A Comprehensive 3-Steps Methodology for Vibration-Based Fault Detection,Diagnosis and Localization in Rotating Machines

In any industry,it is the requirement to know whether the machine is healthy or not to operate machine further.If the machine is not healthy then what is the fault in the machine and then finally its location.The paper is proposing a 3-Steps methodology for the machine fault diagnosis to meet the industrial requirements to aid the maintenance activity.The Step-1 identifies whether machine is healthy or faulty,then Step-2 detect the type of defect and finally its location in Step-3.This method is extended further from the earlier study on the 2-Steps method for the rotor defects only to the 3-Steps methodology to both rotor and bearing defects.The method uses the optimised vibration parameters and a simple Artificial Neural Network(ANN)-based Machine Learning(ML)model from the earlier studies.The model is initially developed,tested and validated on an experimental rotating rig operating at a speed above 1st critical speed.The proposed method and model are then further validated at 2 different operating speeds,one below 1st critical speed and other above 2nd critical speed.The machine dynamics are expected to be significantly different at these speeds.This highlights the robustness of the proposed 3-Steps method.

Calculation of torque and speed of induction machines under rotor winding faults

Based on the multi-loop method, the rotating torque and speed of theinduction machine are analyzed. The fluctuating components of the torque and speed caused by rotorwinding faults are studied. The models for calculating the fluctuating components are put forward.Simulation and computation results show that the rotor winding faults will cause electromagnetictorque and rotating speed to fluctuate; and fluctuating frequencies are the same and their magnitudewill increase with the rise of the severity of the faults. The load inertia affects the torque andspeed fluctuation, with the increase of inertia, the fluctuation of the torque will rise, while thecorresponding speed fluctuation will obviously decline.

A Fault Diagnosis Approach for Broken Rotor Bars Based on EMD and Envelope Analysis

Empirical Mode Decomposition (EMD) used to deal with non-linear and non-stable signals,is a time-frequency analytical method that has been developed recently. In this paper the EMD method is used to filter the noise from the stator current signal that arises when rotor bars break. Then a Hilbert Transform is used to extract the envelope from the filtered signal. With the EMD method again,the frequency band containing the fault characteris-tic-frequency components,2sf,can be extracted from the signal's envelope. The last step is to use a Fast Fourier Trans-form (FFT) method to extract the fault characteristic frequency. This frequency can be detected in actual data from a faulty motor,as shown by example. Compared to the Extend Park Vector method this method is proved to be more sen-sitive under light motor load.

GENERATOR VIBRATION FAULT DIAGNOSIS METHOD BASED ON ROTOR VIBRATION AND STATOR WINDING PARALLEL BRANCHES CIRCULATING CURRENT CHARACTERISTICS

Rotor vibration characteristics are first analyzed, which are that the rotor vibration of fundamental frequency will increase due to rotor winding inter-turn short circuit fault, air-gap dynamic eccentricity fault, or imbalance fault, and the vibration of the second frequency will increase when the air-gap static eccentricity fault occurs. Next, the characteristics of the stator winding parallel branches circulating current are analyzed, which are that the second harmonics circulating current will increase when the rotor winding inter-turn short circuit fault occurs, and the fundamental circulating current will increase when the air-gap eccentricity fault occurs, neither being strongly affected by the imbalance fault. Considering the differences of the rotor vibration and circulating current characteristics caused by different rotor faults, a method of generator vibration fault diagnosis, based on rotor vibration and circulating current characteristics, is developed. Finally, the rotor vibration and circulating current of a type SDF-9 generator is measured in the laboratory to verify the theoretical analysis presented above.

HIGHER ORDER SPECTRAL ANALYSIS IN FAULT DIAGNOSIS OF ROTORS

The nonlinear properties of rotating machinery vibration signals are presented. The relationship between faults and quadratic phase coupling is discussed. The mechanism that gives rise to quadratic phase coupling is analyzed, and the coupling models are summarized. As a result, higher order spectra analysis is introduced into fault diagnosis of rotors. A brief review of the properties of higher order spectra is presented. Furthermore, the bicoherence spectrum is employed to extract the features that signify the machinery condition. Experiments show that bicoherence spectrum patterns of different faults are quite different, so it is proposed to identify the faults in rotors.

