本文關鍵詞： 液壓泵 故障診斷 免疫危險理論 特征選擇 包絡解調 小波簇　出處：《燕山大學》2012年碩士論文　論文類型：學位論文

【摘要】：液壓泵一直被液壓工程人員形象比喻為液壓系統的心臟，它的健康狀況將直接影響整個液壓生產制造設備的正常工作。液壓泵出現故障，輕者致使液壓生產制造設備部分功能缺失，重則將造成嚴重的、災難性的安全生產事故。因此研究液壓泵的狀態(tài)監(jiān)測和故障診斷技術就顯得尤為重要。目前，故障診斷技術正朝著自動化、智能化方向發(fā)展。受生物免疫危險理論模型的啟發(fā)，本文提出基于免疫危險理論的特征選擇算法實現了對具有眾多信息的原始高維特征向量的降維。本文還應用免疫危險理論原理開發(fā)了具有學習、聚類、記憶特性的故障診斷算法，并將這一算法應用于液壓泵的故障診斷中。 免疫危險理論應用于故障診斷領域的案例并不是很多，目前國內外學者更多將這一理論應用于信息安全、機器學習、數據挖掘等領域。本文基于生物免疫系統危險理論模型識別機制，應用Matlab軟件分別開發(fā)了具有能夠降維高維數據的特征選擇算法和故障診斷算法。特征選擇算法將具有眾多信息的高維特征向量降為低維特征向量，大大減少了后續(xù)故障診斷的時間。故障診斷算法將學習樣本看作為抗原，，并通過抗體（隨機檢測器）對抗原（學習樣本）的學習形成記憶抗體種群（成熟檢測器），記憶抗體種群（成熟檢測器）將識別抗原（測試樣本）的再次侵襲。 為驗證本文算法的有效性，本文以實驗室材料實驗機的軸向柱塞液壓泵作為診斷對象。應用加速度傳感器和NI數據采集卡采集液壓泵端蓋振動信號，運用細化譜分析技術分析與確定液壓泵各狀態(tài)原始采集振動信號的共振頻帶范圍；采用基于小波簇的包絡解調方法對確定的共振段信號進行包絡解調；將解調所得的包絡信號進行2層小波包分解與重構，提取每一子帶重構信號的時域、頻域和時頻域信息作原始特征向量；選擇目標函數（各狀態(tài)樣本類間散度矩陣的跡和樣本類內散度矩陣的跡的比值）作為特征選擇后特征子集的分類性能評判函數，應用本文提出的基于免疫危險理論的特征選擇算法，選擇出了目標函數值最大時所對應的特征向量；最后，采用基于免疫危險理論的故障診斷算法對特征選擇后的學習樣本（抗原）進行學習，并生成最終的各狀態(tài)成熟檢測器（記憶抗體群）以便完成對測試樣本（抗原）的狀態(tài)監(jiān)測和故障診斷。通過Matlab軟件的程序仿真，驗證了基于免疫危險理論液壓泵
[Abstract]:The hydraulic pump has always been likened to the heart of the hydraulic system by hydraulic engineers. Its health will directly affect the normal operation of the whole hydraulic production and manufacturing equipment. Some of the functions of hydraulic production and manufacturing equipment are missing and heavy will cause serious and catastrophic accidents in production safety. Therefore, it is very important to study the condition monitoring and fault diagnosis technology of hydraulic pump. At present, it is very important to study the condition monitoring and fault diagnosis technology of hydraulic pump. Fault diagnosis technology is developing towards automation and intelligence. Inspired by the biological immune hazard theory model, In this paper, a feature selection algorithm based on immune hazard theory is proposed to reduce the dimension of the original high dimensional feature vector with a lot of information. The algorithm of fault diagnosis based on memory characteristic is applied to the fault diagnosis of hydraulic pump. There are not many cases in which immune hazard theory is applied in the field of fault diagnosis. At present, many scholars at home and abroad apply this theory to information security and machine learning. Data mining and other fields. This paper based on the biological immune system hazard theory model recognition mechanism, The feature selection algorithm and fault diagnosis algorithm are developed by using Matlab software. The feature selection algorithm reduces the high dimensional feature vector with a lot of information to the low dimensional feature vector. It greatly reduces the time of subsequent fault diagnosis. The fault diagnosis algorithm treats the learning samples as antigens. Furthermore, the memory antibody population (maturation detector) and memory antibody population (maturation detector) will recognize the re-invasion of antigen (test sample) through the learning of antigen (learning sample) by antibody (random detector). In order to verify the validity of this algorithm, the axial plunger hydraulic pump of the laboratory material experiment machine is used as the diagnostic object. The vibration signals of the end cover of the hydraulic pump are collected by using the accelerometer and NI data acquisition card. The resonance frequency band range of the original vibration signal collected by hydraulic pump is analyzed and determined by the technique of thinning spectrum analysis, and the envelope demodulation method based on wavelet cluster is used to demodulate the signal in the determined resonance section. The envelope signal obtained by demodulation is decomposed and reconstructed by two-layer wavelet packet, and the time domain, frequency domain and time-frequency domain information of each sub-band reconstruction signal is extracted as the original eigenvector. The objective function (the ratio of the trace of the scatter matrix between each state sample class and the trace of the divergence matrix within the sample class) is selected as the classification performance evaluation function of the feature subset after feature selection. Using the feature selection algorithm based on immune hazard theory proposed in this paper, the feature vectors corresponding to the maximum value of the objective function are selected. A fault diagnosis algorithm based on immune hazard theory is used to study the learning samples (antigens) after feature selection. The final state maturation detector (memory antibody group) was generated in order to complete the state monitoring and fault diagnosis of the test sample (antigen). The simulation of Matlab software proved that the hydraulic pump based on immune hazard theory was based on the immune hazard theory.
【學位授予單位】：燕山大學
【學位級別】：碩士
【學位授予年份】：2012
【分類號】：TH137.51;TH165.3

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