基于SIE和SVR的液压泵故障定量诊断

为更好地实现液压泵故障定量诊断,对故障定量诊断中的退化特征提取和故障程度诊断方法进行研究。针对排列熵算法的不足,提出空间信息熵(spatial information entropy,简称SIE)的概念,分析了空间信息熵3个参数(时间序列的分区数s、相空间重构的嵌入维数m和延迟时间τ)变化对其性能带来的影响,为其选取提供了依据。仿真分析结果也验证了其作为液压泵退化特征的有效性和优越性。基于空间信息熵算法提取液压泵故障退化特征集,针对退化特征与故障程度之间存在的非线性关系,提出采用果蝇优化算法优化参数的支持向量回归机实现液压泵的故障定量诊断。对实测液压泵振动信号分析结果表明,空间信息熵在表征液压泵故障程度方面具有更好的性能。将果蝇算法优化参数的支持向量回归机用于液压泵的故障定量诊断得到了理想的定量诊断效果,并通过对比分析验证了提出的支持向量回归机模型的有效性和优越性。     

基于多尺度排列熵的液压泵故障识别

将排列熵引入液压泵的故障识别中,分析了排列熵作为液压泵故障特征指标的性能;采用互信息法和伪近邻法优选排列熵计算中的延迟时间和嵌入维数,基于优选参数得到了能够更好区分液压泵故障的排列熵。针对单尺度排列熵只能在单个尺度上衡量振动信号复杂度的不足,在对多尺度排列熵进行研究的基础上提出了一种综合多尺度排列熵熵值和排列熵变化趋势的指标--多尺度排列熵偏均值,对液压泵实测信号的分析结果验证了该指标作为液压泵故障特征的有效性和优越性。     

Multiple model-based detection and estimation scheme for gas turbine sensor and gas path fault simultaneous diagnosis

In this paper, a multiple model (MM)-based detection and estimation scheme for gas turbine sensor and gas path fault diagnosis is proposed, which overcomes the coupling effects between sensor faults and gas path faults, and simultaneously realizes an accurate diagnosis of sensor and gas path faults. First, an adaptive fault detection and isolation (FDI) framework based on the MM method was established to detect and isolate sensor faults and gas path faults. Then, a fault amplitude estimation method was proposed according to the FDI results, and a fault validation method based on the Chi-square test was proposed to confirm the actual fault. Finally, hardware in the loop (HIL) simulation platform was established to validate the effectiveness of the proposed method. Several simulation case studies were conducted based on a two-shaft marine gas turbine with common gas path faults and sensor faults. The simulation results show that the proposed method can accurately diagnose the fault and estimate the corresponding fault amplitude when both the sensor fault and the gas path fault coincide.

     

Multivariate fault isolation of batch processes via variable selection in partial least squares discriminant analysis

In recent years, multivariate statistical monitoring of batch processes has become a popular research topic, wherein multivariate fault isolation is an important step aiming at the identification of the faulty variables contributing most to the detected process abnormality. Although contribution plots have been commonly used in statistical fault isolation, such methods suffer from the smearing effect between correlated variables. In particular, in batch process monitoring, the high autocorrelations and cross-correlations that exist in variable trajectories make the smearing effect unavoidable. To address such a problem, a variable selection-based fault isolation method is proposed in this research, which transforms the fault isolation problem into a variable selection problem in partial least squares discriminant analysis and solves it by calculating a sparse partial least squares model. As different from the traditional methods, the proposed method emphasizes the relative importance of each process variable. Such information may help process engineers in conducting root-cause diagnosis.     

