非线性系统执行器鲁棒故障诊断

针对满足Lipschitz条件的非线性系统,提出了一种鲁棒的控制器时变故障的诊断方案。方案通过采用观测器技术跟踪系统的输出,从而得到残差,根据所得到的残差是否已超过了设定的域值,再通过自适应跟踪器,对故障进行跟踪,从而完成对控制器故障的诊断。同时还对所提出的故障诊断结构的鲁棒性、灵敏度和稳定性作了分析。最后,通过一个简单的仿真例子进一步验证了提出方案的可行性。     

Robust fault detection for the dynamics of high-speed train with multi-source finite frequency interference

This paper proposes a composite fault detection scheme for the dynamics of high-speed train (HST), using an unknown input observer-like (UIO-like) fault detection filter, in the presence of wind gust and operating noises which are modeled as disturbance generated by exogenous system and unknown multi-source disturbance within finite frequency domain. Using system input and system output measurements, the fault detection filter is designed to generate the needed residual signals. In order to decouple disturbance from residual signals without truncating the influence of faults, this paper proposes a method to partition the disturbance into two parts. One subset of the disturbance does not appear in residual dynamics, and the influence of the other subset is constrained by $H_{\infty }$ performance index in a finite frequency domain. A set of detection subspaces are defined, and every different fault is assigned to its own detection subspace to guarantee the residual signals are diagonally affected promptly by the faults. Simulations are conducted to demonstrate the effectiveness and merits of the proposed method.     

Auxiliary input design for stochastic subspace-based structural damage detection

This paper considers the problem of auxiliary input design for subspace-based fault detection methods. In several real applications, particularly in the damage detection of mechanical structures and vibrating systems, environment noise is the only input to the system. In some applications, white noise produces low quality output data for the subspace-based fault detection method. In those methods, a residual is calculated to detect the fault based on the output information. However, some modes of the system may not influence the outputs and the residual appropriately if the input is not exciting enough for those modes. In this paper, the method of “rotated inputs” is implemented to excite the system modes. In addition to produce a residual more sensitive to the weak modes, it is possible to detect system order changes due to the fault using the rotated inputs. Simulation results demonstrate the efficiency of injecting the auxiliary input to improve the subspace-based fault detection methodology.     

EFFECTIVENESS AND SENSITIVITY OF VIBRATION PROCESSING TECHNIQUES FOR LOCAL FAULT DETECTION IN GEARS

This paper deals with gear condition monitoring based on vibration analysis techniques. The detection and diagnostic capability of some of the most effective techniques are discussed and compared on the basis of experimental results, concerning a gear pair affected by a fatigue crack. In particular, the results of new approaches based on time-frequency and cyclostationarity analysis are compared against those obtained by means of the well-accepted cepstrum analysis and time-synchronous average analysis. Moreover, the sensitivity to fault severity is assessed by considering two different depths of the crack. The effect of transducer location and processing options are also shown. In the case of the experimental results considered in this paper, the power cepstrum is practically insensitive to the crack evolution. Conversely, the spectral correlation density function is able to monitor the fault development and does not seem to be significantly influenced by the transducer position. Analysis techniques of the time-synchronous average, such as the ‘residual’ signal and the demodulation technique, are able to localise the damaged tooth; however, the sensitivity of the demodulation technique is strongly dependent on the proper choice of the filtering band and affected by the transducer location. The wavelet transform seems to be a good tool for crack detection; it is particularly effective if the residual part of the time-synchronous averaged signal is processed.     

基于深度残差收缩网络的风力发电机齿轮箱故障诊断

提出了一种基于深度残差收缩网络的风力发电机齿轮箱故障诊断方法。首先，通过齿轮箱动力学模拟实验平台采集9种工况下的8种故障的振动信号；其次，对所采集的信号进行数据预处理，将其输入至深度残差收缩网络中训练；最后，利用反向传播算法不断优化网络参数，实现变工况下风力发电机齿轮箱故障的识别与分类。实验结果表明，所提方法在变工况场景下，可有效提取齿轮箱的故障特征并具有较高的识别准确率，证明了其在风力发电机齿轮箱故障诊断方面的可行性及有效性。     

An automated procedure for detection and identification of ball bearing damage using multivariate statistics and pattern recognition

This paper suggests an automated approach for fault detection and classification in roller bearings, which is based on pattern recognition and principal components analysis of the measured vibration signals. The signals recorded are pre-processed applying a wavelet transform in order to extract the appropriate high frequency (detailed) area needed for ball bearing fault detection. This is followed by a pattern recognition (PR) procedure used to recognise between signals coming from healthy bearings and those generated from different bearing faults. Four categories of signals are considered, namely no fault signals (from a healthy bearing), inner race fault, outer race fault and rolling element fault signals. The PR procedure uses the first six principal components extracted from the signals after a proper principal component analysis (PCA). In this work a modified PCA is suggested, which is much more appropriate for categorical data. The combination of the modified PCA and the PR method ensures that the fault is automatically detected and classified to one of the considered fault categories. The method suggested does not require the knowledge/determination of the specific fault frequencies and/or any expert analysis: once the signal filtering is done and the PC's are found the PR method automatically gives the answer if there is a fault present and its type.     

