基于参数化时频分析的反辐射导弹检测

从参数化时频分析的角度出发,根据反辐射导弹(ARM)回波的信号特征,合理选择3种仿射时频变换并组合得到相应的时频原子,从而提出了三参数Chirplet变换的概念,同时根据ARM和载机回波的三参数Chirplet变换的时移特性,提出了一种用观测信号与其延时信号的三参数Chirplet变换的模之差来检测ARM的新方法。该方法可在不衰减ARM回波能量的前提下有效地对消载机回波干扰和消除部分背景噪声,且整个过程可用快速傅里叶变换(FFT)算法实现,从而简化了整个检测系统。仿真结果表明,该方法可在大载机回波干扰和低信噪比环境下快速准确地检测出ARM,实现实时告警。     

Knowledge based sensor fault detection for gas turbines under consideration of model based methods

The gas path analysis is a useful tool for modeling jet engines and stationary gas turbines, respectively. Such models are used for diagnosis that means that measurement values as inputs deliver state variables as outputs from the model. The measurement values are not fault free. They are disturbed by noise and by systematic sensor errors. Therefore algorithms have to be implemented which filter and compensate these errors; furthermore the modeling is not exact. Thereby the algorithms must be extended and adapted to this case. In alternation to the model based procedures the diagnosis problem can also be solved by knowledge based methods using an expert system. Such an expert system is developed and presented in this paper. It is an efficient tool — also in connection with the model based procedures — for detecting faults in the states of the engine and faults in the sensors as well. The diagnosis software package is applied on military and civilian jet engines and it is used in power plants for stationary gas turbines.     

基于故障检测次序的供料单元故障诊断

针对气动PLC自动生产线中供料单元,在一次供料过程时,上电后却无法运作,通过观察其故障现象,分析其故障原因,提出设定故障检查次序,综合利用假设验证法、替换法、经验法和测量法等故障诊断方法,排除设备的故障,继而通过实践证明合理设定故障检查次序对设备故障排除的重要性.     

Wavelet based fault detection in analog VLSI circuits using neural networks

This paper deals with a new method of testing analog VLSI circuits, using wavelet transform for analog circuit response analysis and artificial neural networks (ANN) for fault detection. Pseudo-random patterns generated by Linear Feedback Shift Register (LFSR) are used as input test patterns. The wavelet coefficients obtained for the fault-free and faulty cases of the circuits under test (CUT) are used to train the neural network. Two different architectures, back propagation and probabilistic neural networks are trained with the test data. To minimize the neural network architecture, normalization and principal component analysis are done on the input data before it is applied to the neural network. The proposed method is validated with two IEEE benchmark circuits, namely, the operational amplifier and state variable filter.     

串联型故障电弧检测方法对比分析

由于线路故障位置的不确定性,目前串联型故障电弧检测方法主要基于电流信号分析进行识别。通过对不同负载在串联型故障电弧发生前后的电流波形进行对比,得出了串联型故障电弧电流特性及其变化规律;以串联型故障电弧的电流信号为研究对象,介绍了基于希尔伯特黄变换、信息熵与短时傅里叶变换、小波近似熵与支持向量机的串联型故障电弧检测方法,概述了不同检测方法的故障电弧特征提取过程;对3种串联型故障电弧检测方法优缺点进行了比较,指出基于希尔伯特黄变换、信息熵与短时傅里叶变换的检测方法可有效提取故障电弧发生时电流的时频特性,对提取的时频谱幅值设置合适的阈值即可作为串联型故障电弧识别的依据,但准确性和实时性不高,而基于小波近似熵与支持向量机的检测方法可直接提取近似熵作为支持向量机的输入来识别串联型故障电弧,具有较高的准确性和实时性,更适用于煤矿现场。     

Bridging fault detection in cluster based FPGA by using Muller C element

In this work, we propose a testing technique for detecting single stuck-at and bridging faults in the interconnects of the cluster based FPGA. The presence of the feedback-bridging fault, race and glitch poses major challenges to the detection of the fault. The feedback bridging fault has a high ingredient of delay dependent properties due to the variation of the feedback path delay. So we have exploited the concept of asynchronous logic in order to detect the fault. We configure the block under test (BUT) with a pseudo delay independent asynchronous element known as Muller C element. The novelty of this scheme lies in the fact that, it can detect the stuck-at and bridging fault including the feedback bridging fault by a single test configuration. The Xilinx Jbits 3.0 API (Application Program Interface) is used to implement the BISTER (Built-in-self-tester) structure in the FPGA. In comparison to the traditional FPGA development tool (ISE), ‘Jbits’ gives more controllability for which the partial run time reconfiguration of the FPGA is easily achieved.     

