is an effective feature extraction and pattern recognition method .

Then , a fault diagnosis rule for energy loss information is established . The fault rule extraction method based on rough set and fuzzy theory is studied , and the fault of gear energy consumption information is divided into equivalent classes by the non - discrimination relationship in rough set theory . The fault decision knowledge and fault classification rule are derived by attribute importance analysis and attribute reduction , and fault rules of gear energy consumption information are established .
Based on fuzzy theory and neural network , an adaptive fuzzy control rule extracting method is applied to input the fuzzy quantity of energy consumption information of diesel engine , and the fuzzy control rule can be modified automatically , and the fuzzy fault rule of piston wear of diesel engine with energy loss information is established .

In the end , the basic structure of the energy consumption information monitoring and diagnosis system is studied . The system structure of energy consumption information monitoring and diagnosis based on virtual instrument technology is designed . The computer programming language of energy consumption loss information online fault diagnosis is developed by using LabVIEW virtualization graphical icon instead of the computer programming language of the text creating application program . The system includes data acquisition system and signal analysis system , and the acquisition and analysis of energy consumption information is realized , and the monitoring and diagnosis system of energy consumption information is established .
【學位授予單位】：華南理工大學
【學位級別】：博士
【學位授予年份】：2013
【分類號】：TH165.3

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