Online Detection of Broken Rotor Bar Fault in Induction Motors by Combining Estimation of Signal Parameters via Min-norm Algorithm and Least Square Method

Current research in broken rotor bar （BRB） fault detection in induction motors is primarily focused on a high-frequency resolution analysis of the stator current. Compared with a discrete Fourier transformation, the parametric spectrum estimation technique has a higher frequency accuracy and resolution. However, the existing detection methods based on parametric spectrum estima- tion cannot realize online detection, owing to the large computational cost. To improve the efficiency of BRB fault detection, a new detection method based on the min-norm algorithm and least square estimation is proposed in this paper. First, the stator current is filtered using a band-pass filter and divided into short overlapped data windows. The min-norm algorithm is then applied to determine the fre- quencies of the fundamental and fault characteristic com- ponents with each overlapped data window. Next, based on the frequency values obtained, a model of the fault current signal is constructed. Subsequently, a linear least squares problem solved through singular value decomposition is designed to estimate the amplitudes and phases of the related components. Finally, the proposed method is applied to a simulated current and an actual motor, the results of which indicate that, not only parametric spectrum estimation technique.

Broken Rotor Bar Fault Detection of Induction Motors Using a Joint Algorithm of Trust Region and Modified Bare-bones Particle Swarm Optimization

A precise detection of the fault feature parameter of motor current is a new research hotspot in the broken rotor bar(BRB) fault diagnosis of induction motors. Discrete Fourier transform(DFT) is the most popular technique in this field, owing to low computation and easy realization. However, its accuracy is often limited by the data window length, spectral leakage, fence e ect, etc. Therefore, a new detection method based on a global optimization algorithm is proposed. First, a BRB fault current model and a residual error function are designed to transform the fault parameter detection problem into a nonlinear least-square problem. Because this optimization problem has a great number of local optima and needs to be resolved rapidly and accurately, a joint algorithm(called TR-MBPSO) based on a modified bare-bones particle swarm optimization(BPSO) and trust region(TR) is subsequently proposed. In the TR-MBPSO, a reinitialization strategy of inactive particle is introduced to the BPSO to enhance the swarm diversity and global search ability. Meanwhile, the TR is combined with the modified BPSO to improve convergence speed and accuracy. It also includes a global convergence analysis, whose result proves that the TR-MBPSO can converge to the global optimum with the probability of 1. Both simulations and experiments are conducted, and the results indicate that the proposed detection method not only has high accuracy of parameter estimation with short-time data window, e.g., the magnitude and frequency precision of the fault-related components reaches 10^(-4), but also overcomes the impacts of spectral leakage and non-integer-period sampling. The proposed research provides a new BRB detection method, which has enough precision to extract the parameters of the fault feature components.

H _ index based fault estimation for micro quad-rotor

Taking the attitude control system of micro quad-rotor as a research object, a design scheme of fault estimator based on generalized Kalman-Yakubovic-Popov （GKYP） lemma is put forward to deal with the problem of estimating multiple actuators malfunctions with couplings. Using an H_index and an appropriate algorithm, the goal of weakening the coupling can be achieved by limiting the fault frequency to a certain range, then different kinds of actuator faults can be estimated correctly. The simulations demonstrate the reliability and validity of the proposed method.

Modelica-based Object-orient Modeling of Rotor System with Multi-Faults

Modelica-based object-orient method is proved to be rapid, accurate and easy to modify, which is suitable for prototype modeling and simulation of rotor system, whose parameters need to be modified frequently. Classical non-object-orient method appears to be inefficient because the code is difficult to modify and reuse. An adequate library for object-orient modeling of rotor system with multi-faults is established, a comparison with non-object-orient method on Jeffcott rotor system and a case study on turbo expander with multi-faults are implemented. The relative tolerance between object-orient method and non-object-orient is less than 0.03%, which proves that these two methods are as accurate as each other. Object-orient modeling and simulation is implemented on turbo expander with crack, rub-impact, pedestal looseness and multi-faults simultaneously. It can be conclude from the case study that when acting on compress side of turbo expander separately, expand wheel is not influenced greatly by crack fault, the existence of rub-impact fault forces expand wheel into quasi-periodic motion and the orbit of expand wheel is deformed and enhanced almost 1.5 times due to pedestal looseness. When acting simultaneously, multi-faults cannot be totally decomposed but can be diagnosed from the feature of vibration. Object-orient method can enhance the efficiency of modeling and simulation of rotor system with multi-faults, which provides an efficient method on prototype modeling and simulation.

