核主元分析及证据理论的多域特征故障诊断新方法研究
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摘要
随着现代液压系统向快速、大功率、高精度的方向发展,机电装备液压系统的功能越来越复杂,结构越来越庞大,不确定因素和不确定信息充斥其间。建立完善的装备液压系统维修管理体制,是降低装备故障率、提高生产率的重要手段。液压系统的早期故障检测与诊断是装备维修管理体制中“预知维修”的核心问题。近年来,智能故障诊断理论与技术发展迅速,能够对特定环境下的诊断对象进行准确的故障模式识别和预报。但是智能故障诊断领域还存在一些难题,如传感器自身的局限性,诊断对象的动态时变性,单源信号诊断信息的不完备性,装备故障常表现为多种故障的复合性等,这些都严重制约着智能故障诊断理论和技术的发展。
本文对核主元分析(KPCA)方法和D-S证据理论基本原理进行了研究。利用小波包滤波去噪的包络解调法进行信号处理,提出了基于声音信号KPCA和指数加权动态KPCA的故障诊断方法。研究了基本概率分配的确定方法,提出了集成支持向量机(SVM)与证据理论的多源信息融合故障诊断方法,该方法充分利用了各信息源的冗余互补信息,能大大提高诊断确诊率,降低诊断的误报和漏报率。试验过程中设置了泵的多种复合故障类型,把复合故障看作一种特定的故障模式,利用SVM与证据理论的融合方法对其进行直接诊断。
本文主要进行了以下几个方面的工作:
(1)为确保故障特征信息的完备性,从时域、频域和时频域中提取16个参量构建出了液压泵故障诊断的多信息域特征向量。在分析小波包和Hilbert包络解调的基本理论的基础上,提出了基于小波包滤波去噪的包络解调信号预处理方法,并用于采集到的振动和声音信号的处理。信号各频带的能量分布包含着丰富的故障信息,利用小波包分解的频带能量特征提取方法提取8个时频域的特征参量。提取了液压泵的松靴、滑靴磨损、中心弹簧失效和斜盘磨损4种常见故障下的5个时域无量纲参量(峰值指标、波形指标、脉冲指标、裕度指标和峭度指标)和3个频域特征参量(重心频率、均方频率和均方频率),并系统地分析了这8个时、频域参量对4种常见故障的敏感度。
(2)提出了利用泵运行声音信号KPCA的故障诊断方法。阐述了KPCA法的基本原理及其应用于故障诊断时的基本步骤,给出了声音信号的预处理过程,提取了由时域、频域和时频域参量共同构成的多域特征向量,利用KPCA法进行了故障诊断。分别与单纯时域、频域和时频域参量的特征向量诊断结果进行了对比,并与利用泵端盖振动信号故障诊断的结果进行了对比。
(3)提出了指数加权动态KPCA的故障诊断方法。针对液压泵运行状态具有动态时变性的特点,采用滑动时间窗口的数据更新方法,利用新数据建立了新核主元模型,引入了指数加权因子,由新模型和旧模型加权共同构造出了具有动态自适应性的诊断模型。阐述了该方法的建模和诊断步骤,利用振动信号完成了泵的故障诊断。讨论了加权因子取值对诊断结果的影响,并与传统的核主元方法得到的诊断结果作了对比。
(4)提出了集成SVM与D-S证据理论的多源信息融合故障诊断方法。利用SVM确定了基本概率分配,给出了证据理论诊断故障的基本步骤,监测了泵出口压力,x、y、z向振动和声音的5通道信号,设置了故障模式的识别框架,设定了采集参数,通过对各通道信号的预处理,提取了多域特征向量,实现了对液压泵的多种故障诊断。与已有的BP神经网络确定基本概率分配的方法作了对比,并研究了在缺失某一通道信息时,该方法的容错能力。
(5)对液压泵的复合故障进行了直接诊断。试验时设置了泵的多种复合故障类型,把复合故障看作一种特定的故障模式,利用SVM与证据理论融合的方法对其进行了故障诊断。
(6)提出了基于虚拟仪器的试验系统组成方案,搭建了5通道轴向柱塞泵运行状态监测系统。选择泵的3个相互垂直方向的振动信号,以及声音信号、出口压力信号作为监测信号,分析了泵的各种故障状态的振动特征频率范围和故障分析频带,选择了合适的传感器,研究了如何合理选择振动传感器和声级计的测点。设置了泵多种故障模式和相应采集参数,为各种故障诊断方法进行诊断奠定了基础。
With the development of modern hydraulic system for the direction of high-speed, high power and high reliability, the function of electromechanical equipment hydraulic system gets more complicated than before, the structure of it gets bigger and at the same time lots of uncertain factor and information appears. To reduce the fault occurring and improve the productivity, it is very important to build a perfect system of equipment hydraulic system's maintenance management. Therefore, the early phase of the hydraulic system’s fault diagnosis and detecting becomes the core problem in the predictive maintenance of the equipment management system. In recent years, the theory and technology of intelligent fault diagnosis has made a big progress which now can help people identify the pattern precisely and predict the fault in given conditions. But there are still some difficulties to be solved in this field, such as the limitation of the sensor, the time-variation of fault diagnostic object, the Incompleteness based on the one-source signal, the compound of equipment fault. They are all the factors preventing the development of the theory and technology of intelligent fault diagnosis.