基于集成NSET和模糊软聚类的风电机组齿轮箱状态监测

风电机组齿轮箱的故障频率和维修成本较高,有必要对其运行状态进行实时监测。非线性状态估计(NSET)算法有着对记忆矩阵依赖大、无法有效利用数据资源改善精度、实时性差等不足。为此,提出一种基于模糊软聚类和集成NSET的状态监测方法:使用模糊软聚类将历史数据分为边界有重叠的不同类别,实现工况的软划分并构造多个不同工况的NSET模型作为个体学习器;以参数回归方法作为结合器,可在不影响实时性的同时,使用大量数据训练参数以改善精度。用某2 MW风电机组的齿轮箱故障数据进行验证,结果表明,相比常规方法,提出方法的精度和实时性均更优;通过预测残差均值和基于残差构造的健康指数,能够灵敏、准确的反映齿轮箱的早期故障及其发展趋势。     

某柴油机油底壳仿真计算分析

本文利用ABAQUS结构仿真软件对某发电用柴油机油底壳进行模态、强度计算。在发动机实际运行工况下,油底壳底部为悬空状态,模态计算结果显示油底壳一阶模态距发动机点火激励频率点较近,低于激励频率的1.2倍,模态不满足设计要求;在发动机装配过程中,油底壳底部压在装配台上,发动机在上下方向三倍的重力加速度冲击载荷工况下,油底壳产生的应力最大值低于对应材料的屈服强度极限,强度满足设计要求。通过对油底壳两种状态下的仿真分析,验证和确保了油底壳结构设计的可行性,有效避免了发动机装配和实际运行过程中故障的发生。     

随车起重机变幅液压系统故障诊断仿真

针对液压系统中元件复杂、故障隐蔽性强而造成的诊断难题,提出了基于模型的故障诊断方法,为验证方法的可行性,进一步提出了故障诊断的仿真验证方法,搭建了其仿真模型。以某型随车起重机变幅液压系统的工作原理为基础,分析了变幅液压系统常见故障及故障注入方法,选取换向阀阀芯卡死故障进行诊断,通过阀芯液阻控制模块注入阀芯卡死故障,建立其功率键合图模型;其次,利用扩展遍历路径法生成一组解析冗余关系(ARR)的公式,并推导出阈值计算公式,采用MATLAB/Simulink平台搭建的故障诊断仿真模型计算出系统残差;最终,根据残差估计结果,对带有换向阀卡死故障的变幅液压系统进行诊断。通过诊断结果与初始故障注入对比,以及对随车起重机实验台的故障实验结果分析,验证了基于模型的液压系统故障诊断方法的可行性和高效性,为工程机械液压系统的故障诊断提供了新思路,并为预测液压系统剩余寿命奠定基础。     

基于小波包分析的液压泵状态监测方法

液压泵是液压系统中的关键部件,对其运行状态的监测与故障诊断对整个液压系统的可靠性具有重要意义。基于小波包分解和小波系数残差分析方法,提出一种利用液压泵出口压力进行液压泵故障诊断的方法。通过分析液压泵出口处压力信号的特征,利用小波包对压力信号进行频谱分解,提取液压泵的故障特征,建立不同频率范围的特征信号与液压泵不同故障因素的对应关系,为液压泵的故障诊断与定位提供依据。利用小波包能量残差判别液压泵的运行健康状态,并比较不同小波基函数在故障诊断时的敏感度。为减小小波分析时边界效应所引起的信号畸变,引入“滑动双窗口”的分析方法。试验结果表明,与快速傅里叶方法相比,基于小波包分解的残差分析方法可有效提高故障诊断的准确率,实现对液压泵的状态监测与故障诊断。     

轴流压缩机基于风险和状态的维修

轴流式压缩机在炼化行业应用广泛,其运行属高风险过程,研究集安全与经济为一体的维修方法十分必要。研究状态监测技术和以可靠性为中心的维修相结合的维修决策方法,对轴流压缩机进行故障特征信号分析、故障预警、剩余寿命预测及风险等级确定,实现了轴流压缩机基于风险和状态的维修。实践证明:轴流压缩机基于风险和状态的维修决策能够制定最佳的维修计划和维修任务,预知隐患和故障;降低设备的故障频率和故障后果影响;提高设备的可靠性和安全性。     

