風力齒輪箱軸承故障的AE信號特征提取與診斷方法研究
發(fā)布時間：2019-07-03 19:30
【摘要】：由于能源危機和環(huán)境污染問題日益嚴重,風能作為無污染可再生能源已受到世界各國的高度重視。隨著風電裝機容量的不斷增加,齒輪箱故障發(fā)生率也不斷升高,嚴重影響了風電的利用率。滾動軸承是風力齒輪箱中故障率較高的部件,軸承故障嚴重時會導致重大的事故。由于滾動軸承在故障形成初期及發(fā)展階段都會產生聲發(fā)射信號,所以采用聲發(fā)射技術對其進行早期故障診斷,對避免重大事故的發(fā)生和降低運行維護成本都具有重要的意義。本論文研究目的是探索風力齒輪箱軸承聲發(fā)射信號特征提取和故障診斷的新方法,以解決目前傳統(tǒng)方法存在的抗噪聲干擾能力差、參數(shù)選擇復雜和模糊樣本難識別等問題,以期提高特征提取準確性和故障診斷的正確率。本論文主要研究內容如下:首先,針對風力齒輪箱軸承聲發(fā)射信號在采集時,單通道中存在著多故障源信號復合問題,提出了一種基于集成經驗模態(tài)分解和改進的快速獨立分量分析算法的單通道盲源分離方法。該方法將一維的單通道復合故障聲發(fā)射信號通過集成經驗模態(tài)分解算法分解成多維的本征模態(tài)函數(shù)分量,然后根據(jù)估計的源信號數(shù)目建立相同數(shù)目的輸入信號,最后輸入信號通過改進的快速獨立分量分析算法進行分離。該方法解決了單通道信號盲源分離的欠定問題,克服了原快速獨立分量分析算法對初值敏感的不足,對復合故障(損傷和斷裂)聲發(fā)射信號進行了有效的分離。其次,針對風力齒輪箱軸承聲發(fā)射信號具有非平穩(wěn)性和不確定性的特點,提出了一種基于完整集成經驗模態(tài)分解和云模型特征熵的特征提取方法。該方法用完整集成經驗模態(tài)分解算法將信號分解成多維的本征模態(tài)函數(shù)分量,由相關系數(shù)法選取出的敏感本征模態(tài)函數(shù)分量重構信號,再利用逆向云發(fā)生器計算重構信號的云模型特征熵作為信號的特征參數(shù)。通過實驗測試與分析,該方法不僅有效的提取了聲發(fā)射信號的特征,還克服了傳統(tǒng)熵方法存在的參數(shù)選擇復雜和閾值取值敏感等缺點。再次,為解決在風力齒輪箱軸承聲發(fā)射信號進行特征提取時存在的強噪聲干擾問題,提出了一種基于改進的集成經驗模態(tài)分解算法和多尺度排列熵偏均值的特征提取方法。該方法首先通過云相似度法選取敏感本征模態(tài)函數(shù)分量,然后由敏感本征模態(tài)函數(shù)分量重構信號,最后計算重構信號的多尺度排列熵偏均值作為信號的特征參數(shù)。該方法克服了傳統(tǒng)方法在選取敏感本征模態(tài)函數(shù)分量時存在的誤判缺點,降低了噪聲干擾,從而提高了特征提取的準確性。最后,為解決具有不確定因素的樣本影響風力齒輪箱軸承故障診斷正確率的問題,提出了一種基于多維云模型確定度的模糊支持向量機故障診斷方法。該方法采用多維云模型確定度作為模糊支持向量機算法的隸屬度,克服了傳統(tǒng)模糊支持向量機算法不能將噪聲或野值樣本從有效樣本集中區(qū)分出來的缺點。利用軸承故障聲發(fā)射數(shù)據(jù)進行驗證,結果表明該方法可以有效地抑制不確定信息(噪聲或野值樣本)的干擾,具有較高的故障診斷性能。
[Abstract]:As the energy crisis and environmental pollution are becoming more and more serious, wind energy, as a pollution-free renewable energy, has been highly valued by the countries of the world. With the increasing of the installed capacity of wind power, the fault rate of the gear box is increasing, and the utilization rate of wind power is seriously affected. The rolling bearing is a component with higher failure rate in the wind-driven gear box, and the bearing failure can lead to a major accident. As the acoustic emission signal is generated at the initial stage and the development stage of the rolling bearing, it is of great significance to use the acoustic emission technology to diagnose the early fault and to avoid the occurrence of major accidents and to reduce the operation and maintenance cost. The purpose of this paper is to explore the new method of the feature extraction and fault diagnosis of the bearing acoustic emission signal of the wind-driven gear box, to solve the problems of the current traditional method, such as the difference of the anti-noise interference ability, the complex parameter selection and the difficult identification of the fuzzy samples, In ord to improve that accuracy of feature extraction and the correct rate of fault diagnosis. The main contents of this thesis are as follows: First, when the sound emission signal of the bearing of the wind-driven gear box is collected, the multi-fault source signal composite problem exists in the single channel. A single-channel blind source separation method based on integrated empirical mode decomposition and improved fast independent component analysis algorithm is proposed. The method comprises the following steps of: decomposing a one-dimensional single-channel composite fault sound emission signal into a multi-dimensional intrinsic mode function component