EXPERIMENTAL VALIDATION OF A STRUCTURAL HEALTH MONITORING METHODOLOGY: PART III. DAMAGE LOCATION ON AN AIRCRAFT WING

This paper documents the third phase of a programme of experimental work which validates a structural health monitoring methodology based on novelty detection. In this paper, an extension of the detection method for damage location is proposed and demonstrated. The structure of interest is a Gnat aircraft wing. Although it was not possible to damage the aircraft, the method was demonstrated by determining which of a set of inspection panels had been removed.     

EXPERIMENTAL VALIDATION OF A STRUCTURAL HEALTH MONITORING METHODOLOGY: PART I. NOVELTY DETECTION ON A LABORATORY STRUCTURE

This paper is concerned with the experimental validation of a structural health monitoring methodology, previously only investigated using synthetic data. The structure considered here is a simplified model of a metallic aircraft wingbox i.e., a plate incorporating stiffening elements. Damage is simulated by a saw-cut to one of the panel stringers (stiffeners). The analysis approach uses novelty detection based on measured transmissibilities from the structure. Three different novelty detection algorithms are considered here: outlier analysis, density estimation and an auto-associative neural network technique. All three methods are shown to be successful to an extent, although a critical comparison indicates reservations about the density estimation approach when used on sparse data sets.     

NOVELTY DETECTION IN A CHANGING ENVIRONMENT: REGRESSION AND INTERPOLATION APPROACHES

The technique of novelty detection is now established as a means of performing the lowest level of damage identification. Data are accumulated while the system or structure is operating in normal condition and used to construct a reference model. During subsequent operation of the system, data are compared to the reference and any significant deviations are taken to indicate damage. This approach has potential problems if the system or structure is embedded in a changing environment. If the reference data are only characteristic of a limited range of the environmental parameters, measurements from the system in an undamaged condition but from a different environmental state, may cause the diagnostic to register novelty and thus falsely infer damage. This paper demonstrates a potential solution to the problem via the construction of a reference set parametrized by an environmental variable. Two approaches are considered: regression and interpolation.     

Differentiation and subtraction of amplitude and phase images using a photorefractive novelty filter

In this article, we demonstrate the capability of a two-beam coupling photorefractive optical novelty filter of detecting changes in the amplitude or phase of optical images. These changes may either be continuous or discrete in time. The performance of the two-beam coupling novelty filter is investigated and expressions for the output contrast corresponding to phase and amplitude changes based on a novel, simple interference model of two-beam coupling are derived. These expressions are verified by experimental results on the novelty contrast, revealing that the amplitude contrast is not described correctly by the commonly accepted coupled-wave theory. The novelty filter was applied to the detection of temporally continuous phase changes provided by a gas flow and moving microscopic objects. A novel scheme for image subtraction is also demonstrated, showing the novelty filter’s ability to detect temporally discrete changes. Received: 14 November 1998 / Revised version: 18 January 1999 / Published online: 12 April 1999     

指向Lyapunov指数及其在单输入单输出系统故障检测中的应用

针对单输入单输出系统的故障检测, 采用混沌振荡器作为激励源, 并利用非一致延迟时间法对被测系统输出时间序列进行相空间重构. 在相空间中平衡点附近定义了指向Lyapunov指数, 并用其对被测系统输出在相空间中平衡点附近特征结构进行分析, 实现了对单输入单输出系统的故障检测. 仿真结果表明, 被测系统的参数变化将会引起相空间中平衡点附近特征结构的改变, 指向Lyapunov指数对其变化敏感. 关键词： 混沌激励 指向Lyapunov指数 故障检测 单输入单输出系统     

Guided waves for autonomous online identification of structural defects under ambient temperature variations

Ultrasonic guided waves that are excited by piezoelectric transducers can be used for the autonomous online identification of structural defects in thin structures. The proposed technique in this paper continuously analyzes a damage metric which is defined as the maximum residual amplitude of the differential signal. A special focus is on the decision making to discriminate the undamaged from the damaged state of the structure where the appropriate detection thresholds are derived statistically from the inverse cumulative distribution function of the damage metric during an initial training phase. An integrated trend analysis by means of the moving average mitigates the impact of statistical outliers and reduces the probability of erroneous identifications.Long-term measurements under ambient temperature variations have been conducted on an aluminum and a composite plate to study the properties of the proposed novelty detection framework. In this process the temperature effect was compensated by the well-known combination of optimal baseline selection (OBS) and baseline signal stretch (BSS). In case of the aluminum structure two artificial cracks with different sizes have been identified reliably. Consistent results were found on the composite specimen where an impact damage was identified for different excitation frequencies.     

