本文選題：狀態(tài)監(jiān)測 + 軸承��； 參考：《上海交通大學(xué)》2014年博士論文

【摘要】：隨著科學(xué)技術(shù)的進(jìn)步和生產(chǎn)效率的提高，機(jī)械設(shè)備不斷向高速、高精度、重載和高可靠性的方向發(fā)展，設(shè)備的結(jié)構(gòu)也日趨復(fù)雜化。在生產(chǎn)過程中，機(jī)械故障不但影響工作效率，并且可能引起嚴(yán)重的安全問題。由于機(jī)械設(shè)備的性能和狀態(tài)在使用過程中總會隨著持續(xù)運(yùn)行而逐漸惡化，因此開展機(jī)械設(shè)備故障診斷技術(shù)的研究對于維護(hù)設(shè)備安全，提高生產(chǎn)的效率和可靠性具有重要意義。 軸承和齒輪作為機(jī)械設(shè)備的關(guān)鍵零部件之一，其工作狀態(tài)的好壞嚴(yán)重影響著設(shè)備性能的變化。因此對軸承和齒輪的故障診斷和性能退化評估一直是設(shè)備故障診斷的研究重點(diǎn)。本文在分析軸承故障機(jī)理的基礎(chǔ)上，，提出了基于頻帶熵的自適應(yīng)濾波器方法并用于軸承微弱故障的特征提取。由于設(shè)備在工作過程中總會經(jīng)歷由正常到退化到最終失效的過程，如果能夠獲得設(shè)備的實(shí)時(shí)健康信息，對于維護(hù)策略的制定、降低維護(hù)成本和生產(chǎn)損失有著積極的意義。本文利用耦合隱馬爾可夫模型的多通道信息融合能力，深入討論了耦合隱馬爾可夫模型在軸承故障診斷和性能退化評估中的應(yīng)用。主要包括以下幾個方面的內(nèi)容： (1)結(jié)合機(jī)械設(shè)備狀態(tài)監(jiān)測的理論基礎(chǔ)和實(shí)際工程應(yīng)用需求，闡述了論文選題的背景和研究意義。回顧和分析了國內(nèi)外在軸承特征提取、信息融合、故障診斷和性能退化評估與預(yù)測方法的研究熱點(diǎn)和現(xiàn)狀，確立了本文的研究內(nèi)容和技術(shù)框架。 (2)介紹了滾動軸承的結(jié)構(gòu)和運(yùn)動特征，通過軸承的點(diǎn)蝕故障模型說明了軸承故障原理和各個特征頻率計(jì)算方法。利用滾動軸承特征頻率調(diào)制的規(guī)律，結(jié)合振動信號的時(shí)頻分布特點(diǎn)和信息熵理論，提出了一種基于頻帶熵的自適應(yīng)濾波器設(shè)計(jì)方法來提取滾動軸承的微弱故障信號。 (3)介紹了軸承故障診斷中常用的時(shí)域和頻域指標(biāo)以及特征約減算法在故障診斷中的應(yīng)用。給出了一種使用正交基的局部保持投影降維方法，研究了如何利用類內(nèi)類間距離指標(biāo)來優(yōu)化鄰接圖構(gòu)造參數(shù)的選擇。 (4)介紹了馬爾可夫鏈和隱馬爾可夫算法的基本概念和算法，討論了隱馬爾可夫模型的評估問題、解碼問題和學(xué)習(xí)問題及基本算法。通過試驗(yàn)證明了特征約減和隱馬爾可夫模型在軸承故障診斷中的可行性和有效性。 (5)針對單鏈隱馬爾可夫模型在多通道數(shù)據(jù)故障診斷中的局限性，討論了基于LPP的特征層信息融合與隱馬爾可夫模型結(jié)合的診斷方法、基于隱馬爾可夫模型和D-S的決策層信息融合方法以及耦合隱馬爾可夫模型的多通道信息融合方法在軸承故障診斷中的應(yīng)用。研究了耦合隱馬爾可夫模型的概率推導(dǎo)和故障診斷建模算法，利用軸承人工故障試驗(yàn)證明了基于耦合隱馬爾可夫模型的故障診斷能獲得更好的診斷精度。通過與其余特征提取和診斷方法的比較，進(jìn)一步證明了耦合隱馬爾可夫模型的診斷準(zhǔn)確性。 (6)研究了利用耦合隱馬爾可夫模型對多通道監(jiān)測數(shù)據(jù)進(jìn)行性能退化評估建模和性能指標(biāo)計(jì)算的方法。利用性能指標(biāo)給出了自適應(yīng)報(bào)警限的計(jì)算方法。最后對齒輪自然失效試驗(yàn)數(shù)據(jù)、滾動軸承加速疲勞試驗(yàn)數(shù)據(jù)和滾柱軸承自然疲勞試驗(yàn)數(shù)據(jù)的分析驗(yàn)證了耦合隱馬爾可夫模型對完備和非完備數(shù)據(jù)進(jìn)行性能退化評估的有效性，結(jié)果證明了所選的性能指標(biāo)能夠定量的反映出軸承的性能退化程度。
[Abstract]:With the progress of science and technology and the improvement of production efficiency, mechanical equipment is constantly developing towards high speed, high precision, heavy load and high reliability, and the structure of equipment is becoming more and more complex. In the process of production, mechanical failures not only affect the efficiency of the work, but also cause serious safety problems. Because of the performance and state of the mechanical equipment, In the process of use, it will always deteriorate with the continuous operation. Therefore, it is of great significance to carry out the research on the fault diagnosis technology of mechanical equipment to maintain the safety of the equipment and improve the efficiency and reliability of production.
As one of the key parts of the mechanical equipment, bearing and gear have a serious influence on the change of equipment performance. Therefore, the fault diagnosis and performance degradation assessment of bearing and gear are always the focus of research on equipment fault diagnosis. On the basis of analyzing the mechanism of bearing fault, this paper puts forward the self - frequency entropy based on the frequency band. The adaptive filter method is used to extract the characteristics of the weak fault of the bearing. Because the equipment will always experience the process from normal to final failure during the working process, it has positive significance for the maintenance strategy formulation and the reduction of the maintenance cost and the loss of production if the equipment is able to obtain the real-time health information. The multi-channel information fusion ability of Markov model is used to discuss the application of the coupled hidden Markov model in bearing fault diagnosis and performance degradation evaluation.