through an integrated empirical mode decomposition algorithm, and then establishing the same number of input signals according to the estimated source signal number, The final input signal is separated by an improved fast independent component analysis algorithm. The method solves the problem of the problem of the single-channel signal blind source separation, overcomes the defect that the original fast independent component analysis algorithm is sensitive to the initial value, and effectively separates the composite fault (damage and fracture) acoustic emission signal. Secondly, aiming at the characteristics of non-stationarity and uncertainty of the acoustic emission signal of the wind-driven gearbox, a feature extraction method based on the complete integrated empirical mode decomposition and the characteristic entropy of the cloud model is proposed. The method comprises the following steps of: decomposing a signal into a multi-dimensional intrinsic mode function component by a complete integrated empirical mode decomposition algorithm, and reconstructing a signal by a sensitive intrinsic mode function component selected by a correlation coefficient method, And then using a reverse cloud generator to calculate the characteristic entropy of the cloud model of the reconstructed signal as the characteristic parameter of the signal. Through the experimental test and analysis, the method not only effectively extracts the characteristics of the acoustic emission signal, but also overcomes the defects of the traditional entropy method that the parameter selection is complex and the threshold value is sensitive and the like. Thirdly, in order to solve the problem of strong noise interference in the feature extraction of the acoustic emission signal of the wind-driven gear box, a feature extraction method based on the improved integrated empirical mode decomposition algorithm and the multi-scale arrangement entropy bias means is proposed. The method comprises the following steps of: firstly, selecting a sensitive intrinsic mode function component by a cloud similarity function, and then reconstructing a signal from a sensitive intrinsic mode function component, and finally calculating a multi-scale arrangement entropy partial average value of the reconstructed signal as a characteristic parameter of the signal. The method overcomes the misjudgment of the traditional method when the sensitive intrinsic mode function component is selected, reduces the noise interference, and improves the accuracy of the feature extraction. Finally, a fuzzy support vector machine fault diagnosis method based on multi-dimensional cloud model determination is proposed in order to solve the problem that a sample with uncertain factors influences the fault diagnosis rate of the bearing of the wind-driven gear box. The method adopts the multi-dimensional cloud model to determine the degree of membership of the fuzzy support vector machine algorithm, and overcomes the defect that the traditional fuzzy support vector machine algorithm cannot distinguish the noise or the field value sample from the effective sample set. The results show that the method can effectively suppress the interference of uncertain information (noise or field value samples) and has higher fault diagnosis performance.
