Vibration acceleration signals are often measured from case surface of arunning machine to monitor its condition. If the measured vibration signals display to have periodicimpulse components with a certain frequency, ...Vibration acceleration signals are often measured from case surface of arunning machine to monitor its condition. If the measured vibration signals display to have periodicimpulse components with a certain frequency, there may exist a corresponding local fault in themachine, and if further extracting the periodic impulse components from the vibration signals, theseverity of the local fault can be estimated and tracked. However, the signal-to-noise ratios (SNRs)of the vibration acceleration signals are often so small that the periodic impulse components aresubmersed in much background noises and other components, and it is difficult or inconvenient for usto detect and extract the periodic impulse components with the current common analyzing methods forvibration signals. Therefore, another technique, called singular value decomposition (SVD), istried to be introduced to solve the problem. First, the principle of detecting and extracting thesignal periodic components using singular value decomposition is summarized and discussed. Second,the infeasibility of the direct use of the existing SVD based detecting and extracting approach ispointed out. Third, the approach to construct the matrix for SVD from the signal series is improvedlargely, which is the key program to improve the SVD technique; Other associated improvement is alsoproposed. Finally, a simulating application example and a real-life application example ondetecting and extracting the periodic impulse components are given, which showed that the introducedand improved SVD technique is feasible.展开更多
Targeting the non-stationary characteristics of diesel engine vibration signals and the limitations of singular value decomposition(SVD) technique, a new method based on improved local mean decomposition(LMD), SVD tec...Targeting the non-stationary characteristics of diesel engine vibration signals and the limitations of singular value decomposition(SVD) technique, a new method based on improved local mean decomposition(LMD), SVD technique and relevance vector machine(RVM) was proposed for the identification of diesel valve fault in this study. Firstly, the vibration signals were acquired through the vibration sensors installed on the cylinder head in one normal state and four fault states of valve trains. Secondly, an improved LMD method was used to decompose the non-stationary signals into a set of stationary product functions(PF), from which the initial feature vector matrices can be formed automatically. Then, the singular values were obtained by applying the SVD technique to the initial feature vector matrixes. Finally, slant binary tree and sort separability criterion were combined to determine the structure of multi-class RVM, and the singular values were regarded as the fault feature vectors of RVM in the identification of fault types of diesel valve clearance. The experimental results showed that the proposed fault diagnosis method can effectively extract the features of diesel valve clearance and identify the diesel valve fault accurately.展开更多
Condition monitoring and fault diagnosis of gearboxes play an important role in the maintenance of mechanical systems.The vibration signal of gearboxes is characterized by complex spectral structure and strong time va...Condition monitoring and fault diagnosis of gearboxes play an important role in the maintenance of mechanical systems.The vibration signal of gearboxes is characterized by complex spectral structure and strong time variability,which brings challenges to fault feature extraction.To address this issue,a new demodulation technique,based on the Fourier decomposition method and resonance demodulation,is proposed to extract fault-related information.First,the Fourier decomposition method decomposes the vibration signal into Fourier intrinsic band functions(FIBFs)adaptively in the frequency domain.Then,the original signal is segmented into short-time vectors to construct double-row matrices and the maximum singular value ratio method is employed to estimate the resonance frequency.Then,the resonance frequency is used as a criterion to guide the selection of the most relevant FIBF for demodulation analysis.Finally,for the optimal FIBF,envelope demodulation is conducted to identify the fault characteristic frequency.The main contributions are that the proposed method describes how to obtain the resonance frequency effectively and how to select the optimal FIBF after decomposition in order to extract the fault characteristic frequency.Both numerical and experimental studies are conducted to investigate the performance of the proposed method.It is demonstrated that the proposed method can effectively demodulate the fault information from the original signal.展开更多
This paper considers robust fault detection and diagnosis for input uncertain nonlinear systems. It proposes a multi-objective fault detection criterion so that the fault residual is sensitive to the fault but insensi...This paper considers robust fault detection and diagnosis for input uncertain nonlinear systems. It proposes a multi-objective fault detection criterion so that the fault residual is sensitive to the fault but insensitive to the uncertainty as much as possible. Then the paper solves the proposed criterion by maximizing the smallest singular value of the transformation from faults to fault detection residuals while minimizing the largest singular value of the transformation from input uncertainty to the fault detection residuals. This method is applied to an aircraft which has a fault in the left elevator or rudder. The simulation results show the proposed method can detect the control surface failures rapidly and efficiently.展开更多
针对水电机组振动信号故障特征提取难,提出一种融合小波变换(Wavelet Transform,WT)和奇异值分解(Singular Value Decomposition,SVD)相结合的故障特征提取方法。首先,通过小波阈值降噪消除强噪声对模型特征提取的干扰,再利用小波变换...针对水电机组振动信号故障特征提取难,提出一种融合小波变换(Wavelet Transform,WT)和奇异值分解(Singular Value Decomposition,SVD)相结合的故障特征提取方法。首先,通过小波阈值降噪消除强噪声对模型特征提取的干扰,再利用小波变换将降噪信号分解成不同频率的模态子序列,应用SVD理论提起子序列的SVD值作为特征,最终将特征输入RF模型中实现水电机组故障的快速识别与诊断。通过在公开数据集和真实机组案例中应用,验证了对水电机组故障诊断的高效性。展开更多
奇异值分解(Singular value decomposition,SVD)作为一种有效的信号降噪方法广泛应用于旋转机械振动信号周期性瞬态冲击提取中。传统SVD以能量为导向,无法提取出能量较弱但含故障信息丰富的奇异分量(Singular Component,SC)。为此,提出...奇异值分解(Singular value decomposition,SVD)作为一种有效的信号降噪方法广泛应用于旋转机械振动信号周期性瞬态冲击提取中。传统SVD以能量为导向,无法提取出能量较弱但含故障信息丰富的奇异分量(Singular Component,SC)。为此,提出加权firm阈值奇异值分解(Weighted Firm Singular Value Decomposition,WFSVD)方法。该方法首先引入平方包络谱峭度(Squared Envelope Spectrum Kurtosis,SESK)作为量化故障敏感度的指标,以评估各个SC所含故障信息的丰富程度;其次,将SESK作为权重因子引入到基于firm阈值的SC去噪中,设计基于SESK的加权firm阈值SC去噪策略;最后,重构信号,实现信号降噪并有效提取故障特征。对于仿真信号与试验数据的分析验证了所提方法在周期性微弱瞬态冲击提取及旋转机械故障诊断中的有效性。展开更多
基金This project is supported by National Natural Science Foundation of China (No.59905011, 60275041).