基于视神经网络的混沌时间序列奇异信号实时检测算法

提出了一种基于视神经网络的实时检测混沌时间序列中的奇异点算法,设计了视神经网络奇异点检测器(RNNND);然后设计了基于反向传播(BP)神经网络和径向基函数(RBF)神经网络的混沌时间序列奇异点检测器.利用Lorenz理论模型产生的时间序列和实测输油管道压力时间序列分别检验了这3个奇异点检测器在抗干扰能力、检测微弱信号能力和运算速度等方面的性能.仿真和分析表明,RNNND具有良好的检测精度和较快检测速度.最后详细分析了3种奇异点检测器优缺点并给出了适用场合.     

一种嵌入式软件逻辑覆盖测试方法研究

针对嵌入式软件测试覆盖率低的问题,本文提出了基于软件故障注入的逻辑覆盖测试方法,首先就嵌入式系统常用传感器建立故障模式库,设计了嵌入式软件故障注入系统;其次选取中间层作为故障注入点,研究基于VxWorks653嵌入式操作系统的故障注入实现方式,并通过分析故障信号在软件系统中的传播,提出优化测试用例的方法;最后通过实验验证了该方法可有效提高容错设计功能、冗余设计功能、故障检测功能测试的逻辑覆盖率;有助于提高嵌入式软件的可靠性。     

新型傅里叶变换光谱探测系统相位误差校正方法

为了降低基于MEMS(micro-electro-mechanical systems)微镜的傅里叶变换光谱探测系统复原光谱仪的畸变,提高复原光谱的质量,减小系统相位误差的影响,提出了一种系统相位误差的修正方法。首先分析了基于MEMS微镜的傅里叶变换光谱探测系统中相位误差的主要来源,分析结果表明：该新型傅里叶变换光谱探测系统的相位误差来源于光程差的零点漂移,该相位误差可以通过改进该系统干涉仪的结构引入过零采样并利用Mertz乘积法进行修正。搭建了光谱探测系统的实验平台,对该相位误差校正方法进行了实验验证,实验结果表明：采用了改进干涉仪并利用Mertz乘积法校正误差后的光谱探测系统所测得的复原光谱质量得到明显改善,去除了原复原谱畸变产生的负峰,且旁瓣得到明显抑制。该相位误差校正方法能够很好的降低相位误差对系统性能的影响,能够有效地提高系统的光谱探测性能。在提出的基于MEMS微镜的新型傅里叶变换光谱探测系统的基础上,分析了该系统相位误差的来源,提出了一种系统相位误差的修正方法,提高了系统的光谱探测性能。     

A Visual Fault Detection Method for Induction Motors Based on a Zero-Sequence Current and an Improved Symmetrized Dot Pattern

Motor faults, especially mechanical faults, reflect eminently faint characteristic amplitudes in the stator current. In order to solve the issue of the motor current lacking effective and direct signal representation, this paper introduces a visual fault detection method for an induction motor based on zero-sequence current and an improved symmetric dot matrix pattern. Empirical mode decomposition (EMD) is used to eliminate the power frequency in the zero-sequence current derived from the original signal. A local symmetrized dot pattern (LSDP) method is proposed to solve the adaptive problem of classical symmetric lattice patterns with outliers. The LSDP approach maps the zero-sequence current to the ultimate coordinate and obtains a more intuitive two-dimensional image representation than the time–frequency image. Kernel density estimation (KDE) is used to complete the information about the density distribution of the image further to enhance the visual difference between the normal and fault samples. This method mines fault features in the current signals, which avoids the need to deploy additional sensors to collect vibration signals. The test results show that the fault detection accuracy of the LSDP can reach 96.85%, indicating that two-dimensional image representation can be effectively applied to current-based motor fault detection.     