基于形态差值滤波和差分熵的滚动轴承故障诊断

针对滚动轴承内外圈的早期故障,提出了一种新的诊断方法,该方法融合了数学形态学对非线性信号的滤波和信息熵理论在信号表征方面的优越性。首先,利用数学形态差值滤波器对实测的轴承内外圈轻重损伤的故障信号进行消噪处理,充分突出了有用的故障特征信息;然后,利用差分熵提取该信号中的突变特征信息,对其进行不确定性和复杂性度量;最后,根据突变点的冲击时间间隔和内外圈故障周期性冲击的时间间隔一致的思想来完成对滚动轴承的故障诊断。通过对仿真信号和滚动轴承实测内外圈两种故障程度的振动信号的诊断分析,证明该方法能够很好地识别轴承内外圈早期故障的类型,且具有很高的准确率。     

An Intelligent Fault Diagnosis Method of Multi-Scale Deep Feature Fusion Based on Information Entropy

For a single-structure deep learning fault diagnosis model,its disadvantages are an insufficient feature extraction and weak fault classification capability.This paper proposes a multi-scale deep feature fusion intelligent fault diagnosis method based on information entropy.First,a normal autoencoder,denoising autoencoder,sparse autoencoder,and contractive autoencoder are used in parallel to construct a multi-scale deep neural network feature extrac-tion structure.A deep feature fusion strategy based on information entropy is proposed to obtain low-dimensional features and ensure the robustness of the model and the quality of deep features.Finally,the advantage of the deep belief network probability model is used as the fault classifier to identify the faults.The effectiveness of the proposed method was verified by a gearbox test-bed.Experimental results show that,compared with traditional and existing intelligent fault diagnosis methods,the proposed method can obtain representative information and features from the raw data with higher classification accuracy.     

Adaptive PCA based fault diagnosis scheme in imperial smelting process

In this paper, an adaptive fault detection scheme based on a recursive principal component analysis (PCA) is proposed to deal with the problem of false alarm due to normal process changes in real process. Our further study is also dedicated to develop a fault isolation approach based on Generalized Likelihood Ratio (GLR) test and Singular Value Decomposition (SVD) which is one of general techniques of PCA, on which the off-set and scaling fault can be easily isolated with explicit off-set fault direction and scaling fault classification. The identification of off-set and scaling fault is also applied. The complete scheme of PCA-based fault diagnosis procedure is proposed. The proposed scheme is first applied to Imperial Smelting Process, and the results show that the proposed strategies can be able to mitigate false alarms and isolate faults efficiently.     

基于选择性加权适配网络的多域新故障识别方法

针对深度迁移学习诊断方法要求机械设备训练数据与测试数据具有相同类别空间,同时难以有效识别新故障的问题, 提出了一种基于选择性加权适配网络的多域新故障识别方法。 所提方法利用一维卷积神经网络提取源域与目标域深度判别特 征,并集成领域判别器与多分类器结构,构建源域与目标域权重函数,自适应度量源域与目标域类别的相似程度;从而利用对抗 学习策略来有效减少源域与目标域共享类数据的分布差异;最后利用高斯分布拟合方法自动判别权重阈值,实现对目标域已知 故障和新故障的有效诊断。 在齿轮箱变工况迁移诊断任务上对所提方法进行分析与应用验证,并与现有的其它方法进行比较, 所提方法在所有任务上的调和平均值(E-score)达到 0. 8 以上,验证了所提方法的有效性与优越性。     

Multiple sensor fault diagnosis for dynamic processes

Modern industrial plants are usually large scaled and contain a great amount of sensors. Sensor fault diagnosis is crucial and necessary to process safety and optimal operation. This paper proposes a systematic approach to detect, isolate and identify multiple sensor faults for multivariate dynamic systems. The current work first defines deviation vectors for sensor observations, and further defines and derives the basic sensor fault matrix (BSFM), consisting of the normalized basic fault vectors, by several different methods. By projecting a process deviation vector to the space spanned by BSFM, this research uses a vector with the resulted weights on each direction for multiple sensor fault diagnosis. This study also proposes a novel monitoring index and derives corresponding sensor fault detectability. The study also utilizes that vector to isolate and identify multiple sensor faults, and discusses the isolatability and identifiability. Simulation examples and comparison with two conventional PCA-based contribution plots are presented to demonstrate the effectiveness of the proposed methodology.     

