流化床风帽故障的声发射检测

针对气固流化床中风帽故障影响流化质量问题,本文提出一种能够快速精确地检测出风帽故障位置和类型的声发射检测方法;声发射技术是一种实时、在线、非侵入流场的声波检测方法,利用均匀安装在流化床分布板下方的声发射传感器进行定位测量;采集气固流化床内颗粒撞击分布板产生的声信号,对该信号进行多尺度小波包分解,找出特征频段,由各个测点声发射信号总能量对比可以直观反应风帽故障存在位置,而各尺度能量分率的变化能进行故障类型判断;这种实时测量方法能更早,更精确的对风帽破损情况进行准确的判断。     

近场子空间聚焦的碰摩故障声发射定位方法

针对宽带多源声发射信号的相干、多模态和能量衰减快问题,提出一种近场多重相干信号子空间聚焦的定位算法用于碰摩故障声发射源的定位检测。首先,为滤除干扰模态波、减小频散效应,采用基于模态声发射传播特性分析的小波分解滤波方法,从碰摩初期的声发射信号中获取零阶模态波及波速用于定位计算;其次,为实现信号解相干,提出基于双边相关变换(TCT)的近场聚焦矩阵估计方法;最后,针对声发射信号的能量衰减快问题,利用近场基于特征分解的多重信号分类(N-MUSIC)的空间谱估计方法来实现声源的精确定位。理论分析和实验结果表明:该方法定位精度高、计算复杂度低、稳定性强,能有效识别多个相干碰摩声源。相比传统相干子空间算法(CSM),该方法减少了信号初值和聚焦频点的计算量,对双声源的分辨概率较现有修正近场多重信号分类算法提高了17%,是一种有效的碰摩故障源检测方法。      

木塑复合材料缺陷及损伤的声发射信号特征分析及神经网络模式识别

针对木塑复合材料五种典型的缺陷及损伤机制，选择合适的木塑试样，应用三点弯曲的加载方法采集声发射信号。对主损伤区附近的声发射事件，应用小波变换提取特征参数，确定五类主要损伤机制所对应的声发射信号特征。采用B—P型反向传播神经网络构成的智能化模式分类器，对此五类声发射信号进行识别，获得了满意的效果。     

应用深度学习识别法兰螺栓连接状态

针对常规诊断方法对螺栓的连接状态识别效果差、鲁棒性和抗噪性弱等问题,提出了基于深度学习理论的螺栓检测新方法。首先以4种预紧力状态下的法兰螺栓结构产生的声发射信号为研究对象,借助于自适应噪声的完整集成经验模态分解理论以及梅尔频率倒谱系数特征提取方式,实现了声发射信号的自适应消噪和最优模态函数分量组的选取,提取到了可以较好分辨螺栓连接状态的梅尔频率倒谱系数特征值。通过训练模型,较好地对4种连接状态下的螺栓进行了识别。结果表明,该模型在法兰螺栓的声发射信号的诊断中,准确率高,具有较好的抗噪性和鲁棒性。     

利用声信号对滚动轴承进行故障诊断的研究

旋转机械在运行过程中产生的声信号包含了滚动轴承的运行状态信息,且可采用非接触式测量,本文应用它对滚动轴承进行故障诊断。基于morlet小波变换的包络分析对采集的声信号进行降噪及包络处理,然后变换到频域,提取出特征频率并经过转换后作为线性神经网路的输入向量,辨识滚动轴承的状态。实验表明,本方法对滚动轴承故障诊断是有效的。     

