發(fā)布時(shí)間：2018-05-04 08:10

  本文選題：面向 + 城軌�。� 參考：《北京交通大學(xué)》2015年博士論文

【摘要】：城市軌道交通是我國(guó)城鎮(zhèn)化和城市現(xiàn)代化的全局性和支撐性的基礎(chǔ)設(shè)施,是城市綜合交通的骨干交通方式。截止2012年底,全國(guó)城市軌道交通規(guī)劃總里程超過(guò)14000公里,覆蓋53個(gè)大中城市；截止2013年底,全國(guó)累計(jì)批復(fù)36個(gè)城市的軌道交通建設(shè)總里程約6000公里；累計(jì)建成開(kāi)通運(yùn)營(yíng)總里程已達(dá)2266公里。 如何保障城市軌道交通系統(tǒng)的運(yùn)營(yíng)安全,提升運(yùn)營(yíng)維護(hù)水平,降低全生命周期運(yùn)營(yíng)成本已成為我國(guó)城市軌道交通可持續(xù)健康發(fā)展的瓶頸問(wèn)題,迫切需要研發(fā)適應(yīng)我國(guó)國(guó)情和運(yùn)營(yíng)管理機(jī)制的包括城軌列車走行部軸承運(yùn)行狀態(tài)在途檢測(cè)、故障診斷和預(yù)警技術(shù)在內(nèi)的軌道交通安全保障技術(shù)與裝備體系。 本文以形成符合國(guó)情和自主知識(shí)產(chǎn)權(quán)的城軌列車走行部軸承運(yùn)營(yíng)狀態(tài)在途監(jiān)測(cè)及預(yù)警關(guān)鍵理論技術(shù)與相關(guān)系統(tǒng)為目標(biāo),形成了具有普適意義的如下理論方法和關(guān)鍵技術(shù)及裝備： 本文對(duì)城軌列車走行部軸承在途故障診斷展開(kāi)以下研究： 1.深入研究了城軌列車走行部軸承結(jié)構(gòu)、振動(dòng)機(jī)理和故障形式及原因,提出了多因素(徑向游隙、轉(zhuǎn)速、載荷、波紋度等)綜合作用下的城軌列車走行部軸承靜、動(dòng)力學(xué)模型。分析了不同因素對(duì)系統(tǒng)的影響,得出軸承內(nèi)在結(jié)構(gòu)以及外部原因與征兆表現(xiàn)之間的內(nèi)在聯(lián)系和映射關(guān)系,再結(jié)合城軌列車特定運(yùn)營(yíng)環(huán)境,確定走行部軸承的監(jiān)測(cè)參數(shù)與監(jiān)測(cè)部位,為后續(xù)城軌列車軸承疲勞壽命評(píng)估和在途故障辨識(shí)提供理論和技術(shù)支撐。 2.基于獲取的實(shí)時(shí)動(dòng)載荷數(shù)據(jù),并在軸承疲勞壽命分析理論的基礎(chǔ)上,構(gòu)建了時(shí)變工況下城軌列車走行部軸承的疲勞壽命評(píng)估模型。首先系統(tǒng)分析了不同參數(shù)(轉(zhuǎn)速、載荷、節(jié)徑、滾動(dòng)體數(shù)目)對(duì)軸承疲勞壽命的影響,在此基礎(chǔ)上,結(jié)合軌道交通列車時(shí)變運(yùn)營(yíng)工況,建立了變工況下走行部軸承疲勞壽命模型,并利用廣州地鐵時(shí)變工況環(huán)境下的數(shù)據(jù)對(duì)模型進(jìn)行了測(cè)試,驗(yàn)證了模型的合理性和有效性。 3.從基于實(shí)時(shí)數(shù)據(jù)特征提取方面考慮,提出了面向城軌列車走行部軸承多智能算法融合的在途故障辨識(shí)方法。在研究小波分析、包絡(luò)分析、經(jīng)驗(yàn)?zāi)�態(tài)分解、神經(jīng)網(wǎng)絡(luò)、遺傳算法等信號(hào)處理方法基礎(chǔ)上,融合諧波小波良好的時(shí)頻局部化特性和包絡(luò)解調(diào)的優(yōu)點(diǎn),設(shè)計(jì)了基于諧波小波包絡(luò)分析的城軌列車軸承故障辨識(shí)方法；基于小波包的時(shí)頻性和神經(jīng)網(wǎng)絡(luò)的自學(xué)習(xí)、自適應(yīng)性,構(gòu)建了基于小波包神經(jīng)網(wǎng)絡(luò)的城軌列車軸承故障辨識(shí)方法：結(jié)合經(jīng)驗(yàn)?zāi)�態(tài)分解方法精細(xì)的時(shí)頻解析度、神經(jīng)網(wǎng)絡(luò)的自學(xué)習(xí)、自適應(yīng)性和遺傳算法的全局搜索能力,建立了基于時(shí)頻域多維特征參量和遺傳神經(jīng)網(wǎng)絡(luò)的城軌列車走行部軸承在途故障辨識(shí)方法,并利用不同工況下的故障數(shù)據(jù)對(duì)算法辨識(shí)精度和實(shí)時(shí)性進(jìn)行測(cè)試,診斷結(jié)果表明面向城軌列車走行部軸承多智能算法融合的在途故障辨識(shí)方法具有較高的辨識(shí)精度和較快的診斷效率,從而為在途故障診斷系統(tǒng)的研發(fā)奠定基礎(chǔ)。 4.基于城軌列車走行部軸承多智能算法融合故障辨識(shí)方法的研究成果,并結(jié)合廣州地鐵現(xiàn)有安全監(jiān)測(cè)裝備,設(shè)計(jì)了城軌列車走行部軸承在途故障診斷系統(tǒng),并通過(guò)試驗(yàn)臺(tái)數(shù)據(jù)驗(yàn)證了該系統(tǒng)故障辨識(shí)的準(zhǔn)確性和實(shí)時(shí)性。
[Abstract]:Urban rail transit is a global and supporting infrastructure for urbanization and urban modernization in China. It is the backbone of urban integrated transportation. By the end of 2012, the total mileage of urban rail transit planning in China was more than 14000 kilometers, covering 53 large and medium-sized cities. By the end of 2013, the whole country has approved the rail transit of 36 cities in China. The total mileage of construction is about 6000 km, and the total mileage of the total operation has reached 2266 km.
How to ensure the operation safety of urban rail transit system, improve the level of operation and maintenance and reduce the cost of life cycle has become the bottleneck of the sustainable and healthy development of urban rail transit in our country. It is urgent to develop the running state of the bearing of urban rail train, which is adapted to the national conditions and operation management mechanism of our country. The technology and equipment system of rail traffic safety, including fault diagnosis and early warning technology.
The aim of this paper is to form the key theory and technology and related system of the bearing operation of urban rail train, which is in line with the national conditions and independent intellectual property rights, and forms the following theoretical methods and key technology and equipment.
