基于HHT变换的起动电动机特征信号时频分析

针对车辆起动电动机电气和机械故障发生时特征信号的时变不平稳特性,进行了时频域分析处理,提出了利用现代信号处理方法对故障信号提取特征向量的方法,主要对起动电动机的电枢和轴承故障进行诊断。在构建电机故障测试实验平台的基础上,利用破坏性实验构造了故障类型,测取了电枢电流和振动信号,分别采用小波分析理论和HHT变换对信号进行分析,通过分解再重构的方式将信号分解成了频率由高到低的不同分量,并获得了故障的特征频率,提取了特征向量。实验结果表明,基于HHT变换的现代信号处理方法在处理时变非平稳信号方面比小波分析理论更具有自适应性,更易识别。     

Direction finding and suppression of vector-scalar sound signals in shallow water taking into account their correlation and mode structure

The correlation of low-frequency sound signals from towed tonal low-frequency sources at the output of the scalar and vector channels is studied in shallow water. The correlation of the scalar field and signal received by a horizontally oriented vector receiver on average is 0.92–0.99; correlation with the signal received by a vertical vector receiver decreases to 0.66–85. When scalar fields or horizontal projections of the vibration velocity vector with application of the aperture synthesis algorithm are used, 3–5 normal waves are isolated; when the vertical component is used, 7–9 modes. It is demonstrated that the high signal correlation ensures direction-finding accuracy and suppression of strongly noise-emitting moving sources by 20–30 dB or more if the cardioid is directed at the source according to the zone of the minimum.     

Research on Fault Diagnosis of Rolling Bearings Based on Variational Mode Decomposition Improved by the Niche Genetic Algorithm

Due to the influence of signal-to-noise ratio in the early failure stage of rolling bearings in rotating machinery, it is difficult to effectively extract feature information. Variational Mode Decomposition (VMD) has been widely used to decompose vibration signals which can reflect more fault omens. In order to improve the efficiency and accuracy, a method to optimize VMD by using the Niche Genetic Algorithm (NGA) is proposed in this paper. In this method, the optimal Shannon entropy of modal components in a VMD algorithm is taken as the optimization objective, by using the NGA to constantly update and optimize the combination of influencing parameters composed of α and K so as to minimize the local minimum entropy. According to the obtained optimization results, the optimal input parameters of the VMD algorithm were set. The method mentioned is applied to the fault extraction of a simulated signal and a measured signal of a rolling bearing. The decomposition process of the rolling-bearing fault signal was transferred to the variational frame by the NGA-VMD algorithm, and several eigenmode function components were obtained. The energy feature extracted from the modal component containing the main fault information was used as the input vector of a particle swarm optimized support vector machine (PSO-SVM) and used to identify the fault type of the rolling bearing. The analysis results of the simulation signal and measured signal show that: the NGA-VMD algorithm can decompose the vibration signal of a rolling bearing accurately and has a better robust performance and correct recognition rate than the VMD algorithm. It can highlight the local characteristics of the original sample data and reduce the interference of the parameters selected artificially in the VMD algorithm on the processing results, improving the fault-diagnosis efficiency of rolling bearings.     

A Novel Fault Diagnosis Method for Rolling Bearing Based on Hierarchical Refined Composite Multiscale Fluctuation-Based Dispersion Entropy and PSO-ELM

This paper proposes a novel fault diagnosis method for rolling bearing based on hierarchical refined composite multiscale fluctuation-based dispersion entropy (HRCMFDE) and particle swarm optimization-based extreme learning machine (PSO-ELM). First, HRCMFDE is used to extract fault features in the vibration signal at different time scales. By introducing the hierarchical theory algorithm into the vibration signal decomposition process, the problem of missing high-frequency signals in the coarse-grained process is solved. Fluctuation-based dispersion entropy (FDE) has the characteristics of insensitivity to noise interference and high computational efficiency based on the consideration of nonlinear time series fluctuations, which makes the extracted feature vectors more effective in describing the fault information embedded in each frequency band of the vibration signal. Then, PSO is used to optimize the input weights and hidden layer neuron thresholds of the ELM model to improve the fault identification capability of the ELM classifier. Finally, the performance of the proposed rolling bearing fault diagnosis method is verified and analyzed by using the CWRU dataset and MFPT dataset as experimental cases, respectively. The results show that the proposed method has high identification accuracy for the fault diagnosis of rolling bearings with varying loads and has a good load migration effect.     