基于时频分析的海杂波背景下舰船目标检测

为了克服基于小波尺度谱重排的时频分析方法中时、频分辨率不佳及时频分布可读性较差等问题,本文提出了一种基于参数优化Morlet小波变换和奇异值分解的海杂波背景下舰船目标检测算法。算法首先利用Shannon小波熵作为目标函数,根据高频地波雷达信号的特点自适应地优化Morlet小波变换的时间带宽积参数,使得后续重排尺度谱的时、频分辨率同时达到最佳。然后再对重排小波尺度谱进行基于奇异值分解的降噪处理,以抑制环境噪声的影响,进一步提高时频分布的可读性。实验结果表明：与传统的时频分析算法相比,本文提出的算法具有更好的时频聚集性和较强的噪声抑制能力,能有效地检测海杂波背景下缓慢运动的匀速和匀加速舰船目标。     

基于时频参数融合的自适应语音端点检测算法

为了解决低信噪比环境下传统的语音端点检测算法性能较差且不能自适应环境噪声,提出了一种基于时频参数融合的自适应语音端点检测算法。将对数能量与改进的Mel能量进行融合,获得了一种新的时频参数（TF）,该参数能有效地区分语音段和噪声段。使用该参数在噪声段对阈值进行更新,采用门限检测法判定出语音端点。仿真实验表明,该算法具有较好的鲁棒性,且能够准确地检测出语音端点。当信噪比（SNR）为0 dB时,端点检测错误率仅为15%左右。     

基于时频图像的不变矩神经网络故障诊断方法

在设备发生故障时,其故障信号往往表现为时变特性。传统的基于FFT变换的分析方法对这些时变信号往往会得出错误的结论。在此,应用一种新的时频分析方法,局域波法,对故障振动信号进行描述。由于局域波时频谱可以表示为灰度图像。因此,利用图像信息的不变矩进行故障特征提取。并且以径向基函数神经网络(RBFNN)作为故障分类器,对实际故障信号进行测试,实验证明了该方法的可行性。     

基于区间观测器的执行器故障检测

针对同时具有未知非线性函数(包括系统不确定性、外部干扰等) 和执行器故障的非线性系统, 提出基于区间观测器的故障检测方法. 首先, 在假定执行器故障不出现的前提下, 基于未知非线性函数的上下界信息, 提出两种区间观测器设计方法; 然后, 利用这两种区间观测器的输出和系统的真实输出, 构造可以对执行器故障进行检测的残差, 以此实现基于区间观测器的执行器故障检测. 最后, 通过两个仿真例子验证了所提出方法的正确性和有效性.

     

基于Peakvue技术的轴承故障检测

针对传统轴承故障检测三频段包络解调方法计算量大、速度慢的特点,提出了一种基于Peakvue技术的轴承故障检测方法.该方法采用加速度传感器捕捉含有具有短时特性应力波的轴承振动信号,对加速度传感器的输出信号进行高通滤波分离出高频信号和应力波信号,并按一定时间间隔提取滤波后的信号峰值,对提取后的信号进行包络解调判断故障类型.Matlab仿真实验结果表明:该方法有利于提高轴承故障检测的处理速度和诊断准确率.     

Robust fault detection of non-linear systems using set-membership state estimation based on constraint satisfaction

In this paper, the robust fault detection problem for non-linear systems considering both bounded parametric modelling errors and measurement noises is addressed. The non-linear system is monitored by using a state estimator with bounded modelling uncertainty and bounded process and measurement noises. Additionally, time-variant and time-invariant system models are taken into account. Fault detection is formulated as a set-membership state estimation problem, which is implemented by means of constraint satisfaction techniques. Two solutions are presented: the first one solves the general case while the second solves the time-variant case, being this latter a relaxed solution of the first one. The performance of the time-variant approach is tested in two applications: the well-known quadruple-tank benchmark and the dynamic model of a representative portion of the Barcelona's sewer network. In both applications, different scenarios are presented: a faultless situation and some faulty situations. All considered scenarios are intended to show the effectiveness of the presented approach.     

Model‐based fault detection and isolation method using ART2 neural network

This article presents a model‐based fault diagnosis method to detect and isolate faults in the robot arm control system. The proposed algorithm is composed functionally of three main parts: parameter estimation, fault detection, and isolation. When a change in the system occurs, the errors between the system output and the estimated output cross a predetermined threshold, and once a fault in the system is detected, the estimated parameters are transferred to the fault classifier by the adaptive resonance theory 2 neural network (ART2 NN) with uneven vigilance parameters for fault isolation. The simulation results show the effectiveness of the proposed ART2 NN–based fault diagnosis method. © 2003 Wiley Periodicals, Inc.     