In this paper, the following research is carried out on the fault diagnosis of bearing on the way of urban rail train.
1. the bearing structure, the vibration mechanism and the fault form and the cause of the bearing of the rail train are studied in depth. The bearing static and dynamic models of the track train under the multiple factors (radial clearance, rotational speed, load and waviness) are put forward. The influence of different factors on the system is analyzed, and the internal structure and external reasons of the bearing are analyzed. The internal relation and mapping relation between the sign performance and the specific operating environment of the rail train will be combined to determine the monitoring parameters and monitoring parts of the bearing of the walking train, and provide the theoretical and technical support for the bearing fatigue life assessment and the fault identification of the following urban rail train bearings.
2. based on the real-time dynamic load data obtained, and on the basis of the theory of bearing fatigue life analysis, a fatigue life assessment model for the bearing of the rail train in time varying condition is constructed. First, the effects of different parameters (rotational speed, load, diameter and number of rolling body) on the fatigue life of the bearing are analyzed systematically, and on this basis, the track is combined with the track. The fatigue life model of the bearing is established under the variable operating conditions. The model is tested by the data of the time-varying working conditions of Guangzhou metro, which verifies the rationality and effectiveness of the model.
3. based on the feature extraction of real time data, a method of fault identification is proposed, which is based on the wavelet analysis, envelope analysis, empirical mode decomposition, neural network, genetic algorithm and other signal processing methods, which combines the good time frequency localization of the harmonic wavelets. As well as the advantages of envelope demodulation, a fault identification method for urban rail bearing based on the harmonic wavelet envelopment analysis is designed. Based on the time frequency of the wavelet packet and self-learning and self-adaptive of the neural network, a fault identification method for urban rail bearing based on the wavelet packet neural network is constructed, and the precise time frequency of the method is combined with the empirical mode decomposition method. Resolution, self-learning of neural network, self-adaptive and global searching ability of genetic algorithm, a fault identification method for bearing in the walk part of urban rail train based on multi-dimensional characteristic parameters of time frequency domain and genetic neural network is established. The identification accuracy and real-time performance of the algorithm are tested by using the fault data under different working conditions, and the diagnosis results are obtained. The results show that the fault identification method for the multi intelligent algorithm fusion for the bearing of urban rail trains has higher identification precision and faster diagnosis efficiency, thus laying the foundation for the research and development of the fault diagnosis system in the road.
4. based on the research results of the fault identification method of multi intelligent algorithm for bearing of urban rail train, combined with the existing safety monitoring equipment in Guangzhou metro, the fault diagnosis system of the bearing in the walk part of the rail train is designed, and the accuracy and real time of the fault identification of the system is verified by the data of the test bench.

【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2015
【分類號(hào)】：U279

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本文編號(hào)：1842266