【摘要】：柴油機(jī)是一種最常見的動(dòng)力裝置,在國民經(jīng)濟(jì)和人們的日常生活中起著舉足輕重的作用,但復(fù)雜的結(jié)構(gòu)和惡劣的工作條件導(dǎo)致柴油機(jī)故障時(shí)有發(fā)生,給人們的生命和財(cái)產(chǎn)造成了很大的威脅。據(jù)統(tǒng)計(jì),在導(dǎo)致柴油機(jī)故障的各種原因中燃油系統(tǒng)故障與配氣機(jī)構(gòu)故障所占的比例較高,分別為27%和15.1%。此外,燃油系統(tǒng)與配氣機(jī)構(gòu)直接影響著發(fā)動(dòng)機(jī)的燃燒情況,它們的工作狀況對整個(gè)發(fā)動(dòng)機(jī)能否正常高效運(yùn)轉(zhuǎn)具有重要影響。因此,本文從柴油機(jī)故障診斷的實(shí)際需求出發(fā),選擇柴油機(jī)中故障概率較高的燃油系統(tǒng)與配氣機(jī)構(gòu)作為研究對象,以分析缸蓋振動(dòng)為切入點(diǎn),對柴油機(jī)燃油系統(tǒng)和配氣機(jī)構(gòu)的故障診斷方法開展深入研究,具體包括以下幾個(gè)方面:分析了柴油機(jī)缸蓋的主要激勵(lì)源,建立了柴油機(jī)缸蓋振動(dòng)信息模型,并對柴油機(jī)缸蓋振動(dòng)的時(shí)域和頻域特性進(jìn)行了研究。在兩臺(tái)柴油機(jī)上進(jìn)行了故障實(shí)驗(yàn),分別模擬了5種典型配氣機(jī)構(gòu)故障和12種常見的燃油系統(tǒng)與配氣機(jī)構(gòu)故障,為后續(xù)的研究工作提供了數(shù)據(jù)支撐。研究了固有時(shí)間尺度分解(Intrinsic Time-scale Decomposition,ITD)方法的基本原理,提出了一種基于ITD邊際譜和馬氏距離的故障診斷方法,并利用該方法對配氣機(jī)構(gòu)故障進(jìn)行了診斷,診斷結(jié)果表明該方法可以對配氣機(jī)構(gòu)故障進(jìn)行粗略識(shí)別。針對有向無環(huán)圖的誤差累積問題,提出了一種基于類別可分性的改進(jìn)有向無環(huán)圖(Separability based Directed Acyclic Graph,SDAG),并利用該方法構(gòu)建了相關(guān)向量機(jī)(Relevance Vector Machine,RVM)多分類模型。此外,提出了一種RVM多分類通用框架概率性糾錯(cuò)輸出編碼(Probability-based Error Correcting Output Codes,PECOC),該框架實(shí)現(xiàn)了RVM的概率性輸出與糾錯(cuò)輸出編碼多分類方法的有機(jī)結(jié)合。針對ITD方法的插值方法、終止條件等方面的不足,提出了一種改進(jìn)固有時(shí)間尺度分解(Improved Intrinsic Time-scale Decomposition,IITD)方法,并將ITD方法和SDAG-RVM相結(jié)合對柴油機(jī)配氣機(jī)構(gòu)故障進(jìn)行診斷。實(shí)驗(yàn)分析結(jié)果表明該方法可以獲得較高的故障診斷精度,優(yōu)于其它常規(guī)方法。研究了IITD方法的模態(tài)混疊問題,提出了一種完備總體改進(jìn)固有時(shí)間尺度分解(Complete Ensemble Imporved Intrinsic Time-scale Decomposition,CEIITD)方法,該方法通過向原信號(hào)添加正負(fù)相反的噪聲并且每產(chǎn)生一個(gè)旋轉(zhuǎn)分量進(jìn)行一次集總平均,消除了分解結(jié)果中的噪聲殘留以及由每次分解獲得的旋轉(zhuǎn)分量個(gè)數(shù)不同造成的難以進(jìn)行集總平均的問題。仿真分析結(jié)果表明CEIITD方法較好地解決了IITD方法的模態(tài)混疊問題,優(yōu)于其它常規(guī)方法。針對Wigner分布的交叉項(xiàng)問題,提出了一種基于IITD方法的Wigner分布交叉項(xiàng)處理方法即自適應(yīng)Wigner分布,并利用仿真信號(hào)對該方法進(jìn)行了驗(yàn)證。仿真分析結(jié)果表明該方法不僅可以消除交叉項(xiàng)的干擾,而且可以確保時(shí)頻圖具有較高的分辨率和時(shí)頻聚集性。針對原快速相關(guān)濾波(Fast Correlation-based Filter,FCBF)算法未考慮候選特征與已選特征子集間冗余的問題,提出了一種改進(jìn)FCBF算法。結(jié)合自適應(yīng)Wigner分布、改進(jìn)FCBF和PECOC-RVM提出了一種新的柴油機(jī)燃油系統(tǒng)與配氣機(jī)構(gòu)故障診斷方法,該方法將柴油機(jī)故障診斷問題轉(zhuǎn)化為時(shí)頻圖像分類問題,從而可以將在圖像分類領(lǐng)域得到大量成功應(yīng)用的不變矩、灰度統(tǒng)計(jì)特征、紋理特征和差分分形盒維數(shù)等圖像特征作為故障特征,降低了特征提取的難度和工作量。實(shí)驗(yàn)分析結(jié)果表明基于自適應(yīng)Wigner分布、改進(jìn)FCBF和PECOC-RVM的柴油機(jī)燃油系統(tǒng)與配氣機(jī)構(gòu)故障診斷方法可以獲得較高的故障診斷精度,優(yōu)于其它常規(guī)方法。針對同時(shí)診斷許多種故障時(shí)單一故障識(shí)別模型精度明顯下降的問題,提出了一種基于多級(jí)Adaboost-RVM的柴油機(jī)燃油系統(tǒng)和配氣機(jī)構(gòu)故障診斷方法。該方法利用KFCM算法將包含較多種故障類型的復(fù)雜分類問題分解為若干個(gè)簡單的問題,通過對每個(gè)簡單分類問題輸出結(jié)果的整合便可以獲得復(fù)雜分類問題的輸出結(jié)果,同時(shí)采用Adaboost技術(shù)對弱分類器進(jìn)行提升,進(jìn)一步增強(qiáng)了分類算法的診斷能力。實(shí)驗(yàn)分析結(jié)果表明該方法對于故障類型較多的診斷問題具有明顯的優(yōu)勢,優(yōu)于其它常規(guī)方法。
[Abstract]:The diesel engine is the most common power device, plays a very important role in the national economy and people's daily life, but the complicated structure and the bad working conditions cause the failure of the diesel engine to happen frequently, which poses a great threat to people's life and property. According to the statistics, the proportion of the failure of the fuel system and the gas distribution mechanism in the various causes leading to the failure of the diesel engine is high, 27% and 15.1%, respectively. In addition, the fuel system and the gas distribution mechanism directly affect the combustion of the engine, and their working conditions have an important influence on the normal and efficient operation of the whole engine. Therefore, based on the actual demand of the diesel engine fault diagnosis, the fuel system and the gas distribution mechanism with higher fault probability in the diesel engine are selected as the research object to analyze the cylinder head vibration as the starting point, and the fault diagnosis method of the diesel engine fuel system and the gas distribution mechanism is further researched. The main excitation source of the cylinder head of the diesel engine is analyzed, the vibration information model of the cylinder head of the diesel engine is established, and the time domain and frequency domain characteristics of the vibration of the cylinder head of the diesel engine are studied. The failure of five typical gas distribution mechanism and 12 common fuel system and gas distribution mechanism were simulated on two diesel engines, and data support was provided for the follow-up