往復式高壓隔膜泵單向閥狀態(tài)監(jiān)測及故障診斷研究
發(fā)布時間：2018-07-21 12:40
【摘要】：大型往復式高壓隔膜泵是長距離、高揚程、高濃度礦漿管道輸送的核心動力設備,它的工作狀態(tài)直接影響企業(yè)生產(chǎn)效率。單向閥作為泵的核心機械零部件之一,需具有良好的快開、快關、密封性及承壓性,比泵的其他部件更容易出現(xiàn)故障。此外,單向閥的運行狀態(tài)與輸送礦物的粒徑級配、漿體流變特性、輸送壓力、泵的固有材質屬性及安裝等密切相關,致使單向閥的故障具有突發(fā)性、并發(fā)性、多源性、非平穩(wěn)性和非線性等特點,大大增加了單向閥狀態(tài)監(jiān)測和故障診斷的難度。因此,從單向閥振動信號分析入手,選取有效的特征提取及故障診斷方法是單向閥運行狀態(tài)監(jiān)測及故障診斷研究的核心內容,具有重要理論研究價值及經(jīng)濟意義。本文圍繞單向閥狀態(tài)監(jiān)測及故障診斷開展了如下研究工作：(1)提出了一種基于局部均值分解(Local Mean Decomposition, LMD)和包絡解調的單向閥故障檢測方法。單向閥故障振動信號通常表現(xiàn)為復雜的調幅調頻信號,使得利用包絡解調方法提取單向閥故障特征頻率成為可能。但是,單向閥受環(huán)境噪聲、耦合工況及其他激勵源干擾等影響,其振動信號表現(xiàn)出明顯的非線性,直接對其進行包絡解調無法獲得理想的效果。因此,提出基于LMD和包絡解調的單向閥故障檢測方法,先利用LMD將信號分解為一系列純調幅調頻信號——乘積函數(shù)(Production Function, PF);進而對PF分量進行包絡解調以完成單向閥故障檢測。(2)提出了一種基于多域混合特征極限學習機(extreme learning machine, ELM)的單向閥故障診斷方法。針對單一域特征無法完全描述單向閥運行狀態(tài)、支持向量機(Support Vector Machine, SVM)和BP神經(jīng)網(wǎng)絡等模型優(yōu)化參數(shù)多、速度慢等問題,結合多域混合特征和ELM的優(yōu)勢,提出基于多域混合特征ELM的單向閥故障診斷方法。提取單向閥振動信號時域、頻域、小波域、TK (Teager Kaiser)域特征構建多域混合特征集,并引入核主元分析(Kernel Principal Component Analysis, KPCA)方法進行多域混合特征集的二次特征提取,消除特征冗余。最后基于二次特征提取后的多域混合特征集建立單向閥ELM故障診斷模型,完成單向閥故障診斷。(3)提出了一種基于小波包能量熵和模糊核極限學習機(fuzzy kernel extreme learning machine, F-KELM)的單向閥故障診斷方法。在討論復雜非線性振動信號、樣本分布不均衡及ELM隱含層神經(jīng)元個數(shù)對ELM分類性能影響的基礎上,引入小波包能量熵、核函數(shù)、模糊隸屬函數(shù)建立小波包能量熵和模糊核極限學習機的故障診斷模型。通過滾動軸承和單向閥的實驗對比分析,證實了方法能有效解決上述難題,提高了模型分類性能及泛化能力。(4)提出了一種基于多核代價敏感極限學習機(multi-kernel cost sensitive extreme learning machine, MKL-CS-ELM)的單向閥故障診斷方法。針對單一核函數(shù)分類器無法完全詮釋分類決策函數(shù)、分類代價均等的不合理假設及樣本分布不均衡對分類器影響嚴重等問題,引入多核函數(shù)和代價敏感學習機制,建立基于多核代價敏感極限學習機的故障診斷模型(MKL-CS-ELM)。并通過滾動軸承和單向閥二分類和多分類故障診斷的對比實驗分析,方法取得與多核代價敏感支持向量機(multi-kernel cost sensitive support vector machine, MKL-CS-SVM)相當?shù)奶幚硇Ч?并繼承了ELM時間消耗少的優(yōu)點,提高了方法的實用性。同時,方法引入魯棒性指標對代價敏感處理方法的效果進行評判,為代價敏感處理方法的選取提供了依據(jù)。(5)完成單向閥狀態(tài)監(jiān)測及故障系統(tǒng)的研發(fā)及測試�；贑#和Matlab混合編程模式,完成了單向閥狀態(tài)監(jiān)測及故障診斷系統(tǒng)開發(fā)。選取云南大紅山鐵精礦管道輸送高壓隔膜泵作為測試對象,采集單向閥整個生命周期的振動信號,完成單向閥運行狀態(tài)監(jiān)測及故障診斷系統(tǒng)測試。本文以礦漿管道輸送大型往復式高壓隔膜泵單向閥為研究對象,完成其狀態(tài)監(jiān)測與故障診斷方法的探索研究及系統(tǒng)開發(fā),豐富了往復式機械設備的故障診斷研究理論,推動了往復式機械設備的故障診斷技術的應用及發(fā)展。
[Abstract]:The large reciprocating high pressure diaphragm pump is the core power equipment of long distance, high lift and high concentration slurry pipeline. Its working condition directly affects the production efficiency of the enterprise. As one of the core mechanical parts of the pump, the one-way valve needs to have good fast opening, fast closing, sealing and pressure bearing, which is more prone to failure than the other parts of the pump. In addition, the operation state of the one-way valve is closely related to the grain size distribution of the conveying minerals, the rheological characteristics of the slurry, the conveying pressure, the inherent material properties of the pump and the installation of the pump, which makes the failure of the one-way valve have the characteristics of sudden, concurrency, multi source, non-stationary and nonlinear, which greatly increases the difficulty of the state monitoring and fault diagnosis of the one-way valve. Therefore, starting from the analysis of the vibration signal analysis of one way valve, selecting effective feature extraction and fault diagnosis method is the core content of the monitoring and fault diagnosis of one-way valve operation and fault diagnosis. It has important theoretical research value and economic significance. The following research work is carried out on the state monitoring and fault diagnosis of one-way valve. (1) a new research work is put forward. A one-way valve fault detection method based on Local Mean Decomposition (LMD) and envelope demodulation is used. The one-way valve fault vibration signal is usually expressed as a complex amplitude modulation and frequency modulation signal, making