(1) combining the theoretical basis of state monitoring of mechanical equipment and the requirement of practical engineering application, the background and research significance of the thesis are expounded. The research heat point and current situation of bearing feature extraction, information fusion, fault diagnosis and performance degradation assessment and prediction methods are reviewed and analyzed, and the research content and technical frame of this paper are established. Frame.
(2) the structure and motion characteristics of the rolling bearing are introduced. The principle of bearing fault and the calculation method of each characteristic frequency are explained by the fault model of the pitting corrosion of the bearing. A adaptive filter based on the frequency band entropy is proposed, which is based on the characteristic frequency modulation of the rolling bearing and the time frequency distribution characteristics of the vibration signal and the information entropy theory. The design method is used to extract the weak fault signals of rolling bearings.
(3) the application of time domain and frequency domain index in fault diagnosis of bearing and the application of feature reduction algorithm in fault diagnosis are introduced. A local maintenance projection reduction method using orthogonal basis is given, and how to optimize the selection of construction parameters of adjacency graph by using intra class distance index is studied.
(4) the basic concepts and algorithms of Markov chain and hidden Markov algorithm are introduced. The evaluation of hidden Markov models, decoding problems, learning problems and basic algorithms are discussed. The feasibility and effectiveness of feature reduction and hidden Markov model in bearing fault diagnosis are proved by experiments.
(5) in view of the limitation of single chain hidden Markov model in multi channel data fault diagnosis, the diagnosis method based on LPP based feature layer information fusion and hidden Markov model is discussed. The method of information fusion based on Hidden Markov model and D-S decision layer and multi channel information fusion method of coupled hidden Markov model are discussed. The application of the bearing fault diagnosis is studied. The probability deduction and fault diagnosis modeling algorithm of the coupled hidden Markov model are studied. The fault diagnosis based on the coupled hidden Markov model can obtain better diagnostic accuracy by using the bearing artificial fault test. The comparison with the other features and the diagnosis methods is further proved. The diagnostic accuracy of the coupled hidden Markov model (HMM).
(6) the method of performance degradation assessment modeling and performance index calculation of multi-channel monitoring data using coupled hidden Markov model is studied. The calculation method of adaptive alarm limit is given by using performance index. Finally, the natural fatigue test data of gear, the data of rolling bearing accelerated fatigue test and the natural fatigue test of roller bearing are given. The analysis of the experimental data validates the effectiveness of the coupled hidden Markov model (HMM) for performance degradation assessment of complete and incomplete data. The results show that the selected performance indicators can quantitatively reflect the performance degradation of the bearings.
【學(xué)位授予單位】：上海交通大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2014
【分類號】：TH165.3;TP202

【參考文獻(xiàn)】

相關(guān)期刊論文 前6條

1 杜卓明;屠宏;耿國華;;KPCA方法過程研究與應(yīng)用[J];計(jì)算機(jī)工程與應(yīng)用;2010年07期

2 魯珂;趙繼東;吳躍;何曉飛;;基于保局投影的相關(guān)反饋算法[J];計(jì)算機(jī)輔助設(shè)計(jì)與圖形學(xué)學(xué)報(bào);2007年01期

3 張國印;樓宋江;王慶軍;程慧杰;;一種基于局部和判別特性的降維算法[J];計(jì)算機(jī)應(yīng)用研究;2009年09期

4 李學(xué)軍;廖傳軍;褚福磊;;適于聲發(fā)射信號故障特征提取的小波函數(shù)[J];機(jī)械工程學(xué)報(bào);2008年03期

5 李弼程,王波,魏俊,錢曾波,黃玉琪;一種有效的證據(jù)理論合成公式[J];數(shù)據(jù)采集與處理;2002年01期

6 李興林;張仰平;曹茂來;張燕遼;陸水根;李建平;;滾動軸承故障監(jiān)測診斷技術(shù)應(yīng)用進(jìn)展[J];工程與試驗(yàn);2009年04期

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