Kernel principal component analysis (KPCA) and D-S evidence theory are analyzed in this paper. The denoising envelop demodulation method is used to process the signal based on wavelet packet, and KPCA fault diagnosis based on noise signal and fault diagnosis method based on exponentially weighted dynamic KPCA are presented here. Methods are studies how to settle basic probability assignment, multi-source information fusion fault diagnosis method which combined Support Vector Machine (SVM) and evidence theory is proposed in this paper. This method makes full use of complementary and redundant information of all signal sources can improve the precise and reduce the rate of misdeclaration and missing report of fault diagnosis remarkably. Diversified compound fault patterns are set in experiment which can be seen as a special fault pattern and then the pattern was diagnosed directly by multi-source information fault diagnosis method which combined Support Vector Machine (SVM) and evidence theory.
The main works in this dissertation are showed as follows:
(1)To ensure the completeness of feature fault information, 16 feature parameters from the time, frequency and time-frequency domain are selected to constitute multi-domain feature vector in fault diagnosis of the hydraulic pump. On the basis of analyzing the basic theory of wavelet packets and envelope demodulation algorithm based on Hilbert transform, the pretreatment method of envelop demodulation based on wavelet packets band-pass filtering denoising is put forward. Then the method is used to process the gathered vibration and sound signal. The energy of each frequency band contains much fault information. Method of extracting band energy based on wavelet packet decomposition is given and 8 feature parameters are extracted in time-frequency domain. 5 dimensionless parameters in time domain and 3 feature parameters in frequency domain under 4 common faults are presented, it also analyses the degree of the parameters’sensitivity to these 4 faults.
(2)The fault diagnosis method of KPCA based on sound signal of pump is expatiated. The basic theory of KPCA and its basic procedure when applied in the fault diagnosis is introduced. The detailed pre-processing procedure of the sound signal is presented. Feature vector in multi-information domains is extracted from time domain, frequency domain and time-frequency domain. At last fault diagnosis is carried out by KPCA. The diagnosis result is compared with the single feature vector of parameters in time domain, frequency and the time-frequency respectively, and then compared with diagnosis result based on vibration signal.
(3)Fault diagnosis method based on exponentially weighted dynamic KPCA is proposed. Due to the dynamic and time varying state of the normal hydraulic pump, it adopts the refresh method of date by time sliding window, sets up new KPCA mode with the new date. It introduces exponential weighting coefficient, constructs a diagnosis model featured dynamic self-adaptability by using both the new and the old model. It then elaborates in detail its procedure of modeling and diagnosis, makes the diagnosis based on vibration signal, discusses the exponential weighting coefficient’s influence on the diagnosis, and then makes a contrast with the traditional KPCA diagnosis result.
(4)Multi-source information fault diagnosis method based on combining Support Vector Machine (SVM) and D-S evidence theory is proposed. The basic probability assignment is settled by using SVM, the basic step of fault diagnosis of D-S evidence theory is given. 5 channel Signals are monitored, which include 3 channel vibration signals in the x-axis, y-axis, z-axis, sound signal and outlet pressure signal. Recognition framework of fault diagnosis and the acquisition parameters are set, feature vector is extracted in multi-domain to complete the fault diagnosis of hydraulic pump, after signals are pre-processed. The result is compared in detail with the basic probability assignment acquired by the method of BP-neural network, and fault-tolerant capability of the new method when losing one certain channel is studied.
(5)Compound faults of the hydraulic pump are diagnosed directly. The experiment puts the pump in the state of compound faults, and compound fault is seen as a special pattern of fault. Then diagnosis can be executed by using both SVM and D-S evidence theory fault diagnosis method.