use of the envelope demodulation method to extract the frequency of the one-way valve fault, but the one-way valve is subjected to environmental noise, coupling conditions and Other excitation sources, such as interference, the vibration signal shows obvious nonlinearity, and its envelope demodulation can not achieve the ideal effect. Therefore, a one-way valve fault detection method based on LMD and envelope demodulation is proposed. First, LMD is used to decompose the signal into a series of pure amplitude modulation signals, product function (Production Function,) PF); then the PF component is enveloped and demodulated to complete the one-way valve fault detection. (2) a one-way valve fault diagnosis method based on the multi domain hybrid feature limit learning machine (extreme learning machine, ELM) is proposed. The single domain characteristics can not fully describe the one-way valve movement state, the support vector machine (Support Vector Machine, SVM) and B are used. P neural network model has many optimization parameters, slow speed and so on. Combining the multi domain mixed feature and the advantage of ELM, a multi domain hybrid feature ELM based one-way valve fault diagnosis method is proposed. The multi domain mixed feature set is extracted from the time-domain, frequency domain, wavelet domain, and TK (Teager Kaiser) domain characteristics of the vibration signal of one-way valve, and the kernel principal component analysis (Kernel) is introduced. Principal Component Analysis, KPCA) method is used to extract the two characteristics of multi domain mixed feature set and eliminate feature redundancy. Finally, based on the multi domain mixed feature set after two feature extraction, a one-way valve ELM fault diagnosis model is established, and the one-way valve fault diagnosis is completed. (3) a kind of learning based on wavelet packet energy entropy and fuzzy kernel limit learning is proposed. Fuzzy kernel extreme learning machine (F-KELM) fault diagnosis method of one way valve. Based on the discussion of the complex nonlinear vibration signal, the disequilibrium of sample distribution and the influence of the number of neurons in the ELM hidden layer on the ELM classification performance, the wavelet packet energy entropy, the kernel function and the fuzzy membership function are introduced to establish the wavelet packet energy entropy and the fuzzy kernel. The fault diagnosis model of the limited learning machine. Through the comparison and analysis of the experiment of rolling bearing and one-way valve, it is proved that the method can solve the above problems effectively and improve the classification performance and generalization ability of the model. (4) a kind of multi-kernel cost sensitive extreme learning machine, MKL-CS-ELM is proposed. The single kernel function classifier can not fully interpret the classification decision function, the unreasonable assumption of the classification of the cost equality and the serious influence of the disequilibrium of the sample distribution on the classifier, and introduces the multi kernel function and the cost sensitive learning mechanism, and establishes the fault diagnosis model based on the multi-core cost sensitive limit learning machine. Type (MKL-CS-ELM). Through comparative experiment analysis of two classification and multi classification fault diagnosis of rolling bearing and one-way valve, the method has obtained the equivalent processing effect with multi-kernel cost sensitive support vector machine, MKL-CS-SVM, and inherits the advantages of low consumption of ELM time, and improves the method's reality. At the same time, the method introduces the robustness index to judge the effect of the cost sensitive processing method, and provides the basis for the selection