research work. In this paper, the basic principle of Intrisic Time-scale Decomposition (ITD) method is studied, a fault diagnosis method based on the ITD marginal spectrum and the horse's distance is proposed, and the fault of the gas distribution mechanism is diagnosed by the method. The results show that the method can be used for rough identification of the fault of the gas distribution mechanism. In order to solve the problem of error accumulation for acyclic graph, a kind of improved non-ring graph (SDAG) based on the division of the class is put forward, and the multi-classification model of the Reliance Vector Machine (RVM) is constructed by this method. In addition, an RVM multi-classification general-purpose frame probability error correction output code (PECOC) is proposed, which realizes the organic combination of the probability output of RVM and the multi-classification method of error correction output coding. In view of the deficiencies of the interpolation method and the termination condition of the ITD method, an improved Intro-time-scale Decomposition (IITD) method is proposed, and the ITD method and the SDAG-RVM are combined to diagnose the fault of the gas distribution mechanism of the diesel engine. The experimental results show that the method can obtain higher fault diagnosis precision than other conventional methods. In this paper, we study the mode mixing problem of the IITD method, and put forward a complete and improved complete-improved Intro-scale Decomposition (CEIITD) method. The method adds positive and negative noise to the original signal and makes a total average of one rotation component at a time. the problem that the noise residue in the decomposition result and the number of rotation components obtained by each decomposition are different due to the difference in the number of rotation components obtained by each decomposition are eliminated. The results of the simulation show that the CEIITD method better solves the problem of the IITD method and is superior to the other conventional methods. In order to solve the cross-item problem of the Wigner distribution, a Wigner distribution cross-item processing method based on the IITD method is proposed, that is, the adaptive Wigner distribution, and the method is verified by using the simulation signal. The simulation results show that the method can not only eliminate the cross-term interference, but also ensure that the time-frequency graph has higher resolution and time-frequency aggregation. In order to solve the problem of redundancy between the candidate feature and the selected feature subset, an improved FCBF algorithm is proposed for the Fast Correlation-based Filter (FCBF) algorithm. Combined with the adaptive Wigner distribution, the improved FCBF and PECOC-RVM provide a new method for fault diagnosis of the fuel system and the gas distribution mechanism of the diesel engine. The method converts the fault diagnosis of the diesel engine into the time-frequency image classification problem, so that the invariant moment of a large number of successful applications in the field of image classification can be obtained. Image features such as gray-scale statistical features, texture features, and differential fractal box dimension numbers are used as fault features, and the difficulty and workload of feature extraction are reduced. The experimental results show that the improved FCBF and PECOC-RVM diesel engine fuel system and the gas distribution mechanism fault diagnosis method can obtain higher fault diagnosis accuracy than other conventional methods based on the adaptive Wigner distribution. In order to solve the problem of the accuracy of single fault identification model at the same time, a new fault diagnosis method for diesel engine fuel system and gas distribution mechanism based on multi-level Adaboost-RVM is proposed. the method uses the KFCM algorithm to decompose the complex classification problem containing a plurality of fault types into a plurality of simple problems, the diagnostic capability of the classification algorithm is further enhanced. The results of the experiment show that the method has obvious advantages over the diagnosis of more fault types, and is superior to other conventional methods.
【學(xué)位授予單位】：天津大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類號(hào)】：TK428

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