A New Deep Dual Temporal Domain Adaptation Method for Online Detection of Bearings Early Fault

With the quick development of sensor technology in recent years, online detection of early fault without system halt has received much attention in the field of bearing prognostics and health management. While lacking representative samples of the online data, one can try to adapt the previously-learned detection rule to the online detection task instead of training a new rule merely using online data. As one may come across a change of the data distribution between offline and online working conditions, it is challenging to utilize the data from different working conditions to improve detection accuracy and robustness. To solve this problem, a new online detection method of bearing early fault is proposed in this paper based on deep transfer learning. The proposed method contains an offline stage and an online stage. In the offline stage, a new state assessment method is proposed to determine the period of the normal state and the degradation state for whole-life degradation sequences. Moreover, a new deep dual temporal domain adaptation (DTDA) model is proposed. By adopting a dual adaptation strategy on the time convolutional network and domain adversarial neural network, the DTDA model can effectively extract domain-invariant temporal feature representation. In the online stage, each sequentially-arrived data batch is directly fed into the trained DTDA model to recognize whether an early fault occurs. Furthermore, a health indicator of target bearing is also built based on the DTDA features to intuitively evaluate the detection results. Experiments are conducted on the IEEE Prognostics and Health Management (PHM) Challenge 2012 bearing dataset. The results show that, compared with nine state-of-the-art fault detection and diagnosis methods, the proposed method can get an earlier detection location and lower false alarm rate.     

A time domain approach to diagnose gearbox fault based on measured vibration signals

Spectral analysis techniques to process vibration measurements have been widely studied to characterize the state of gearboxes. However, in practice, the modulated sidebands resulting from the local gear fault are often difficult to extract accurately from an ambiguous/blurred measured vibration spectrum due to the limited frequency resolution and small fluctuations in the operating speed of the machine that often occurs in an industrial environment. To address this issue, a new time-domain diagnostic algorithm is developed and presented herein for monitoring of gear faults, which shows an improved fault extraction capability from such measured vibration signals. This new time-domain fault detection method combines the fast dynamic time warping (Fast DTW) as well as the correlated kurtosis (CK) techniques to characterize the local gear fault, and identify the corresponding faulty gear and its position. Fast DTW is employed to extract the periodic impulse excitations caused from the faulty gear tooth using an estimated reference signal that has the same frequency as the nominal gear mesh harmonic and is built using vibration characteristics of the gearbox operation under presumed healthy conditions. This technique is beneficial in practical analysis to highlight sideband patterns in situations where data is often contaminated by process/measurement noises and small fluctuations in operating speeds that occur even at otherwise presumed steady-state conditions. The extracted signal is then resampled for subsequent diagnostic analysis using CK technique. CK takes advantages of the periodicity of the geared faults; it is used to identify the position of the local gear fault in the gearbox. Based on simulated gear vibration signals, the Fast DTW and CK based approach is shown to be useful for condition monitoring in both fixed axis as well as epicyclic gearboxes. Finally the effectiveness of the proposed method in fault detection of gears is validated using experimental signals from a planetary gearbox test rig. For fault detection in planetary gear-sets, a window function is introduced to account for the planet motion with respect to the fixed sensor, which is experimentally determined and is later employed for the estimation of reference signal used in Fast DTW algorithm.     

变采样周期和多包传输的非线性NCS故障检测

研究具有多包传输、时变采样周期和未知干扰输入的Lipschitz非线性网络控制系统的故障检测问题。利用主动变采样周期的方法将多包传输的非线性连续网络控制系统建模为离散切换系统,设计基于观测器的鲁棒故障检测滤波器构造残差产生系统,运用Lyapunov稳定性理论和线性矩阵不等式(LMI)技术,给出了使闭环系统渐近稳定的充分条件及故障检测滤波器的增益矩阵。最后运用仿真算例说明了故障检测滤波器的残差产生系统对故障具有敏感性,同时对外部扰动输入具有鲁棒性。     

Characterizing turbulence in globally coupled maps with stochastic finite automata

We describe a method to discriminate between ordered and turbulent behavior in a general class of collective systems known as Globally Coupled Maps (GCM). Our method is able to discover an unknown small ordered region inside the turbulent phase of GCM parameter space. The computational nature of the method is the main novelty of our approach; it is another example of how measures based on computational notions of structure may provide new information in the study of dynamical systems.     

一种容差模拟电路的分级故障诊断方法

In this paper, a tolerance analog circuit fault diagnosis method based on hierarchical fault dictionary is proposed. During the simulation before test, firstly, the Worse-Case Analysis is used to get the normal characteristics output interval of the circuit under test and the output interval is saved as the first class fault dictionary, which will be used to fault detection; secondly, node-voltage sensitivity sequence is used as fault characteristics to build the second class fault dictionary for locating fault component; thirdly, based on simulation before test according to dividing the component parameters into seven segments, the third class fault dictionary is built to identify the parameter interval of components. In the fault diagnosis stage, based on the established three-class fault dictionary, fault detection, fault locating and component parameter interval identification can be realized respectively according to practical application. The proposed method can improve the efficiency of diagnosis after test and the solution will be a meaningful reference for practical applications. Finally, the simulation experiment demonstrates the effectiveness of the proposed method.     