Multiple incipient sensor faults diagnosis with application to high-speed railway traction devices

This paper deals with the problem of incipient fault diagnosis for a class of Lipschitz nonlinear systems with sensor biases and explores further results of total measurable fault information residual (ToMFIR). Firstly, state and output transformations are introduced to transform the original system into two subsystems. The first subsystem is subject to system disturbances and free from sensor faults, while the second subsystem contains sensor faults but without any system disturbances. Sensor faults in the second subsystem are then formed as actuator faults by using a pseudo-actuator based approach. Since the effects of system disturbances on the residual are completely decoupled, multiple incipient sensor faults can be detected by constructing ToMFIR, and the fault detectability condition is then derived for discriminating the detectable incipient sensor faults. Further, a sliding-mode observers (SMOs) based fault isolation scheme is designed to guarantee accurate isolation of multiple sensor faults. Finally, simulation results conducted on a CRH2 high-speed railway traction device are given to demonstrate the effectiveness of the proposed approach.     

基于动态数据挖掘的热力参数传感器故障诊断

针对火电机组热力参数动态数据的海量、高维特点,提出了一种基于动态数据挖掘的热力参数传感器故障诊断新方法。该方法通过对热力参数信号进行经验模态分解,获得一系列平稳的本征模态函数(intrinsic mode function,简称IMF)分量和一个趋势余量,实现传感器故障特征信息的动态挖掘。以各IMF分量和趋势余量的方差作为特征向量构建欧氏距离判别函数,结合径向基函数神经网络确认传感器是否发生故障。根据专家经验得到的规则分析传感器测量值与理论值之间的差值,判别传感器的故障类型。以某电厂600MW火电机组实时运行数据为基础进行仿真实验,结果表明:该方法能够仅使用热力参数传感器正常状态下的样本,有效区分传感器故障造成的信号变化与机组本身正常负荷波动造成的信号变化,实现快速准确地对热力参数传感器的工作状态和故障类型进行判别。     

Fault diagnosis of motor drives using stator current signal analysis based on dynamic time warping

Electrical motor stator current signals have been widely used to monitor the condition of induction machines and their downstream mechanical equipment. The key technique used for current signal analysis is based on Fourier transform (FT) to extract weak fault sideband components from signals predominated with supply frequency component and its higher order harmonics. However, the FT based method has limitations such as spectral leakage and aliasing, leading to significant errors in estimating the sideband components. Therefore, this paper presents the use of dynamic time warping (DTW) to process the motor current signals for detecting and quantifying common faults in a downstream two-stage reciprocating compressor. DTW is a time domain based method and its algorithm is simple and easy to be embedded into real-time devices. In this study DTW is used to suppress the supply frequency component and highlight the sideband components based on the introduction of a reference signal which has the same frequency component as that of the supply power. Moreover, a sliding window is designed to process the raw signal using DTW frame by frame for effective calculation. Based on the proposed method, the stator current signals measured from the compressor induced with different common faults and under different loads are analysed for fault diagnosis. Results show that DTW based on residual signal analysis through the introduction of a reference signal allows the supply components to be suppressed well so that the fault related sideband components are highlighted for obtaining accurate fault detection and diagnosis results. In particular, the root mean square (RMS) values of the residual signal can indicate the differences between the healthy case and different faults under varying discharge pressures. It provides an effective and easy approach to the analysis of motor current signals for better fault diagnosis of the downstream mechanical equipment of motor drives in the time domain in comparison with conventional FT based methods.     

Rolling bearing fault diagnosis based on mean multigranulation decision-theoretic rough set and non-naive Bayesian classifier

To analyze data from multi-level view, reduce computational burden, and improve fault diagnosis accuracy, a novel fault diagnosis method of rolling bearings based on mean multigranulation decision-theoretic rough set (MMG-DTRS) and non-naive Bayesian classifier (NNBC) is proposed in this paper. First, fault diagnosis features of rolling bearings in training samples are extracted to construct MMG-DTRS. Then, the significance degree of condition attribute in MMG-DTRS is defined to quantitatively measure the influence of condition attributes with respect to the decision ability of an information system. An attribute reduction algorithm based on MMG-DTRS is applied to acquire a lower dimensional condition attribute set, which reduces computational complexity and avoids the interference of irrelevant or redundant condition attributes. Finally, NNBC is constructed to classify rolling bearing conditions in test samples. The classification procedures by using NNBC are given. The performance of the proposed method is validated and the advantages are investigated by using a fault diagnosis experiment of rolling bearings. Experimental investigations demonstrate the proposed method is effective and reliable in identifying fault categories and fault severities of rolling bearings.     