文摘Vibration acceleration signals are often measured from case surface of arunning machine to monitor its condition. If the measured vibration signals display to have periodicimpulse components with a certain frequency, there may exist a corresponding local fault in themachine, and if further extracting the periodic impulse components from the vibration signals, theseverity of the local fault can be estimated and tracked. However, the signal-to-noise ratios (SNRs)of the vibration acceleration signals are often so small that the periodic impulse components aresubmersed in much background noises and other components, and it is difficult or inconvenient for usto detect and extract the periodic impulse components with the current common analyzing methods forvibration signals. Therefore, another technique, called singular value decomposition (SVD), istried to be introduced to solve the problem. First, the principle of detecting and extracting thesignal periodic components using singular value decomposition is summarized and discussed. Second,the infeasibility of the direct use of the existing SVD based detecting and extracting approach ispointed out. Third, the approach to construct the matrix for SVD from the signal series is improvedlargely, which is the key program to improve the SVD technique; Other associated improvement is alsoproposed. Finally, a simulating application example and a real-life application example ondetecting and extracting the periodic impulse components are given, which showed that the introducedand improved SVD technique is feasible.
基金Supported by the National High Technology Research and Development Program of China("863" Program,No.2014AA041501)
文摘Targeting the non-stationary characteristics of diesel engine vibration signals and the limitations of singular value decomposition(SVD) technique, a new method based on improved local mean decomposition(LMD), SVD technique and relevance vector machine(RVM) was proposed for the identification of diesel valve fault in this study. Firstly, the vibration signals were acquired through the vibration sensors installed on the cylinder head in one normal state and four fault states of valve trains. Secondly, an improved LMD method was used to decompose the non-stationary signals into a set of stationary product functions(PF), from which the initial feature vector matrices can be formed automatically. Then, the singular values were obtained by applying the SVD technique to the initial feature vector matrixes. Finally, slant binary tree and sort separability criterion were combined to determine the structure of multi-class RVM, and the singular values were regarded as the fault feature vectors of RVM in the identification of fault types of diesel valve clearance. The experimental results showed that the proposed fault diagnosis method can effectively extract the features of diesel valve clearance and identify the diesel valve fault accurately.
基金supported by the National Key R&D Program of China(No.2019YFB2004604)the National Natural Science Foundation of China(No.52075477)the Key R&D Program of Zhejiang Province(No.2021C01139),China。
文摘Condition monitoring and fault diagnosis of gearboxes play an important role in the maintenance of mechanical systems.The vibration signal of gearboxes is characterized by complex spectral structure and strong time variability,which brings challenges to fault feature extraction.To address this issue,a new demodulation technique,based on the Fourier decomposition method and resonance demodulation,is proposed to extract fault-related information.First,the Fourier decomposition method decomposes the vibration signal into Fourier intrinsic band functions(FIBFs)adaptively in the frequency domain.Then,the original signal is segmented into short-time vectors to construct double-row matrices and the maximum singular value ratio method is employed to estimate the resonance frequency.Then,the resonance frequency is used as a criterion to guide the selection of the most relevant FIBF for demodulation analysis.Finally,for the optimal FIBF,envelope demodulation is conducted to identify the fault characteristic frequency.The main contributions are that the proposed method describes how to obtain the resonance frequency effectively and how to select the optimal FIBF after decomposition in order to extract the fault characteristic frequency.Both numerical and experimental studies are conducted to investigate the performance of the proposed method.It is demonstrated that the proposed method can effectively demodulate the fault information from the original signal.
基金supported by the National Natural Science Foundation of China(60328304)the"111"project of Beihang University (B07009)
文摘This paper considers robust fault detection and diagnosis for input uncertain nonlinear systems. It proposes a multi-objective fault detection criterion so that the fault residual is sensitive to the fault but insensitive to the uncertainty as much as possible. Then the paper solves the proposed criterion by maximizing the smallest singular value of the transformation from faults to fault detection residuals while minimizing the largest singular value of the transformation from input uncertainty to the fault detection residuals. This method is applied to an aircraft which has a fault in the left elevator or rudder. The simulation results show the proposed method can detect the control surface failures rapidly and efficiently.
文摘针对水电机组振动信号故障特征提取难,提出一种融合小波变换(Wavelet Transform,WT)和奇异值分解(Singular Value Decomposition,SVD)相结合的故障特征提取方法。首先,通过小波阈值降噪消除强噪声对模型特征提取的干扰,再利用小波变换将降噪信号分解成不同频率的模态子序列,应用SVD理论提起子序列的SVD值作为特征,最终将特征输入RF模型中实现水电机组故障的快速识别与诊断。通过在公开数据集和真实机组案例中应用,验证了对水电机组故障诊断的高效性。