of the cost sensitive processing method. (5) complete the state monitoring of the one-way valve and the development and test of the fault system. Based on the C# and Matlab hybrid programming model, the state monitoring and fault diagnosis system of the one-way valve is completed. The high pressure diaphragm pump of Yunnan Da Hongshan iron concentrate is selected as the test object, and the vibration signals of the whole life cycle of the one-way valve are collected to complete the monitoring of the operation state of the one-way valve and the test of the fault diagnosis system. The paper takes the large reciprocating high-pressure diaphragm pump as the research object, and completes its state monitoring and failure. The research and system development of the diagnostic method enrich the theory of fault diagnosis for reciprocating machinery and promote the application and development of the fault diagnosis technology of the reciprocating machinery.
【學位授予單位】：昆明理工大學
【學位級別】：博士
【學位授予年份】：2016
【分類號】：TH323
，
本文編號：2135556
[Abstract]:The large reciprocating high pressure diaphragm pump is the core power equipment of long distance, high lift and high concentration slurry pipeline. Its working condition directly affects the production efficiency of the enterprise. As one of the core mechanical parts of the pump, the one-way valve needs to have good fast opening, fast closing, sealing and pressure bearing, which is more prone to failure than the other parts of the pump. In addition, the operation state of the one-way valve is closely related to the grain size distribution of the conveying minerals, the rheological characteristics of the slurry, the conveying pressure, the inherent material properties of the pump and the installation of the pump, which makes the failure of the one-way valve have the characteristics of sudden, concurrency, multi source, non-stationary and nonlinear, which greatly increases the difficulty of the state monitoring and fault diagnosis of the one-way valve. Therefore, starting from the analysis of the vibration signal analysis of one way valve, selecting effective feature extraction and fault diagnosis method is the core content of the monitoring and fault diagnosis of one-way valve operation and fault diagnosis. It has important theoretical research value and economic significance. The following research work is carried out on the state monitoring and fault diagnosis of one-way valve. (1) a new research work is put forward. A one-way valve fault detection method based on Local Mean Decomposition (LMD) and envelope demodulation is used. The one-way valve fault vibration signal is usually expressed as a complex amplitude modulation and frequency modulation signal, making use of the envelope demodulation method to extract the frequency of the one-way valve fault, but the one-way valve is subjected to environmental noise, coupling conditions and Other excitation sources, such as interference, the vibration signal shows obvious nonlinearity, and its envelope demodulation can not achieve the ideal effect. Therefore, a one-way valve fault detection method based on LMD and envelope demodulation is proposed. First, LMD is used to decompose the signal into a series of pure amplitude modulation signals, product function (Production Function,) PF); then the PF component is enveloped and demodulated to complete the one-way valve fault detection. (2) a one-way valve fault diagnosis method based on the multi domain hybrid feature limit learning machine (extreme learning machine, ELM) is proposed. The single domain characteristics can not fully describe the one-way valve movement state, the support vector machine (Support Vector Machine, SVM) and B are used. P neural network model has many optimization parameters, slow speed and so on. Combining