转子共频相关故障源源数估计与子带盲分离

针对转子异常振动产生含交叉频率的响应,其共频相关故障源不满足统计独立要求,提出利用非负矩阵法在频域中计算故障源个数,不计及源信号和混合系统特性,可以正确估计出故障源数目或源数上限。提出利用小波包分解故障信号,选择互信息较小的子带进行重构,剔除共频信号并进行盲分离,得到独立非相关的源信号,保留了故障信息。理论及实验结果证明了所提出方法的有效性。     

基于KPCA残差方向梯度的故障检测方法及应用

针对核主元分析(KPCA)在应用过程中非线性映射不存在原像、故障变量无法辨识、工程应用困难等问题,提出了一种改进的KPCA残差方向梯度故障检测方法。利用主元统计量和残差统计量的偏微分之间存在着相关性这一性质,对与主元统计量相关的格拉姆矩阵偏微分中间计算过程进行优化,提出一种新的KPCA残差方向梯度算法,在此基础上结合统计量形成系统故障检测的新方法。非线性系统仿真表明,改进的KPCA残差方向梯度法不仅具有较优的故障变量辨识能力,还极大地减小了计算量,缩短了计算时间。大型热力系统的应用进一步表明,无论对于单故障和多故障的情况,方法均具有较好的故障检测能力,并且不存在残差污染,易于工程实现。     

Robust detection of gearbox deterioration using compromised autoregressive modeling and Kolmogorov–Smirnov test statistic. Part II: Experiment and application

Optimal maintenance decision analysis is heavily dependent on the accuracy of condition indicators. A condition indicator that is subject to such varying operating conditions as load is unable to provide precise condition information of the monitored object for making optimal operational maintenance decisions even if the maintenance program is established within a rigorous theoretical framework. For this reason, the performance of condition monitoring techniques applied to rotating machinery under varying load conditions has been a long-term concern and has attracted intensive research interest. Part I of this study proposed a novel technique based on adaptive autoregressive modeling and hypothesis tests. The method is able to automatically search for the optimal time-series model order and establish a compromised autoregressive model fitting based on the healthy gear motion residual signals under varying load conditions. The condition of the monitored gearbox is numerically represented by a modified Kolmogorov–Smirnov test statistic.Part II of this study is devoted to applications of the proposed technique to entire lifetime condition detection of three gearboxes with distinct physical specifications, distinct load conditions, and distinct failure modes. A comprehensive and thorough comparative study is conducted between the proposed technique and several counterparts. The detection technique is further enhanced by a proposed method to automatically identify and generate fault alerts with the aid of the Wilcoxon rank-sum test and thus requires no supervision from maintenance personnel. Experimental analysis demonstrated that the proposed technique applied to automatic identification and generation of fault alerts also features two highly desirable properties, i.e. few false alerts and early alert for incipient faults. Furthermore, it is found that the proposed technique is able to identify two types of abnormalities, i.e. strong ghost components abruptly appearing in and disturbing the nominal operating process and the breakage of gear teeth, whereas conventional condition indicators are usually not capable of detecting the presence of the former.     

基于改进EMD与PNN的汽轮机通流部分故障诊断

根据热力参数非线性、非稳态的特点,提出了一种基于改进的经验模态分解(empirical mode decomposition,简称EMD)算法与概率神经网络(probabilistic neural network,简称PNN)的汽轮机通流部分故障诊断新方法。该方法针对EMD存在的端点效应问题,采取基于波形相似度的镜像延拓法进行改进,以得到更准确、更真实的本征模函数(intrinsic mode function,简称IMF)分量,从而有效提取了故障特征信息,并通过PNN训练判别汽轮机通流部分故障类型。以某电厂600MW火电机组实时运行数据为基础进行仿真实验,结果表明,基于改进EMD与PNN的汽轮机通流部分诊断方法能够快速准确地判别汽轮机通流部分的故障类型,其准确率明显高于基于EMD与PNN的故障诊断方法。     

Induction motors airgap-eccentricity detection through the discrete wavelet transform of the apparent power signal under non-stationary operating conditions

Fast Fourier transform (FFT) analysis has been successfully used for fault diagnosis in induction machines. However, this method does not always provide good results for the cases of load torque, speed and voltages variation, leading to a variation of the motor-slip and the consequent FFT problems that appear due to the non-stationary nature of the involved signals. In this paper, the discrete wavelet transform (DWT) of the apparent-power signal for the airgap-eccentricity fault detection in three-phase induction motors is presented in order to overcome the above FFT problems. The proposed method is based on the decomposition of the apparent-power signal from which wavelet approximation and detail coefficients are extracted. The energy evaluation of a known bandwidth permits to define a fault severity factor (FSF). Simulation as well as experimental results are provided to illustrate the effectiveness and accuracy of the proposed method presented even for the case of load torque variations.