HDP-HSMM的磨削声发射砂轮钝化状态识别*

在高精度金属材料磨削加工中,刀具即砂轮的状态对加工效率和加工质量具有重要的影响。钝化程度较高的砂轮不适于加工精密工件,需提前预警并修整更换砂轮。该文提出一种通过磨削声发射信号来检测砂轮钝化状态的方法。首先,对于采集到的信号进行小波软阈值降噪。然后,将其分割成多个有重叠的帧,并提取每帧信号的8个特征组成声发射数据集。最后,通过分层Dirichlet过程-隐半马尔可夫模型来建立声发射数据集和不同的砂轮钝化状态之间的非线性关系,旨在识别砂轮钝化状态。结果表明,上述检测方法能有效识别砂轮的不同钝化状态并能对整个加工过程中的砂轮钝化程度进行自动划分,其在测试数据集上的准确率达到93.7%,可以为实际工业应用提供理论指导。     

自适应信号增强在瞬态诱发耳声发射信号检测中的应用

耳声发射是近年来耳科学领域的研究热点。它已成为临床听力筛选和诊断耳蜗病变最为有效的手段之一。耳声发射信号的信噪比和相关率是临床诊断的重要依据。文中将自适应信号增强技术引入到对瞬态诱发耳声发射信号的检测中,并提出了一个基于最小均方算法的实用的自适应信号增强器结构。通过对106例受试耳所进行的瞬态诱发耳声发射信号检测,结果表明,自适应信号增强技术与传统的相干平均技术相比有更好的增强信号和抑制噪声的性能。利用自适应信号增强可以提高瞬态诱发耳声发射信号的信噪比增速、减少检测所需叠加次数、检测用时可缩短近一半。     

基于边界扫描的雷达嵌入式测试和诊断技术

为解决因雷达数字化、高速化发展引起的测试和诊断技术难题,提出了基于边界扫描的雷达嵌入式测试和诊断方案,介绍了系统的硬件架构和软件设计。该方法可以在雷达系统正常执行任务期间,实时检测数字集成电路的故障,并将故障定位到芯片引脚。对低速数字信号,可以采集完整的信号波形;对较高速度的数字信号,可以通过多次采集和统计分析的方法提取故障特征。该方法已通过试验验证。     

基于粒子滤波状态估计的滚动轴承故障识别方法

提出了一种基于粒子滤波状态估计的滚动轴承故障识别方法,该方法主要包括故障模型建立和故障识别两个步骤。在故障模型建立部分,首先依据滚动轴承不同故障状态下的振动信号,建立对应的自回归模型,作为故障模型;在故障识别部分,将正常状态下对应的模型,转化为状态空间模型,设计粒子滤波器,然后对不同的故障状态进行估计,提取其残差的相关特征,并结合模型参数特征应用BP神经网络识别算法进行故障识别。最后以美国凯斯西储大学的滚动轴承振动数据为例,验证了该方法的有效性。     

基于DE-LSSVM的滚动轴承故障诊断

针对滚动轴承故障诊断难以获得大量样本的问题以及LS-SVM 模型参数选择方法易陷入局部最优的缺点,提出了一种集合经验模态分解能量熵和差分进化算法（DE）优化最小二乘支持向量机相结合的轴承故障诊断方法。首先原始振动信号采用EEMD分解得到一组固有模态函数（IMF）,从有效本征模态函数IMF分量中提取的能量特征作为输入建立支持向量机,通过计算不同振动信号的能量熵值大小来判断轴承的故障损伤程度。为了提高模型的诊断精度,采用差分进化算法对LS-SVM的结构参数进行优化,并与LS-SVM和PSO-LSSVM模型相比较。结果表明,DE-LSSVM 模型的故障分类准确性得到了提高,可以有效应用于滚动轴承故障诊断中。     

Roller bearing acoustic signature extraction by wavelet packet transform,applications in fault detection and size estimation