基于谐波小波包能量熵的轴承故障实时诊断

将谐波小波包变换与信息熵相结合,从揭示故障信号能量分布的复杂程度入手,提出一种轴承故障实时诊断的新方法.对故障振动信号进行谐波小波包分解,将分解的小波系数按尺度进行排列,计算不同尺度的能量,以尺度能量为划分标准,计算故障信号的能量熵,通过能量的熵值诊断轴承故障.给出谐波小波包能量熵的轴承故障的具体诊断方法和模型.对不同...     

基于概率神经网络的大机组组合故障识别

分析了大机组组合故障难以正确识别的原因和常见组合故障识别方法的不足 ,提出了基于识别后验熵的组合故障优化判别规则 ,改进了概率神经网络的分类能力。最后 ,对模拟组合样本和大机组高频组合故障进行识别 ,获得了满意的结果     

交流暂态故障对柔性直流系统影响的评判研究

在模块化多电平电压源换流器高压直流输电(MMC-HVDC)系统中,发生在交流输电线路的各类暂态故障或扰动不但会引起与交流系统连接的MMC换流站内上下桥臂不同程度过流、过压现象,也会对对向站的正常运行产生一定程度的负面影响。如何利用MMC换流站内各电气信息实现交流暂态故障或扰动对其运行状态影响程度的评判将是今后MMC-HVDC研究领域的热点问题。针对上述问题,首先分析了交流输电线路暂态故障对站内电压、电流的影响,指出站内各电气信息之间的联系。而后鉴于MMC-HVDC系统阻尼对暂态故障能量传递的抑制作用,考虑故障扰动时站内电压、电流具有变化无规律、频率复杂且扰动特征能量等级低等特点,提出利用Renyi小波包能量熵对交流暂态故障发生时站内电气信息进行特征提取,并根据特征提取结果构建了系统运行状态评判指标集。基于Renyi熵权和灰色关联分析理论,提出了一种双端MMC-HVDC系统运行状态综合评判方法。最后,利用双端31电平MMC-HVDC系统进行了各类交流暂态故障的模拟实验,并基于该方法实现了不同交流暂态故障对MMC-HVDC系统运行状态影响程度的评判,判断结果证明了该方法的有效性。     

轴承故障动态检测的迭代交叉信息熵方法

将小波包变换和迭代交叉信息熵有机结合,从检测信号信息量差异性的角度出发,提出一种设备故障动态检测的新方法。该方法的核心是动态选取时间相邻的两帧振动信号,对两帧信号作小波包分解得到等频宽的分解信号,计算分解信号不同时间段的能量,得到信号的尺度分段时间能量矩阵,以尺度分段时间能量矩阵作为一种信息的划分,计算两帧信号的迭代交叉熵,用熵值来表征信号的差异性而检测出故障。检测实例验证了该方法的有效性,检测的实时性好、准确性高。     

基于奇异值分解的故障检测技术及其应用

针对基于未知输入观测器的诊断方法在诊断多故障时具有局限性，提出了一种基于奇异值分解的诊断方法，这种方法通过奇异值分解将不同故障对系统残差的影响进行分离，给出了多故障检测与分离的方法，仿真证明这种方法对于多故障诊断有很好的效果。     

Fault detection and classification in chemical processes based on neural networks with feature extraction

Feed forward neural networks are investigated here for fault diagnosis in chemical processes, especially batch processes. The use of the neural model prediction error as the residual for fault diagnosis of sensor and component is analyzed. To reduce the training time required for the neural process model, an input feature extraction process for the neural model is implemented. An additional radial basis function neural classifier is developed to isolate faults from the residual generated, and results are presented to demonstrate the satisfactory detection and isolation of faults using this approach.     