基于矢量阵的运动声源柱面聚焦定位方法试验研究

本文基于被动合成孔径原理, 在建立运动声源矢量阵近场柱面聚焦测量模型的基础上, 分别研究了适用于单频线谱信号和宽带连续谱信号的矢量阵柱面聚焦定位方法, 通过数值仿真计算了该方法在多种误差条件下的定位精度, 并进一步通过舱段模型试验对该方法的工程实用性和正确性进行了详细的分析和论证. 舱段模型试验结果表明, 柱面聚焦定位结果与壳体振动能量分布规律符合较好, 该方法不仅能真实反映声源位置信息, 而且能反映不同频带内声源能量分布的相对大小, 具有良好的定位效果.     

A Novel Method for Fault Diagnosis of Rotating Machinery

When rotating machinery fails, the consequent vibration signal contains rich fault feature information. However, the vibration signal bears the characteristics of nonlinearity and nonstationarity, and is easily disturbed by noise, thus it may be difficult to accurately extract hidden fault features. To extract effective fault features from the collected vibration signals and improve the diagnostic accuracy of weak faults, a novel method for fault diagnosis of rotating machinery is proposed. The new method is based on Fast Iterative Filtering (FIF) and Parameter Adaptive Refined Composite Multiscale Fluctuation-based Dispersion Entropy (PARCMFDE). Firstly, the collected original vibration signal is decomposed by FIF to obtain a series of intrinsic mode functions (IMFs), and the IMFs with a large correlation coefficient are selected for reconstruction. Then, a PARCMFDE is proposed for fault feature extraction, where its embedding dimension and class number are determined by Genetic Algorithm (GA). Finally, the extracted fault features are input into Fuzzy C-Means (FCM) to classify different states of rotating machinery. The experimental results show that the proposed method can accurately extract weak fault features and realize reliable fault diagnosis of rotating machinery.     

Sound radiation into air by a point source moving underwater

Based on the discovery that the majority of radiated energy of a stationary sound source in shallow water is into the air at infrasonic frequencies, the sound transmission into air from a point source moving underwater is investigated in this letter. It is found that a moving sound source can radiate more acoustic energy into the air than a stationary one and the amount of energy radiated into the air increases with the speed of the moving source. Simulations show that the sound transmission into air is dominated by the inhomogeneous waves generated by the moving source.     

A Sound Field Separation and Reconstruction Technique Based on Reciprocity Theorem and Fourier Transform

We show a method to separate the sound field radiated by a signal source from the sound field radiated by noise sources and to reconstruct the sound field radiated by the signal source. The proposed method is based on reciprocity theorem and the Fourier transform. Both the sound field and its gradient on a measurement surface are needed in the method. Evanescent waves are considered in the method, which ensures a high resolution reconstruction in the near field region of the signal source when evanescent waves can be measured. A simulation is given to verify the method and the influence of measurement noise on the method is discussed.     

A restoration procedure for (non-stationary) signals from moving sources

Moving sources, such as trains, cars and airplanes, provide non-stationary sound and vibration signals in situations where the receiver is not moving with the source. For non-stationary signals there are strong limitations on the use of computerized analysis techniques based on Fourier transformation. For instance, it is not possible to compute reliably either power spectral density functions or coherence functions. A procedure has been developed, and is discussed in this paper, that restores non-stationary signals into stationary ones, thus enabling one to apply the analysis techniques mentioned above to moving source data with a reliable outcome.     