(6)The scheme is proposed of experimental system based on virtual instrument, system of 5 channels’axial piston pump operation monitoring is established. The system chooses 3 orthogonal components, that is vibration signal, sound signal and outlet pressure signal as its monitor signal. It analyses the vibration state's frequency range and the fault analysis frequency band of every pump and selects suitable sensors. It studies where to place the stations of the vibration sensor and sound sensor. Many faults and collecting parameters are given, the foundations of fault diagnosis methods are proposed.
本文对核主元分析(KPCA)方法和D-S证据理论基本原理进行了研究。利用小波包滤波去噪的包络解调法进行信号处理,提出了基于声音信号KPCA和指数加权动态KPCA的故障诊断方法。研究了基本概率分配的确定方法,提出了集成支持向量机(SVM)与证据理论的多源信息融合故障诊断方法,该方法充分利用了各信息源的冗余互补信息,能大大提高诊断确诊率,降低诊断的误报和漏报率。试验过程中设置了泵的多种复合故障类型,把复合故障看作一种特定的故障模式,利用SVM与证据理论的融合方法对其进行直接诊断。
本文主要进行了以下几个方面的工作:
(1)为确保故障特征信息的完备性,从时域、频域和时频域中提取16个参量构建出了液压泵故障诊断的多信息域特征向量。在分析小波包和Hilbert包络解调的基本理论的基础上,提出了基于小波包滤波去噪的包络解调信号预处理方法,并用于采集到的振动和声音信号的处理。信号各频带的能量分布包含着丰富的故障信息,利用小波包分解的频带能量特征提取方法提取8个时频域的特征参量。提取了液压泵的松靴、滑靴磨损、中心弹簧失效和斜盘磨损4种常见故障下的5个时域无量纲参量(峰值指标、波形指标、脉冲指标、裕度指标和峭度指标)和3个频域特征参量(重心频率、均方频率和均方频率),并系统地分析了这8个时、频域参量对4种常见故障的敏感度。
(2)提出了利用泵运行声音信号KPCA的故障诊断方法。阐述了KPCA法的基本原理及其应用于故障诊断时的基本步骤,给出了声音信号的预处理过程,提取了由时域、频域和时频域参量共同构成的多域特征向量,利用KPCA法进行了故障诊断。分别与单纯时域、频域和时频域参量的特征向量诊断结果进行了对比,并与利用泵端盖振动信号故障诊断的结果进行了对比。
(3)提出了指数加权动态KPCA的故障诊断方法。针对液压泵运行状态具有动态时变性的特点,采用滑动时间窗口的数据更新方法,利用新数据建立了新核主元模型,引入了指数加权因子,由新模型和旧模型加权共同构造出了具有动态自适应性的诊断模型。阐述了该方法的建模和诊断步骤,利用振动信号完成了泵的故障诊断。讨论了加权因子取值对诊断结果的影响,并与传统的核主元方法得到的诊断结果作了对比。
(4)提出了集成SVM与D-S证据理论的多源信息融合故障诊断方法。利用SVM确定了基本概率分配,给出了证据理论诊断故障的基本步骤,监测了泵出口压力,x、y、z向振动和声音的5通道信号,设置了故障模式的识别框架,设定了采集参数,通过对各通道信号的预处理,提取了多域特征向量,实现了对液压泵的多种故障诊断。与已有的BP神经网络确定基本概率分配的方法作了对比,并研究了在缺失某一通道信息时,该方法的容错能力。
(5)对液压泵的复合故障进行了直接诊断。试验时设置了泵的多种复合故障类型,把复合故障看作一种特定的故障模式,利用SVM与证据理论融合的方法对其进行了故障诊断。
(6)提出了基于虚拟仪器的试验系统组成方案,搭建了5通道轴向柱塞泵运行状态监测系统。选择泵的3个相互垂直方向的振动信号,以及声音信号、出口压力信号作为监测信号,分析了泵的各种故障状态的振动特征频率范围和故障分析频带,选择了合适的传感器,研究了如何合理选择振动传感器和声级计的测点。设置了泵多种故障模式和相应采集参数,为各种故障诊断方法进行诊断奠定了基础。
With the development of modern hydraulic system for the direction of high-speed, high power and high reliability, the function of electromechanical equipment hydraulic system gets more complicated than before, the structure of it gets bigger and at the same time lots of uncertain factor and information appears. To reduce the fault occurring and improve the productivity, it is very important to build a perfect system of equipment hydraulic system's maintenance management. Therefore, the early phase of the hydraulic system’s fault diagnosis and detecting becomes the core problem in the predictive maintenance of the equipment management system. In recent years, the theory and technology of intelligent fault diagnosis has made a big progress which now can help people identify the pattern precisely and predict the fault in given conditions. But there are still some difficulties to be solved in this field, such as the limitation of the sensor, the time-variation of fault diagnostic object, the Incompleteness based on the one-source signal, the compound of equipment fault. They are all the factors preventing the development of the theory and technology of intelligent fault diagnosis.