Continuous online monitoring of rotating machines is necessary to assess real-time health conditions so as to enable early detection of operation problems and thus reduce the possibility of downtime. Rolling element bearings are crucial parts of many machines and there has been an increasing demand to find effective and reliable health monitoring technique and advanced signal processing to detect and diagnose the size and location of incipient defects. Condition monitoring of rolling element bearings, comprises four main stages which are, statistical analysis, fault diagnostics, defect size calculation, and prognostics. In this paper the effect of defect size, operating speed, and loading conditions on statistical parameters of acoustic emission (AE) signals, using design of experiment method (DOE), have been investigated to select the most sensitive parameters for diagnosing incipient faults and defect growth on rolling element bearings. A modified and effective signal processing algorithm is designed to diagnose localized defects on rolling element bearings components under different operating speeds, loadings, and defect sizes. The algorithm is based on optimizing the ratio of Kurtosis and Shannon entropy to obtain the optimal band pass filter utilizing wavelet packet transform (WPT) and envelope detection. Results show the superiority of the developed algorithm and its effectiveness in extracting bearing characteristic frequencies from the raw acoustic emission signals masked by background noise under different operating conditions. To experimentally measure the defect size on rolling element bearings using acoustic emission technique, the proposed method along with spectrum of squared Hilbert transform are performed under different rotating speeds, loading conditions, and defect sizes to measure the time difference between the double AE impulses. Measurement results show the power of the proposed method for experimentally measuring size of different fault shapes using acoustic emission signals.     

Low speed bearing fault diagnosis using acoustic emission sensors

In this paper, a new methodology for low speed bearing fault diagnosis is presented. This acoustic emission (AE) based technique starts with a heterodyne frequency reduction approach that samples AE signals at a rate comparable to vibration centered methodologies. Then, the sampled AE signal is time synchronously resampled to account for possible fluctuations in shaft speed and bearing slippage. The resampling approach is able to segment the AE signal according to shaft crossing times such that an even number of data points are available to compute a single spectral average which is used to extract features and evaluate numerous condition indicators (CIs) for bearing fault diagnosis. Unlike existing averaging based noise reduction approaches that require the computation of multiple averages for each bearing fault type, the presented approach computes only one average for all bearing fault types. The presented technique is validated using the AE signals of seeded fault steel bearings on a bearing test rig. The results in this paper have shown that the low sampled AE signals in combination with the presented approach can be utilized to effectively extract condition indicators to diagnose all four bearing fault types at multiple low shaft speeds below 10 Hz.     

基于相关性原理起重机负载电机监测系统设计

对起重机负载电机进行了研究,采用西门子公司的S7-200 SMART PLC采集负载电机的机械振动信号,通过工业Wi-Fi无线模块以无线数据包的形式将采集的数据汇总到上位机LabVIEW监测平台。上位机的LabVIEW监测平台对电动机振动信号进行相关性和频谱分析,将实时振动数据频谱信号和已知常见负载电机的轴承外圈故障、轴承内环故障和滚子故障三种典型的故障状态频谱信号进行相关性运算,得到实时信号与已知状态的相关系数;提出了以相关系数作为故障诊断判定阈值的方法进行故障诊断,实现了对起重机状态进行监测以及监控信息发布。     

Identification of multiple transient faults based on the adaptive spectral kurtosis method

This paper proposes a multi-fault detection method based on the adaptive spectral kurtosis (ASK) analysis of the vibration signal from single sensor. A theoretical model of multiple bearing faults is established in this paper. Compared with the kurtogram and protrugram techniques, the proposed method can more effectively extract signatures of multiple bearing faults even in the presence of strong background noise. The performance of the proposed method in fault detection of the rolling element bearings is validated using simulation data and experimental signals from a bearing with multiple faults and two faulty bearings.     