浅海单矢量传感器宽带声源三维定位仿真

针对浅海水平不变波导中利用单矢量传感器的低频宽带声源三维被动定位问题,首先利用平均声强器估计声源方位;其次,通过分离后的简正模声压和水平振速分量联合处理获得运动声源相对速度,进一步基于垂直复声强中简正模相干项特征频率不变性以及长时间窗口中多快拍信号的统一处理,建立以warping变换归一化频谱强度和作为代价函数的距离搜索处理器,估计该段信号的初始距离,进而获得各时刻声源距离,所提出的方法避免了对拷贝声场和引导声源的依赖。信噪比大于10 dB时,相对误差在11.33%之内;最后,利用多阶简正模相干项与非相干项能量的模基处理方法,当声场中存在三阶以上简正模时,对声源深度进行匹配估计。仿真分析表明,单个矢量传感器能够完成声源的方位、距离及深度的三维定位。     

电力变压器绕组振动声纹特性分析*

为更加准确分析变压器绕组的状态特征,本文提出一种基于多物理场耦合仿真的变压器绕组振动声纹特性分析方法。根据实验条件,建立变压器绕组振动噪声模型,考虑变压器绝缘油在噪声传播过程中的作用,对S13-M-200/10型号的油浸式变压器进行短路实验,测量油箱表面的振动加速度以及周围空间的声音信号。仿真结果与实测数据对比分析,油箱表面的振动加速度集中频率为100Hz,空间声音信号集中频率为100Hz和200Hz,验证仿真模型的有效性。最后,建立变压器机械故障的仿真模型,分析得到变压器发生机械故障时,声音信号中100Hz频率分量减少,200Hz频率分量增加,为变压器绕组故障诊断提供依据。     

Vibration signal models for fault diagnosis of planetary gearboxes

A thorough understanding of the spectral structure of planetary gear system vibration signals is helpful to fault diagnosis of planetary gearboxes. Considering both the amplitude modulation and the frequency modulation effects due to gear damage and periodically time variant working condition, as well as the effect of vibration transfer path, signal models of gear damage for fault diagnosis of planetary gearboxes are given and the spectral characteristics are summarized in closed form. Meanwhile, explicit equations for calculating the characteristic frequency of local and distributed gear fault are deduced. The theoretical derivations are validated using both experimental and industrial signals. According to the theoretical basis derived, manually created local gear damage of different levels and naturally developed gear damage in a planetary gearbox can be detected and located.     

Low-Pass Filtering Empirical Wavelet Transform Machine Learning Based Fault Diagnosis for Combined Fault of Wind Turbines

Fault diagnosis of wind turbines is of great importance to reduce operating and maintenance costs of wind farms. At present, most wind turbine fault diagnosis methods are focused on single faults, and the methods for combined faults usually depend on inefficient manual analysis. Filling the gap, this paper proposes a low-pass filtering empirical wavelet transform (LPFEWT) machine learning based fault diagnosis method for combined fault of wind turbines, which can identify the fault type of wind turbines simply and efficiently without human experience and with low computation costs. In this method, low-pass filtering empirical wavelet transform is proposed to extract fault features from vibration signals, LPFEWT energies are selected to be the inputs of the fault diagnosis model, a grey wolf optimizer hyperparameter tuned support vector machine (SVM) is employed for fault diagnosis. The method is verified on a wind turbine test rig that can simulate shaft misalignment and broken gear tooth faulty conditions. Compared with other models, the proposed model has superiority for this classification problem.     