多运动状态下的移动机器人故障诊断方法

本文提出了一种处理不同运动状态下移动机器人故障的诊断方法。该方法将移动机器人的运动状态分为5种：静止状态、直线运动状态、3种不同的转动状态。在不同的运动状态下，考虑不同模式的故障（左轮编码器故障、右轮编码器故障和陀螺仪故障等）。利用卡尔曼滤波器组，处理每一种运动状态下不同模式的故障发生的概率。最后，根据故障发生的概率值判断故障模式发生的可能性。与其他故障诊断方法相比，文中的故障诊断方法有效改善了误诊和漏诊现象。仿真结果表明在轮式移动机器人故障诊断上的可行性。     

基于频域时空特征谱的信息故障诊断方法

目前振动信号的分析主要是针对特定测点在某一瞬间采集的一段振动波形,提取其中的特征量来进行诊断故障,这种基于状态信息的诊断方法对故障类型的辨别能力有限。基于多个振动波形状态的过程信息,提出和定义了两种基于过程信息融合的信息火用指标,用于反映同一个过程中不同状态间的过程变化规律以及不同过程中对应状态间的过程变化规律。在此基础上,提出了一种基于频域时空特征谱的旋转机械信息火用故障诊断方法,并通过该方法对试验台获取的振动故障信号进行分析。计算结果表明,该方法是一种有效的故障诊断方法。     

基于信息熵贴近度和证据理论的旋转机械故障诊断方法

从信息融合的思路出发,建立反映振动能量的旋转机械故障状态的信息熵特征,如奇异谱熵、功率谱熵、小波空间状态特征谱熵和小波能谱熵。通过试验,建立了旋转机械典型故障下的信息熵期望值,即获得基于信息熵的故障诊断标准特征向量。由于传感器的不确定性和故障的多样性,提出采用D-S证据理论来对4种信息熵进行信息融合。根据越相似模式间的距离越短的思路,提出采用信息熵贴近度来建立证据理论的基本可信度分配,以基于基本可信数的决策方法来作为故障模式识别方法。通过实例计算,证明基于信息熵贴近度和证据理论的旋转机械故障诊断方法是故障模式定量识别的一种可行的新方法。     

Investigation of vehicle crash modeling techniques: theory and application

Creating a mathematical model of a vehicle crash is a task which involves considerations and analysis of different areas which need to be addressed because of the mathematical complexity of a crash event representation. Therefore, to simplify the analysis and enhance the modeling process, in this work, a brief overview of different vehicle crash modeling methodologies is proposed. The acceleration of a colliding vehicle is measured in its center of gravity—this crash pulse contains detailed information about vehicle behavior throughout a collision. A virtual model of a collision scenario is established in order to provide an additional data set further used to evaluate a suggested approach. Three different approaches are discussed here: lumped parameter modeling of viscoelastic systems, data-based approach taking advantage of neural networks and autoregressive models and wavelet-based method of signal reconstruction. The comparative analysis between each method’s outcomes is performed and reliability of the proposed methodologies and tools is evaluated.     

Diagnosing planetary gear faults using the fuzzy entropy of LMD and ANFIS

The small size, low weight, and large transmission ratio of planetary gear have resulted in large-scale use, low speed, and heavy-duty mechanical systems. Poor working conditions of planetary gear lead to frequent occurrence of faults. A method is proposed for diagnosing faults in planetary gear based on fuzzy entropy of Local mean decomposition (LMD) and Adaptive neuro-fuzzy inference system (ANFIS). The original vibration signal is decomposed into six Product function (PF) components and a residual using LMD. Given that decomposed PF components contain the main fault feature information, fuzzy entropy is used to reflect the complexity and irregularity of each PF component. The fuzzy entropies of each PF component are defined as the input of the ANFIS model, and its parameters and membership functions are adaptively adjusted based on training samples. Finally, fuzzy inference rules are determined, and the optimal ANFIS model is obtained. Testing samples are used to verity the trained ANFIS model. The overall fault recognition rate reaches 88.8%, and the fault recognition rate for gear with wear reaches 96%. Therefore, the proposed method is effective at diagnosing planetary gear faults.