Kernel principal component analysis (KPCA) and D-S evidence theory are analyzed in this paper. The denoising envelop demodulation method is used to process the signal based on wavelet packet, and KPCA fault diagnosis based on noise signal and fault diagnosis method based on exponentially weighted dynamic KPCA are presented here. Methods are studies how to settle basic probability assignment, multi-source information fusion fault diagnosis method which combined Support Vector Machine (SVM) and evidence theory is proposed in this paper. This method makes full use of complementary and redundant information of all signal sources can improve the precise and reduce the rate of misdeclaration and missing report of fault diagnosis remarkably. Diversified compound fault patterns are set in experiment which can be seen as a special fault pattern and then the pattern was diagnosed directly by multi-source information fault diagnosis method which combined Support Vector Machine (SVM) and evidence theory.
The main works in this dissertation are showed as follows:
(1)To ensure the completeness of feature fault information, 16 feature parameters from the time, frequency and time-frequency domain are selected to constitute multi-domain feature vector in fault diagnosis of the hydraulic pump. On the basis of analyzing the basic theory of wavelet packets and envelope demodulation algorithm based on Hilbert transform, the pretreatment method of envelop demodulation based on wavelet packets band-pass filtering denoising is put forward. Then the method is used to process the gathered vibration and sound signal. The energy of each frequency band contains much fault information. Method of extracting band energy based on wavelet packet decomposition is given and 8 feature parameters are extracted in time-frequency domain. 5 dimensionless parameters in time domain and 3 feature parameters in frequency domain under 4 common faults are presented, it also analyses the degree of the parameters’sensitivity to these 4 faults.
(2)The fault diagnosis method of KPCA based on sound signal of pump is expatiated. The basic theory of KPCA and its basic procedure when applied in the fault diagnosis is introduced. The detailed pre-processing procedure of the sound signal is presented. Feature vector in multi-information domains is extracted from time domain, frequency domain and time-frequency domain. At last fault diagnosis is carried out by KPCA. The diagnosis result is compared with the single feature vector of parameters in time domain, frequency and the time-frequency respectively, and then compared with diagnosis result based on vibration signal.
(3)Fault diagnosis method based on exponentially weighted dynamic KPCA is proposed. Due to the dynamic and time varying state of the normal hydraulic pump, it adopts the refresh method of date by time sliding window, sets up new KPCA mode with the new date. It introduces exponential weighting coefficient, constructs a diagnosis model featured dynamic self-adaptability by using both the new and the old model. It then elaborates in detail its procedure of modeling and diagnosis, makes the diagnosis based on vibration signal, discusses the exponential weighting coefficient’s influence on the diagnosis, and then makes a contrast with the traditional KPCA diagnosis result.
(4)Multi-source information fault diagnosis method based on combining Support Vector Machine (SVM) and D-S evidence theory is proposed. The basic probability assignment is settled by using SVM, the basic step of fault diagnosis of D-S evidence theory is given. 5 channel Signals are monitored, which include 3 channel vibration signals in the x-axis, y-axis, z-axis, sound signal and outlet pressure signal. Recognition framework of fault diagnosis and the acquisition parameters are set, feature vector is extracted in multi-domain to complete the fault diagnosis of hydraulic pump, after signals are pre-processed. The result is compared in detail with the basic probability assignment acquired by the method of BP-neural network, and fault-tolerant capability of the new method when losing one certain channel is studied.
(5)Compound faults of the hydraulic pump are diagnosed directly. The experiment puts the pump in the state of compound faults, and compound fault is seen as a special pattern of fault. Then diagnosis can be executed by using both SVM and D-S evidence theory fault diagnosis method.
(6)The scheme is proposed of experimental system based on virtual instrument, system of 5 channels’axial piston pump operation monitoring is established. The system chooses 3 orthogonal components, that is vibration signal, sound signal and outlet pressure signal as its monitor signal. It analyses the vibration state's frequency range and the fault analysis frequency band of every pump and selects suitable sensors. It studies where to place the stations of the vibration sensor and sound sensor. Many faults and collecting parameters are given, the foundations of fault diagnosis methods are proposed.
引文
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