基于不确定性的故障预测方法综述

故障预测是实现视情维修策略的基础. 不确定性问题在故障预测中普遍存在, 对此, 总结了基于不确定性的故障预测方法的关键问题, 并以不确定性属性的特点将现有故障预测方法分为基于随机性、模糊性、灰性及混合不确定性等4 类. 综述了各类方法的研究现状与不足, 并展望了基于不确定性的故障预测方法的发展趋势, 探讨了基于区间不确定性的故障预测方法的可行性.

     

Improved canonical correlation analysis-based fault detection methods for industrial processes

Recent research has emphasized the successful application of canonical correlation analysis (CCA) to perform fault detection (FD) in both static and dynamic processes with additive faults. However, dealing with multiplicative faults has not been as successful. Thus, this paper considers the application of CCA to deal with the detection of incipient multiplicative faults in industrial processes. The new approaches incorporate the CCA-based FD with the statistical local approach. It is shown that the methods are effective in detecting incipient multiplicative faults. Experiments using a continuous stirred tank heater and simulations on the Tennessee Eastman process are provided to validate the proposed methods.     

High-speed train fault detection with unsupervised causality-based feature extraction methods

With the development of smart sensors, large amount of operating data collected from a complex system as a high-speed train providing opportunities in efficient and effective fault detection and diagnosis (FDD). The data brings also challenges in the FDD modelling process, since the various signals may be redundant, useless and noisy for the FDD modelling of a specific sub-system. The data-driven methods suffer also from the curse of dimensionality. Feature dimension reduction can reduce the dimension of the monitoring dataset and eliminate the useless information. Different from the classical methods based on the correlation among variables, recent studies have shown that causality-based methods can make the FDD model more explanatory and robust. From the adjacency matrix of the causal network diagram, three unsupervised causality-based feature extraction methods for FDD in the braking system of a high-speed train are proposed in this paper. By constructing the causal network diagram among the raw monitoring feature variables through the causal discovery algorithm, the proposed methods extract informative features based on the causal adjacency matrix or the full causal adjacency matrix proposed in this work. These methods are adopted for fault detection with real dataset collected from the braking system in a high-speed train to verify their effectiveness. The experimental results show that the proposed causality-based feature extraction methods are effective and have certain advantages in comparison with the classical correlation-based methods. Especially, the feature extraction method based on the correlation matrix constructed from full causal adjacency matrix achieves better and stable results than the benchmark methods in the experiment.     

故障检测中核参数优化方法性能评估

近年来,基于核主元分析与核偏最小二乘的方法经常被应用于过程监控与故障检测领域以克服工业过程的非线性.研究发现此类方法的检测性能很大程度上受核参数的影响,而目前学术界对该参数的优化方法研究较少.因此,本文以最常用的高斯核方法为例,首先总结了3类常用的核参数优化方法:二分法、基于BP神经网络的重构法和基于样本分类的重构法;其次重点分析每个方法的特点和它们之间的联系,并评估它们的性能;最后将上述方法设计成一个核参数优化系统应用于热连轧过程的故障检测中.应用结果表明,优化后的核参数能显著提高故障检测性能.     

Induction machine fault detection using clone selection programming

A clonal selection programming (CSP)-based fault detection system is developed for performing induction machine fault detection and analysis. Four feature vectors are extracted from power spectra of machine vibration signals. The extracted features are inputs of an CSP-based classifier for fault identification and classification. In this paper, the proposed CSP-based machine fault diagnostic system has been intensively tested with unbalanced electrical faults and mechanical faults operating at different rotating speeds. The proposed system is not only able to detect electrical and mechanical faults correctly, but the rules generated is also very simple and compact and is easy for people to understand, This will be proved to be extremely useful for practical industrial applications.     

基于高效核偏最小二乘的质量相关故障检测

核偏最小二乘(KPLS)是一种多元统计方法, 广泛应用于过程监控, 然而, KPLS采用斜交分解, 导致质量相关空间存在冗余信息易引发误报警. 因此, 本文提出了高效核偏最小二乘(EKPLS)模型, 所提方法通过奇异值分解(SVD)将核矩阵正交分解为质量相关空间和质量无关空间, 有效降低质量相关空间中的冗余信息, 并采用主成分分析(PCA)按方差大小将质量相关空间分解为质量主空间和质量次空间. 此外, 为进一步降低由质量无关故障引发的误报警, 提出基于质量估计的正交信号修正(OSC)预处理方法, 并结合EKPLS模型提出了OSC-EKPLS算法. OSCEKPLS通过质量估计值对被测数据进行OSC预处理, 降低了计算复杂度和误报率. 最后, 通过数值仿真和田纳西–伊斯曼过程验证了OSC-EKPLS具有良好的故障检测性和更低的误报率.