【學位授予單位】：哈爾濱工業(yè)大學
【學位級別】：博士
【學位授予年份】：2016
【分類號】：TH133.3
本文編號：2509615
[Abstract]:As the energy crisis and environmental pollution are becoming more and more serious, wind energy, as a pollution-free renewable energy, has been highly valued by the countries of the world. With the increasing of the installed capacity of wind power, the fault rate of the gear box is increasing, and the utilization rate of wind power is seriously affected. The rolling bearing is a component with higher failure rate in the wind-driven gear box, and the bearing failure can lead to a major accident. As the acoustic emission signal is generated at the initial stage and the development stage of the rolling bearing, it is of great significance to use the acoustic emission technology to diagnose the early fault and to avoid the occurrence of major accidents and to reduce the operation and maintenance cost. The purpose of this paper is to explore the new method of the feature extraction and fault diagnosis of the bearing acoustic emission signal of the wind-driven gear box, to solve the problems of the current traditional method, such as the difference of the anti-noise interference ability, the complex parameter selection and the difficult identification of the fuzzy samples, In ord to improve that accuracy of feature extraction and the correct rate of fault diagnosis. The main contents of this thesis are as follows: First, when the sound emission signal of the bearing of the wind-driven gear box is collected, the multi-fault source signal composite problem exists in the single channel. A single-channel blind source separation method based on integrated empirical mode decomposition and improved fast independent component analysis algorithm is proposed. The method comprises the following steps of: decomposing a one-dimensional single-channel composite fault sound emission signal into a multi-dimensional intrinsic mode function component through an integrated empirical mode decomposition algorithm, and then establishing the same number of input signals according to the estimated source signal number, The final input signal is separated by an improved fast independent component analysis algorithm. The method solves the problem of the problem of the single-channel signal blind source separation, overcomes the defect that the original fast independent component analysis algorithm is sensitive to the initial value, and effectively separates the composite fault (damage and fracture) acoustic emission signal. Secondly, aiming at the characteristics of non-stationarity and uncertainty of the acoustic emission signal of the wind-driven gearbox, a feature extraction method based on the complete integrated empirical mode decomposition and the characteristic entropy of the cloud model is proposed. The method comprises the following steps of: decomposing a signal into a multi-dimensional intrinsic mode function component by a complete integrated empirical mode decomposition algorithm, and reconstructing a signal by a sensitive intrinsic mode function component selected by a correlation coefficient method, And then using a reverse cloud generator to calculate the characteristic entropy of the cloud model of the reconstructed signal as the characteristic parameter of the signal. Through the experimental test and analysis, the method not only effectively extracts the characteristics of the acoustic emission signal, but also overcomes the defects of the traditional entropy method that the parameter selection is complex and the threshold value is sensitive and the like. Thirdly, in order to solve the problem of strong noise interference in the feature extraction of the acoustic emission signal of the wind-driven gear box, a feature extraction method based on the improved integrated empirical mode decomposition algorithm and the multi-scale arrangement entropy bias means is proposed. The method comprises the following steps of: firstly, selecting a sensitive intrinsic mode function component by a cloud similarity function, and then reconstructing a signal from a sensitive intrinsic mode function component, and finally calculating a multi-scale arrangement entropy partial average value of the reconstructed signal as a characteristic parameter of the signal. The method overcomes the misjudgment of the traditional method when the sensitive intrinsic mode function component is selected, reduces the noise interference, and improves the accuracy of the feature extraction. Finally, a fuzzy support vector machine fault diagnosis method based on multi-dimensional cloud model determination is proposed in order to solve the problem that a sample with uncertain factors influences the fault diagnosis rate of the bearing of the wind-driven gear box. The method adopts the multi-dimensional cloud model to determine the degree of membership of the fuzzy support vector machine algorithm, and overcomes the defect that the traditional fuzzy support vector machine algorithm cannot distinguish the noise or the field value sample from the effective sample set. The results show that the method can effectively suppress the interference of uncertain information (noise or field value samples) and has higher fault diagnosis performance.
【學位授予單位】：哈爾濱工業(yè)大學
【學位級別】：博士
【學位授予年份】：2016
【分類號】：TH133.3
【引證文獻】
相關碩士學位論文 前4條
1 馬雯萍;基于VMD的天然氣管道泄漏信號特征提取與檢測技術研究[D];東北石油大學;2018年
2 李紅賢;齒輪信號干擾下風電齒輪箱軸承早期故障診斷方法研究[D];重慶大學;2018年
3 吳文軒;基于變分模態(tài)分解的齒輪箱復合故障提取研究[D];中北大學;2018年
4 陳炳光;基于EMD和SVM煤礦通風機軸承故障診斷的研究[D];中國礦業(yè)大學;2018年
，本文編號：2509615
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