the multi domain mixed feature and the advantage of ELM, a multi domain hybrid feature ELM based one-way valve fault diagnosis method is proposed. The multi domain mixed feature set is extracted from the time-domain, frequency domain, wavelet domain, and TK (Teager Kaiser) domain characteristics of the vibration signal of one-way valve, and the kernel principal component analysis (Kernel) is introduced. Principal Component Analysis, KPCA) method is used to extract the two characteristics of multi domain mixed feature set and eliminate feature redundancy. Finally, based on the multi domain mixed feature set after two feature extraction, a one-way valve ELM fault diagnosis model is established, and the one-way valve fault diagnosis is completed. (3) a kind of learning based on wavelet packet energy entropy and fuzzy kernel limit learning is proposed. Fuzzy kernel extreme learning machine (F-KELM) fault diagnosis method of one way valve. Based on the discussion of the complex nonlinear vibration signal, the disequilibrium of sample distribution and the influence of the number of neurons in the ELM hidden layer on the ELM classification performance, the wavelet packet energy entropy, the kernel function and the fuzzy membership function are introduced to establish the wavelet packet energy entropy and the fuzzy kernel. The fault diagnosis model of the limited learning machine. Through the comparison and analysis of the experiment of rolling bearing and one-way valve, it is proved that the method can solve the above problems effectively and improve the classification performance and generalization ability of the model. (4) a kind of multi-kernel cost sensitive extreme learning machine, MKL-CS-ELM is proposed. The single kernel function classifier can not fully interpret the classification decision function, the unreasonable assumption of the classification of the cost equality and the serious influence of the disequilibrium of the sample distribution on the classifier, and introduces the multi kernel function and the cost sensitive learning mechanism, and establishes the fault diagnosis model based on the multi-core cost sensitive limit learning machine. Type (MKL-CS-ELM). Through comparative experiment analysis of two classification and multi classification fault diagnosis of rolling bearing and one-way valve, the method has obtained the equivalent processing effect with multi-kernel cost sensitive support vector machine, MKL-CS-SVM, and inherits the advantages of low consumption of ELM time, and improves the method's reality. At the same time, the method introduces the robustness index to judge the effect of the cost sensitive processing method, and provides the basis for the selection of the cost sensitive processing method. (5) complete the state monitoring of the one-way valve and the development and test of the fault system. Based on the C# and Matlab hybrid programming model, the state monitoring and fault diagnosis system of the one-way valve is completed. The high pressure diaphragm pump of Yunnan Da Hongshan iron concentrate is selected as the test object, and the vibration signals of the whole life cycle of the one-way valve are collected to complete the monitoring of the operation state of the one-way valve and the test of the fault diagnosis system. The paper takes the large reciprocating high-pressure diaphragm pump as the research object, and completes its state monitoring and failure. The research and system development of the diagnostic method enrich the theory of fault diagnosis for reciprocating machinery and promote the application and development of the fault diagnosis technology of the reciprocating machinery.
【學位授予單位】：昆明理工大學
【學位級別】：博士
【學位授予年份】：2016
【分類號】：TH323
，
本文編號：2135556
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