A New Method Based on Time-Varying Filtering Intrinsic Time-Scale Decomposition and General Refined Composite Multiscale Sample Entropy for Rolling-Bearing Feature Extraction

The early fault diagnosis of rolling bearings has always been a difficult problem due to the interference of strong noise. This paper proposes a new method of early fault diagnosis for rolling bearings with entropy participation. First, a new signal decomposition method is proposed in this paper: intrinsic time-scale decomposition based on time-varying filtering. It is introduced into the framework of complete ensemble intrinsic time-scale decomposition with adaptive noise (CEITDAN). Compared with traditional intrinsic time-scale decomposition, intrinsic time-scale decomposition based on time-varying filtering can improve frequency-separation performance. It has strong robustness in the presence of noise interference. However, decomposition parameters (the bandwidth threshold and B-spline order) have significant impacts on the decomposition results of this method, and they need to be artificially set. Aiming to address this problem, this paper proposes rolling-bearing fault diagnosis optimization based on an improved coyote optimization algorithm (COA). First, the minimal generalized refined composite multiscale sample entropy parameter was used as the objective function. Through the improved COA algorithm, optimal intrinsic time-scale decomposition parameters based on time-varying filtering that match the input signal are obtained. By analyzing generalized refined composite multiscale sample entropy (GRCMSE), whether the mode component is dominated by the fault signal is determined. The signal is reconstructed and decomposed again. Finally, the mode component with the highest energy in the central frequency band is selected for envelope spectrum variation for fault diagnosis. Lastly, simulated and experimental signals were used to verify the effectiveness of the proposed method.     

A Rolling Bearing Fault Classification Scheme Based on k-Optimized Adaptive Local Iterative Filtering and Improved Multiscale Permutation Entropy

The health condition of the rolling bearing seriously affects the operation of the whole mechanical system. When the rolling bearing parts fail, the time series collected in the field generally shows strong nonlinearity and non-stationarity. To obtain the faulty characteristics of mechanical equipment accurately, a rolling bearing fault detection technique based on k-optimized adaptive local iterative filtering (ALIF), improved multiscale permutation entropy (improved MPE), and BP neural network was proposed. In the ALIF algorithm, a k-optimized ALIF method based on permutation entropy (PE) is presented to select the number of ALIF decomposition layers adaptively. The completely average coarse-graining method was proposed to excavate more hidden information. The performance analysis of the simulation signal shows that the improved MPE can more accurately dig out the depth information of the time series, and the entropy value obtained is more consistent and stable. In the research application, rolling bearing time series are decomposed by k-optimized ALIF to obtain a certain number of intrinsic mode functions (IMFs). Then the improved MPE value of effective IMF is calculated and input into backpropagation (BP) neural network as the feature vector for automatic fault identification. The comparative analysis of simulation signals shows that this method can extract fault information effectively. At the same time, the experimental part shows that this scheme not only effectively extracts the fault features, but also realizes the classification and identification of different fault modes and faults of different degrees, which has a certain application prospect in the research and application direction of rolling bearing fault identification.     

A fault diagnosis scheme of rolling element bearing based on near-field acoustic holography and gray level co-occurrence matrix

Vibration signal analysis is the most widely used technique in condition monitoring or fault diagnosis, whereas in some cases vibration-based diagnosis is restrained because of its contact measurement. Acoustic-based diagnosis (ABD) with non-contact measurement has received little attention, although sound field may contain abundant information related to fault pattern. A new scheme of ABD for rolling element bearing fault diagnosis based on near-field acoustic holography (NAH) and gray level co-occurrence matrix (GLCM) is presented in this paper. It focuses on applying the distribution information of sound field to bearing fault diagnosis. A series of rolling element bearings with different types of fault are experimentally studied. Sound fields and corresponding acoustic images in different bearing conditions are obtained by fast Fourier transform (FFT) based NAH. GLCM features are extracted for capturing fault pattern information underlying sound fields. The optimal feature subset selected by improved F-score is fed into multi-class support vector machine (SVM) for fault pattern identification. The feasibility and effectiveness of our proposed scheme is demonstrated on the good experimental results and the comparison with the traditional ABD method. Considering test cost, the quantized level and the number of GLCM features for each characteristic frequency is suggested to be 4 and 32, respectively, with the satisfactory accuracy rate 97.5%.     