Subway Gearbox Fault Diagnosis Algorithm Based on Adaptive Spline Impact Suppression

In the signal processing of real subway vehicles, impacts between wheelsets and rail joint gaps have significant negative effects on the spectrum. This introduces great difficulties for the fault diagnosis of gearboxes. To solve this problem, this paper proposes an adaptive time-domain signal segmentation method that envelopes the original signal using a cubic spline interpolation. The peak values of the rail joint gap impacts are extracted to realize the adaptive segmentation of gearbox fault signals when the vehicle was moving at a uniform speed. A long-time and unsteady signal affected by wheel–rail impacts is segmented into multiple short-term, steady-state signals, which can suppress the high amplitude of the shock response signal. Finally, on this basis, multiple short-term sample signals are analyzed by time- and frequency-domain analyses and compared with the nonfaulty results. The results showed that the method can efficiently suppress the high-amplitude components of subway gearbox vibration signals and effectively extract the characteristics of weak faults due to uniform wear of the gearbox in the time and frequency domains. This provides reference value for the gearbox fault diagnosis in engineering practice.     

Combine Harvester Bearing Fault-Diagnosis Method Based on SDAE-RCmvMSE

In the fault monitoring of rolling bearings, there is always loud noise, leading to poor signal stationariness. How to accurately and efficiently identify the fault type of rolling bearings is a challenge. Based on multivariate multiscale sample entropy (mvMSE), this paper introduces the refined composite mvMSE (RCmvMSE) into the fault extraction of the rolling bearing. A rolling bearing fault-diagnosis method based on stacked auto encoder and RCmvMSE (SDAE-RCmvMSE) is proposed. In the actual environment, the fault-diagnosis method use the multichannel vibration signals of the bearing as the input of stacked denoising autoencoders (SDAEs) to filter the noise of the vibration signals. The features of denoise signals are extracted by RCmvMSE and the rolling bearing operation-state diagnosis is completed with a support-vector machine (SVM) model. The results show that in the original test data, the accuracy rates of SDAE-RCmvMSE, RCmvMSE, and commonplace features of vibration signals combined with SVM (CFVS-SVM) methods are 99.5%, 100%, and 96% respectively. In the data with noise, the accuracy rates of RCmvMSE and CFVS-SVM are 97.75% and 93.08%, respectively, but the accuracy of SDAE-RCmvMSE is still 100%.     

A wavelet approach to fault diagnosis of a gearbox under varying load conditions

Varying load can cause changes in a measured gearbox vibration signal. However, conventional techniques for fault diagnosis are based on the assumption that changes in vibration signal are only caused by deterioration of the gearbox. There is a need to develop a technique to provide accurate state indicator of gearbox under fluctuating load conditions. This paper presents an approach to gear fault diagnosis based on complex Morlet continuous wavelet transform under this condition. Gear motion residual signal, which represents the departure of time synchronously averaged signal from the average tooth-meshing vibration, is analyzed as source data due to its lower sensitiveness to the alternating load condition. A fault growth parameter based on the amplitude of wavelet transform is proposed to evaluate gear fault advancement quantitatively. We found that this parameter is insensitive to varying load and can correctly indicate early gear fault. For a comparison, the advantages and disadvantages of other measures such as kurtosis, mean, variance, form factor and crest factor, both of residual signal and mean amplitude of continuous wavelet transform waveform, are also discussed. The effectiveness of the proposed fault indicator is demonstrated using a full lifetime vibration data history obtained under sinusoidal varying load.     

Simulation and analysis of machinery fault signals

Machinery condition monitoring is rapidly finding applications in all branches of industry. In particular, vibration monitoring is playing an increasingly important role as a tool for assisting with predictive and preventive maintenance and for improving operation efficiency of plant. Condition monitoring systems are used for the detection of incipient failure and the diagnosis of the nature of faults in operating machinery. However, for these systems to be reliable an improved understanding is required of the vibration signatures produced by machinery failure mechanisms and of methods for the interpretation of these signals. Many types of fault produce vibration signals which are impulsive in nature and which may be buried in background noise. A method is described for simulating this type of signal and modelling the various stages of incipient failure. Statistical and spectral analysis are used to describe the fault development. The influence of machinery frequency response characteristics on signal transmission from the damaged are to the measurement point are also considered.     

