【摘要】：柴油機(jī)作為一種常見的復(fù)雜的動力機(jī)械,廣泛應(yīng)用于汽車、飛機(jī)、船舶等交通工具中,它具有著效率高、比功率大等特點。整體動力系統(tǒng)能否安全、可靠的運(yùn)行,受到很多因素的影響,柴油機(jī)的工作狀態(tài)就是其中之一。由此可見,通過研究不斷完善柴油機(jī)監(jiān)測和故障診斷技術(shù)具有重大的實踐價值。柴油機(jī)工作時其內(nèi)部零件的狀態(tài)信息會經(jīng)過某種渠道反映在缸蓋振動當(dāng)中,所以通過缸蓋振動信號對柴油機(jī)進(jìn)行故障診斷是一種有效方法。本課題的研究,主要包含了如何有效地從柴油機(jī)缸蓋振動信號中提取故障特征信息以及診斷識別柴油機(jī)故障狀態(tài),提出了結(jié)合CEEMD-樣本熵的柴油機(jī)故障診斷新方法。本文所開展的工作有:(1)設(shè)計并構(gòu)建了能夠測取柴油機(jī)缸蓋振動信號的實驗平臺。選擇CZ4110柴油機(jī)為例展開測試,對該型號柴油機(jī)在不同工作狀態(tài)(包括正常的以及各種異常的狀態(tài))下的缸蓋振動信號數(shù)據(jù)進(jìn)行采集,在數(shù)據(jù)上為后面的柴油機(jī)缸蓋振動信號特征的提取與故障診斷研究提供支持。(2)研究了柴油機(jī)出現(xiàn)故障的原因以及傳播渠道。采用理論分析結(jié)合實驗驗證的方式,對柴油機(jī)不同故障下的缸蓋振動信號特征進(jìn)行分析,以時域和頻域入手,揭示了柴油機(jī)缸蓋振動信號在這兩方面上的特征。(3)對經(jīng)驗?zāi)Ｊ紼MD分解原理在信號分解領(lǐng)域中的應(yīng)用展開了研究;針對EMD對信號分解的過程中會出現(xiàn)模態(tài)混疊的問題,引入具有噪聲輔助功能的EEMD以及CEEMD分解方法,通過實驗驗證了兩者能夠在一定程度上抑制模態(tài)混疊,方法有效,實驗還證明CEEMD能夠?qū)⑿盘柗纸獾讲煌瑫r間尺度上從而對信號的局部信息進(jìn)行提取。提出結(jié)合CEEMD和小波的降噪方法,即先用CEEMD對信號進(jìn)行分解,再用小波對分解出來的每一階IMF分別進(jìn)行去噪,最后再重構(gòu)各降噪后的IMF作為最終降噪的信號,實驗驗證了其對信號進(jìn)行了有效的降噪處理。(4)引入專門用于度量信號復(fù)雜性和非線性的樣本熵,將它們應(yīng)用于柴油機(jī)出現(xiàn)故障時振動序列復(fù)雜性的度量當(dāng)中,分析表明了樣本熵有著一致性和受參數(shù)影響等特點。提出在挑選IMF分量時,選擇依據(jù)應(yīng)為完成分解的IMF分量和原始信號彼此間相關(guān)性的大小。對柴機(jī)缸蓋信號,使用樣本熵量化CEEMD分解出的IMF分量從而獲得缸蓋振動信號在不同頻帶上的信息,將其作為模式識別的輸入向量,為柴油機(jī)故障識別提供依據(jù)。(5)將CEEMD分解后的各IMF樣本熵作為特征向量輸入支持向量機(jī)訓(xùn)練并對柴油機(jī)故障樣本進(jìn)行識別,對比其他一些診斷方法,其提高了準(zhǔn)確率。研究了主元分析PCA原理在故障特征降維處理中的應(yīng)用,通過進(jìn)一步的對比診斷實驗,證明了該方法在有效保留了故障特征信息的同時去除了冗余成分,獲得了更精準(zhǔn)的診斷信息,用之結(jié)合CEEMD-樣本熵方法,能夠在柴油機(jī)故障診斷中取得良好識別效果。
[Abstract]:As a kind of common complex power machinery, diesel engine is widely used in vehicles, aircraft, ships and other vehicles. It has the characteristics of high efficiency and high specific power. Whether the whole power system can operate safely and reliably is affected by many factors, and the working condition of diesel engine is one of them. Therefore, it is of great practical value to improve the monitoring and fault diagnosis technology of diesel engine. The state information of the internal parts of a diesel engine is reflected in the vibration of the cylinder head through a certain channel, so it is an effective method to diagnose the fault of the diesel engine by the vibration signal of the cylinder head. The research of this subject mainly includes how to extract the fault characteristic information from the vibration signal of the cylinder head of diesel engine effectively and to diagnose and identify the fault state of the diesel engine. A new method of diesel engine fault diagnosis based on CEEMD- sample entropy is put forward. The main works of this paper are as follows: (1) an experimental platform for measuring vibration signals of diesel engine cylinder head is designed and constructed. Taking CZ4110 diesel engine as an example, the vibration signal data of cylinder head of the diesel engine under different working conditions (including normal and abnormal states) are collected. On the basis of the data, it can be used to extract the vibration signal characteristics of diesel engine cylinder head and to study the fault diagnosis. (2) the cause of diesel engine failure and the propagation channel are studied. The characteristics of cylinder head vibration signal under different faults of diesel engine are analyzed by means of theoretical analysis and experimental verification, starting with time domain and frequency domain. The characteristics of cylinder head vibration signal in diesel engine are revealed in this paper. (3) the application of empirical mode EMD decomposition principle in signal decomposition field is studied, and the problem of mode aliasing in the process of EMD signal decomposition is discussed. The EEMD and CEEMD decomposition methods with noise auxiliary function are introduced. The experimental results show that the two methods can suppress the mode aliasing to a certain extent and the method is effective. The experiment also proves that CEEMD can decompose the signal to different time scales to extract the local information of the signal. A denoising method combining CEEMD and wavelet is proposed, that is, the signal is decomposed by CEEMD, then each IMF is de-noised by wavelet, and then the IMF is reconstructed as the final de-noising signal. Experimental results show that the proposed method is effective in noise reduction. (4) sample entropy is introduced to measure signal complexity and nonlinearity, and it is applied to measure the complexity of vibration sequence in diesel engine when fault occurs. The analysis shows that the sample entropy is consistent and affected by parameters. In selecting the IMF component, the selection is based on the magnitude of the correlation between the IMF component and the original signal. For the cylinder head signal of diesel engine, the information of cylinder head vibration signal in different frequency bands is obtained by quantizing the IMF component decomposed by CEEMD with sample entropy, which is regarded as the input vector of pattern recognition. (5) the entropy of IMF samples decomposed by CEEMD is used as feature vector to input support vector machine to train diesel engine fault samples, and compared with other diagnosis methods, the accuracy is improved. The application of principal component analysis (PCA) principle in fault feature dimensionality reduction is studied. Through further comparative diagnosis experiments, it is proved that this method not only effectively preserves fault feature information, but also removes redundant components. More accurate diagnosis information is obtained, and the method of CEEMD- sample entropy can be used to identify diesel engine fault.
【學(xué)位授予單位】：江蘇科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TK428

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