Intelligent Fault Identification for Rolling Bearings Fusing Average Refined Composite Multiscale Dispersion Entropy-Assisted Feature Extraction and SVM with Multi-Strategy Enhanced Swarm Optimization

Rolling bearings act as key parts in many items of mechanical equipment and any abnormality will affect the normal operation of the entire apparatus. To diagnose the faults of rolling bearings effectively, a novel fault identification method is proposed by merging variational mode decomposition (VMD), average refined composite multiscale dispersion entropy (ARCMDE) and support vector machine (SVM) optimized by multistrategy enhanced swarm optimization in this paper. Firstly, the vibration signals are decomposed into different series of intrinsic mode functions (IMFs) based on VMD with the center frequency observation method. Subsequently, the proposed ARCMDE, fusing the superiorities of DE and average refined composite multiscale procedure, is employed to enhance the ability of the multiscale fault-feature extraction from the IMFs. Afterwards, grey wolf optimization (GWO), enhanced by multistrategy including levy flight, cosine factor and polynomial mutation strategies (LCPGWO), is proposed to optimize the penalty factor C and kernel parameter g of SVM. Then, the optimized SVM model is trained to identify the fault type of samples based on features extracted by ARCMDE. Finally, the application experiment and contrastive analysis verify the effectiveness of the proposed VMD-ARCMDE-LCPGWO-SVM method.     

Rolling Bearing Fault Diagnosis Based on VMD-MPE and PSO-SVM

The goal of the paper is to present a solution to improve the fault detection accuracy of rolling bearings. The method is based on variational mode decomposition (VMD), multiscale permutation entropy (MPE) and the particle swarm optimization-based support vector machine (PSO-SVM). Firstly, the original bearing vibration signal is decomposed into several intrinsic mode functions (IMF) by using the VMD method, and the feature energy ratio (FER) criterion is introduced to reconstruct the bearing vibration signal. Secondly, the multiscale permutation entropy of the reconstructed signal is calculated to construct multidimensional feature vectors. Finally, the constructed multidimensional feature vector is fed into the PSO-SVM classification model for automatic identification of different fault patterns of the rolling bearing. Two experimental cases are adopted to validate the effectiveness of the proposed method. Experimental results show that the proposed method can achieve a higher identification accuracy compared with some similar available methods (e.g., variational mode decomposition-based multiscale sample entropy (VMD-MSE), variational mode decomposition-based multiscale fuzzy entropy (VMD-MFE), empirical mode decomposition-based multiscale permutation entropy (EMD-MPE) and wavelet transform-based multiscale permutation entropy (WT-MPE)).     

Wayside acoustic defective bearing detection based on improved Dopplerlet transform and Doppler transient matching

The diagnosis of train bearing defects plays a significant role in maintaining the safety of railway transport. However, the phenomenon of Doppler Effect in the acoustic signal recorded by the wayside Acoustic Defective Bearing Detector (ADBD) system leads to the difficulty for fault diagnosis of train bearings with a high moving speed. This paper proposes a double-searching solution based on improved Dopplerlet transform and Doppler transient matching to overcome the difficulty in wayside acoustic bearing diagnosis. In the solution, the first searching procedure is to extract necessary parameters of Doppler Effect under the situation with very low signal-to-noise ratio (SNR) based on an improved Dopplerlet transform. Using the obtained parameters, the Doppler Effect can be embedded into the constructed periodic Laplace wavelet transient models. Subsequently, the second searching procedure is conducted to search fault impact period of the defective bearing through an operation, called Doppler transient matching, which is to calculate the correlation coefficient between the Doppler transient model and the filtered raw signal with the Doppler Effect. The proposed double-searching algorithm can adapt to the real Doppler Effect situation and extract the exact fault impact period from the Doppler distorted signal, and thus shows powerful capability to analyze wayside acoustic signals from train bearings. The proposed wayside acoustic diagnostic scheme is verified by means of a simulated Doppler distorted signal with a very low SNR (−20 dB) and the experiments conducted on train bearings. The results indicate that the proposed algorithm is effective and has obvious advantages for ADBD system.