A Fault Detection Method for Electrohydraulic Switch Machine Based on Oil-Pressure-Signal-Sectionalized Feature Extraction

A turnout switch machine is key equipment in a railway, and its fault condition has an enormous impact on the safety of train operation. Electrohydraulic switch machines are increasingly used in high-speed railways, and how to extract effective fault features from their working condition monitoring signal is a difficult problem. This paper focuses on the sectionalized feature extraction method of the oil pressure signal of the electrohydraulic switch machine and realizes the fault detection of the switch machine based on this method. First, the oil pressure signal is divided into three stages according to the working principle and action process of the switch machine, and multiple features of each stage are extracted. Then the max-relevance and min-redundancy (mRMR) algorithm is applied to select the effective features. Finally, the mini batch k-means method is used to achieve unsupervised fault diagnosis. Through experimental verification, this method can not only derive the best sectionalization mode and feature types of the oil pressure signal, but also achieve the fault diagnosis and the prediction of the status of the electrohydraulic switch machine.     

A Fault Detection Method Based on an Oil Temperature Forecasting Model Using an Improved Deep Deterministic Policy Gradient Algorithm in the Helicopter Gearbox

The main gearbox is very important for the operation safety of helicopters, and the oil temperature reflects the health degree of the gearbox; therefore establishing an accurate oil temperature forecasting model is an important step for reliable fault detection. Firstly, in order to achieve accurate gearbox oil temperature forecasting, an improved deep deterministic policy gradient algorithm with a CNN–LSTM basic learner is proposed, which can excavate the complex relationship between oil temperature and working condition. Secondly, a reward incentive function is designed to accelerate the training time costs and to stabilize the model. Further, a variable variance exploration strategy is proposed to enable the agents of the model to fully explore the state space in the early training stage and to gradually converge in the training later stage. Thirdly, a multi-critics network structure is adopted to solve the problem of inaccurate Q-value estimation, which is the key to improving the prediction accuracy of the model. Finally, KDE is introduced to determine the fault threshold to judge whether the residual error is abnormal after EWMA processing. The experimental results show that the proposed model achieves higher prediction accuracy and shorter fault detection time costs.     

A five-point stencil based algorithm used for phase shifting low-coherence interference microscopy

The phase shifting technique is the most widely used approach for detecting the envelope in low coherence interferometry. However, if the phase shifts calibration contains errors, some parasitic fringe structure will propagate into the calculated envelopes and cause imprecision in the envelope peak detection. To tackle these problems, a five-point stencil algorithm is introduced into the phase shifting interference microscopy. Considering the amount of parasitic fringes, envelope peak detection and computational efficiency, the presented approach leads to satisfactory results in performance. In combination with a simple polynomial curve fitting method the proposed algorithm exhibits good performance on envelope peak detection in surface profiling. Both of the simulated results and the experimental results indicated that the presented approach can be taken as an alternative to the currently existing methods used for phase shifting low-coherence interference microscopy.     

Social Computing and the Attention Economy

Social computing focuses on the interaction between social behavior and information, especially on how the latter propagates across social networks and is consumed and transformed in the process. At the same time the ubiquity of information has left it devoid of much monetary value. The scarce, and therefore valuable, resource is now attention, and its allocation gives rise to an attention economy that determines how content is consumed and propagated. Since two major factors involved in getting attention are novelty and popularity, we analyze the role that both play in attracting attention to web content and how to prioritize them in order to maximize it. We also demonstrate that the relative performance of strategies based on prioritizing either popularity or novelty exhibit an abrupt change around a critical value of the novelty decay time, resembling a phase transition.     

城轨车辆电气柜故障诊断方法研究

城轨车辆电气柜种类繁多,结构复杂,若依靠人工检测的方式进行故障的排除,不仅诊断效率低且可靠性差。通过对多个城轨车辆电气柜的具体分析,提出一种动态提取有向图结构模型并生成测试序列的算法,利用图的最优路径算法进行测试自检,最后使用一种适应性测试诊断树来完成故障的定位与隔离。实验表明,该诊断方法能够有效地对城轨车辆电气柜的故障进行诊断,且对于不同拓扑结构的城轨车辆电气柜具有良好的适应性。