A Study on Turbo-rotor Multi-fault Diagnosis Based on a Neural Network

The multi-fault phenomena are common in the turbo-rotor system of a liquidrocket engine. As it has many excellent qualities, the neural network might be used to solve theproblems of multi-fault diagnosis of a turbo-rotor system. First, the feature expression of a commonturbo-rotor fault was studied in order to build up the standard fault pattern and satisfy the needof neural network studying and diagnosing. Then, the turbo-rotor fault identification and diagnosisproblems were investigated by using a BP( back-propagation) neural network. According to the BPneural network problems, the parallel BP neural network method of multi-fault diagnosis andclassification was presented and investigated. The results indicated that the parallel BP neuralnetwork method could solve the turbo-rotor multi-fault diagnosis problems.

Research on Rubbing-fault Diagnosis System in High-speed Rotor based on LabVIEW

The rubbing between rotors and determiners is the common mechanic vibration fault in the operation of rotation machinery. During the operation of equipment, in order to meet the demand of high speed and efficiency of machinery, the gap between the active and passive parts of the rotor system become smaller, which results in the common rubbing fault of rotors and stators. This essay studies the fault diagnosis of high speed rotors based on invented instrument and shows the measurement and research of the signals of rubbing failure of high speed rotors. The research introduces the designed software and hardware which are experimented and testified on Bentley rotor experiment platform. The system has theoretical and applicative meaning in practical projects.

Broken Rotor Bar Fault Diagnosis of Induction Motors Using a Hybrid Bare-bones Particle Swarm Optimization Algorithm

在传统定子电流频谱分析中,感应电机转子断条故障特征经常被基波分量淹没而无法准确检测。针对该问题,提出一种基于混合骨干微粒群优化算法的转子断条故障诊断新方法。该方法首先根据电流信号与单位余弦基函数的内积最大准则,利用混合骨干微粒群算法强大的全局搜索能力,准确估计出基波波形参数;然后利用波形参数构造出基波表达式,并将其从原电流信号中剔除,达到突出故障特征的目的。针对微粒群算法在进化后期收敛缓慢的缺点,通过K–均值聚类方式,引入单纯形法对其进行改进,使整个算法的广度探索与深度开发能力得到了有效均衡。最后,对模拟数据和实测信号进行实验,结果验证了所提方法的有效性和优越性。

供电电压闪变对异步电动机转子故障在线检测的影响及应对

供电电压闪变可能对异步电动机转子故障在线检测产生不利影响,导致基于定子电流信号分析(motor current signature analysis,MCSA)的转子故障在线检测方法失效。通过理论分析,揭示供电电压闪变恶化转子故障在线检测性能的机制。提出免于供电电压闪变影响的异步电动机转子故障在线检测方法,首先,根据转子故障主特征频率分量预判转子健康或故障;继而,根据转子故障独有的辅助特征频率分量进一步确认转子健康或故障。仿真与实验结果证明了该方法的有效性。

基于SVD-CWT和CNN的水轮发电机转子故障识别

水轮发电机转子振动故障识别是水电站运维的重难点问题,为此提出一种基于转子振动信号的故障识别方法。首先针对发电机转子的非平稳和非线性振动信号,采用奇异值分解(SVD)并结合能量差分谱理论进行降噪预处理;对预处理数据使用连续小波变换(CWT)转换为时频图并形成图像数据集;然后将该图像数据集作为卷积神经网络(CNN)输入,通过CNN多层池化及卷积形成分布式故障特征表达,最终实现发电机转子故障模式识别和分类。经实验验证,该方法准确率达到99.5%以上,能有效识别出发电机转子的故障类型。

联合收割机裂纹转子与滚动轴承故障诊断系统研究--基于卷积神经网络

首先,介绍了传统神经网络,在其基础上引出了改进的卷积神经网络;然后,搭建了转子和滚动轴承的动力学模型,对转子和轴承的裂纹模型进行分析研究;最后,实现了联合收割机裂纹转子与滚动轴承故障诊断系统。实验结果表明:基于卷积神经网络的诊断模型达到稳定识别精度的迭代次数更少,且识别精度更高,效果更好,证明了系统的可行性和可靠性。