基于EMD-AWPP和HOSA-SVM算法的分布式光纤振动入侵信号的特征提取与识别

针对传统的信号处理方法无法有效区分不同振动入侵信号,提出一种基于EMD-AWPP和HOSA-SVM算法的振动信息特征提取与识别方法,用于解决分布式光纤振动入侵检测系统的高精度信号识别问题。处理不同振动类型时,该方法首先利用基于经验模态分解的自适应小波包处理算法,不仅对信号的低频部分进行了分解,而且对高频部分即信号的细节部分也进行了更好的时频局部化处理,改善了信号特征提取精度,减少传感信号异常值的影响; 其次采用高阶谱分析中的双谱和双相干谱,精确提取包含不同振动入侵信号类型的特征矢量; 最后在BPNN参比模型的基础上,用粒子群算法优化SVM的识别参数,使识别模型具有更强的自适应和自学习能力,克服了神经网络易陷入局部最优的不足之处,实现不同振动入侵信号的特征矢量识别。分析结果表明,针对不同类型的入侵源识别,该方法可以有效剔除随机噪声的影响,提取传感信息的特征矢量,降低异常值的影响,算法的预测类别与输出类别几乎一致,振动识别的精确率达到95%以上,识别效果明显强于BPNN网络的检测算法,提高了信息分析的准确性。     

Examination of multi-dimensional vibration isolation measures and their correlation to sound radiation over a broad frequency range

This article examines alternate vibration isolation measures for a multi-dimensional system. The isolator and receiver are modelled by the continuous system theory. The source is assumed to be rigid and both force and moment excitations are considered. Our analysis is limited to a linear time-invariant system, and the mobility synthesis method is adopted to describe the overall system behavior. Inverted ‘L’ beam and plate receivers are employed here to incorporate the contribution of their in-plane motions to vibration powers and radiated sound. Multi-dimensional transmissibilities and effectivenesses are comparatively evaluated along with power-based measures for the inverted ‘L’ beam receiver and selected source configurations. Further, sound pressures radiated from the inverted ‘L’ beam receiver are calculated and correlated with power transmitted to the receiver. Interactions within the ‘L’ beam receiver are also analyzed and measures that could identify dominant transfer paths within a system are examined. Sound measurements and predictions for the inverted ‘L’ plate receiver demonstrate that a rank order based on free field sound pressures, at one or more locations, may be regarded as a measure of isolation performance. Measured insertion losses for sound pressure match well with those based on computed results although further study is needed in relation to some discrepancies shown in the results. Finally, several emerging research topics are identified.     

A Novel Deep Transfer Learning Method for Intelligent Fault Diagnosis Based on Variational Mode Decomposition and Efficient Channel Attention

In recent years, deep learning has been applied to intelligent fault diagnosis and has achieved great success. However, the fault diagnosis method of deep learning assumes that the training dataset and the test dataset are obtained under the same operating conditions. This condition can hardly be met in real application scenarios. Additionally, signal preprocessing technology also has an important influence on intelligent fault diagnosis. How to effectively relate signal preprocessing to a transfer diagnostic model is a challenge. To solve the above problems, we propose a novel deep transfer learning method for intelligent fault diagnosis based on Variational Mode Decomposition (VMD) and Efficient Channel Attention (ECA). In the proposed method, the VMD adaptively matches the optimal center frequency and finite bandwidth of each mode to achieve effective separation of signals. To fuse the mode features more effectively after VMD decomposition, ECA is used to learn channel attention. The experimental results show that the proposed signal preprocessing and feature fusion module can increase the accuracy and generality of the transfer diagnostic model. Moreover, we comprehensively analyze and compare our method with state-of-the-art methods at different noise levels, and the results show that our proposed method has better robustness and generalization performance.