滚动球轴承局部故障引起滚珠负载振荡性分析

为深入剖析滚动球轴承局部故障对轴承整体振动特性的影响,利用Hertz接触理论构建了转子偏心力激励下的球轴承-转子系统动力学数字仿真模型,并结合深沟球轴承外圈、内圈、滚珠的局部故障引起的轴承间隙变化,推演了滚珠与各类故障区域间的冲击激励响应方程。在此基础上通过分析单点损伤引起的滚珠内部接触负载变化,揭示了单个滚珠接触应力对相邻滚子负载与系统整体振动特性的影响规律。通过分析轴承转动过程中有效承载滚珠个数的变化情况,建立了的滚珠负载占比时间对故障冲击振荡幅值的影响关系。分析结果表明,滚珠与故障区域的接触分离会使其负载发生振荡,且各故障状态下的振荡频率都为系统共振频率;在2个等效承载滚珠上的运转时间占比越大,故障冲击振荡的强度越大;各类型早期故障状态下的Poincaré映射点的保持在相同幅值附近;当局部故障程度增大到一定程度时,系统振动混沌特性增强,振动幅值相应增大。

基于加权D-S证据理论的旋翼故障诊断

旋翼作为直升机的升力面和操作面,其健康状态对直升机的安全至关重要。旋翼故障诊断技术仍是直升机健康与使用监测系统(Health and usage monitoring system, HUMS)领域的薄弱环节,开发旋翼故障诊断技术具有重要价值。基于信息融合技术,首先分析了旋翼故障的诊断机理,建立了旋翼故障模型,通过流固耦合仿真获取了不同故障下桨叶、轮毂和机身的故障特征信息,生成数据集进行网络训练和验证。然后,利用遗传算法反向传播(Genetic algorithm-backpropagation, GA-BP)优化神经网络诊断3种类型的直升机旋翼故障,即后缘调整片误调、变距拉杆误调和桨叶质量不平衡。3个逐级神经网络分别对旋翼故障类型、故障位置和故障程度进行了诊断识别。最后采用加权的Dempster-Shafer(D-S)证据理论对旋翼故障进行诊断和分析。结果证明基于改进D-S证据理论的旋翼故障诊断方法能够成功应用到旋翼故障诊断中,并具有良好的识别效果。

基于小波包能量分析和信号融合的异步电机转子故障诊断

为提高异步电机转子故障诊断的可靠性,文中介绍了一种基于小波包能量分析和信号融合的异步电机转子故障诊断方法。采用定子电流信号和振动信号的频谱特征融合作为转子断条以及气隙偏心故障的诊断依据,首先对信号进行小波包分解,获得不同小波包频带节点下对应的能量分布,并与正常电机信号进行比较,进而对能量异常的信号频段进行小波包节点重构,最后通过快速傅里叶变换识别故障特征频率,诊断电机故障是否发生。通过仿真分析,验证了该方法的有效性和实用性,对于电机运行状态的准确监测具有重要意义。

同步发电机励磁变压器低压侧单相接地故障识别与定位方法

同步发电机励磁变压器低压侧未配置零序电压接地保护,当发生单相接地故障时会造成转子接地保护动作,从而误导故障排查方向。为实现励磁变低压侧单相接地故障准确识别与定位,利用叠加原理分析发生单相接地故障时转子对地电压的故障特征,提出当转子对地电压工频分量与励磁变高压侧相电压之比超过判据门槛,且转子对地绝缘电阻值较低时,判定故障点在励磁变压器低压侧。进一步地,通过分析转子对地电压工频分量与励磁变高压侧相电压的相位关系,实现励磁变压器低压侧接地故障相判别。通过仿真模型和动模实验验证了所提方法的正确性和有效性。

抑制风机超速脱网的永磁风电场经柔直系统并网的受端交流故障穿越控制策略

永磁风电场经柔直外送的系统发生受端交流电压跌落时,利用转子储能进行风机减载控制的策略可能存在超速脱网问题。对此,提出一种抑制转子超速脱网的风电场减载控制策略。该策略通过转子转速与风机有功功率的关系,对风电机组减载期间的最小有功功率整定,确定风电场的最小有功输出,尽可能地减小直流两侧不平衡功率。严重故障时与直流卸荷电阻配合,快速减载的同时减小卸荷电阻承担的容量。搭建了基于某型号的永磁风电机组构成的风电场经柔直外送的仿真模型,对所提控制策略与传统控制策略进行仿真对比分析,验证了所提控制策略的有效性。