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.

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.

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。

An Adaptive EMD Technique for Induction Motor Fault Detection

Reliable induction motor (IM) fault detection techniques are very useful in industries to diagnose IM defects and improve operational performance. An adaptive empirical mode decomposition (EMD) technology is proposed in this paper for rotor bar fault detection in IMs. As the characteristic fault frequency will change with operating conditions related to load and speed, the proposed adaptive EMD technique correlates fault features over different frequency bands and intrinsic mode function (IMF) sidebands. The adaptive EMD technique uses the first IMF to detect the fault type and the second IMF as an indicator to predict the fault severity. It can overcome the problems of the sensitivity of sideband frequencies related to the speed and load oscillations. The effectiveness of the proposed adaptive EMD technique is verified by experimental tests under different motor conditions.

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。

井下胶带运输机中减速器断轴的分析

随着煤矿开采工艺的升级,在挖掘效率不断提高下,虽然可以提高矿企的经济效益,不过也带给井下胶带运输机较大的运行压力,容易在超载、环境等多方面因素影响下频繁出现减速器断轴问题,一定程度会降低井下煤炭运输的安全性。如何有效结合常见的减速器断轴问题进行处理,也成为当前很多矿企需要考虑的问题,为此本文会先概述减速器的运行,然后分析影响断轴的各因素,最后提出针对性的解决策略,以期望可以降低减速器的故障率,满足矿企的可持续发展需要。

综掘煤巷过断层支护分析

针对2315辅助进风巷掘进期间揭露断层问题,为确保巷道安全高效掘进并实现断层影响区围岩变形有效控制,结合现场条件提出顶板采用全锚索支护、巷帮采用全长自巩固锚杆支护、在断层破碎带采用注浆锚索补强;考虑2315辅助进风巷后期切顶留巷需要,针对性对顶锚索及巷帮锚杆布置方案进行设计.现场应用后,2315辅助进风巷在断层影响范围内围岩始终保持稳定,围岩变形量在允许范围内,现场采用的支护措施可满足巷道快速掘进以及破碎围岩变形有效控制需要.

掘进巷道过断层破碎带支护实践研究

随着我国煤炭产量的不断增长和资源开采深度的增加,巷道掘进已成为制约矿井生产能力提高、成本增加和安全生产的主要因素之一。近年来,在掘进过程中遇到断层破碎带的情况越来越多,严重制约着巷道掘进速度,因此做好掘进巷道过断层破碎带的支护工作非常关键。基于此,本文结合某煤矿巷道开采工作展开分析,对本次巷道过断层破碎带的支护工作要点进行探索,保证工作面开采工作的安全进行,同时为后续类似工作的顺利开展提供参考。

基于零序分量的电网断线故障分析

当线路发生断线故障时,断线相电流降为0,零序电流上升,零序功率为正方向。因此,基于零序分量的分析,继电保护可准确地识别出断线故障并动作跳闸,从而防止线路非全相运行。以一起断线故障为例,分析零序保护在断线故障中的识别和动作过程,以利于继电保护人员在电网发生跳闸事故时及时做出判断并进行处置。

基于机器学习算法的10kV配网断线故障定位方法研究

为减小10kV配网断线故障定位误差,提高定位准确性,利用机器学习算法,提出了一种全新的10kV配网断线故障定位方法。首先,利用数据采集设备,实时采集配网运行数据;其次,从采集到的数据中,选择与断线故障相关性较高、对断线故障敏感且区分度高的特征;在此基础上,利用机器学习算法,初步判定故障区段,进而在该区段内精确定位故障。实验结果表明,提出的方法应用后,断线故障定位最大误差值不超过0.09km,展现出卓越的定位精度和稳定性。

电网单相断线故障类型及电压特征

断线故障包括断线不接地、断线电源端接地、断线负荷端接地等。而断线故障类型、位置、系统中性点接地方式等均会影响系统电压幅值和相位等电气量特征。为挖掘各种断线故障在不同情景下的电压特征,系统地归纳和总结了单相断线故障类型,分析了不同断线故障下中性点可能的移动范围及断线后断口两端电压变化特征,运用对称分量法推导了110 kV断线时,负荷侧35 kV、10 kV母线电压特征,对断线故障定位、电网故障判别与处置具有现实意义。

北辛窑矿南翼回风大巷过断层支护技术分析

北辛窑矿南翼回风大巷通过断层破碎带时,因围岩裂隙较发育、夹带部分裂隙水,造成巷道稳定性差,岩巷若采用“锚网索喷”支护,不足以保证岩巷围岩稳定及其安全使用;决定采用“锚网索+U型棚+注浆+喷浆”联合支护技术方案,并通过实时矿压监测巷道围岩变形;实践表明,联合支护技术方案应用后巷道顶板未出现明显变形,巷道支护质量逐步趋于稳定,能满足巷道的正常施工和安全使用。

深部开采巷道掘进过导水断层围岩控制技术研究

12201运输巷沿着深部区域的12^(#)煤层底板掘进,巷道掘进区域存在有DF6-1、DF6-3导水裂隙,虽进行超前处理,但在扰动影响下巷道仍会出现淋水情况,加之围岩破碎、岩体为软岩,制约巷道掘进过断层围岩支护工作。结合现场条件,提出通过加固围岩、封堵导水裂隙方式提高围岩自身承载能力,降低淋水对破碎围岩影响,现场应用后,12201运输巷过断层期间,围岩未有淋水情况出现,同时巷道顶底板、两帮变形量均较小,实现了巷道导水断层围岩变形有效控制。

断层防水煤柱侧工作面“采帮钻孔+断顶卸压”防冲研究

大直径钻孔卸压是冲击地压煤层常用的卸压手段,但在断层附近施工大直径钻孔可能会出现断层导水或无法施工至设计孔深等问题,而断顶爆破卸压可以作为一种补充卸压措施。以某矿采煤工作面为工程背景,分析了煤柱应力特征,模拟研究了不同方案卸压效果,针对具体工况设计了卸压方案,并进行了现场应用。研究结果表明,受回采影响,靠近工作面采空区一侧的煤柱应力集中程度较高;对比分析了“采帮钻孔+断顶卸压”与“两帮钻孔卸压”效果,发现采用“采帮钻孔+断顶卸压”方案后煤柱应力降低较明显;将该方案进行了现场应用,工作面回采期间煤柱侧未发生应力预警,保证了工作面的安全回采。

应用探地雷达探测活动断层

在浅覆盖区采用探地雷达探测地震活动断层。通过实验确定出最佳的采集参数和数据处理流程,在雷达剖面上,能够清晰地显示出断层上部的形态特征、上断点埋深和岩土分层。结合钻孔资料,利用雷达剖面上对第四系覆盖的分层结果和上断点所在的层位可以分析和评价断层的活动性。

直流侧故障对模块化多电平换流器的影响研究

对于直流侧双极性短路故障,首先分析了故障通路对电容闭锁充电以及故障对交流侧功率的影响,然后对如何减少故障对交流侧无功的影响提出了建议。对于直流侧单极接地故障,通过仿真换流站不同接地电阻下,故障对系统稳定性的影响,对接地电阻的选择提供了建议。对于直流侧断线故障,分析了故障发生后控制器的作用,并给出了不同控制方式下,故障对系统稳定性的影响。此外,就双极短路和断线故障后闭锁时刻对系统的影响进行了分析。最后,在PSCAD/EMTDC中建立HBSM-MMC模型验证了分析的正确性。

陆相盆地破碎断块区储层预测方法研究

陆相盆地破碎断块区具有砂体单层厚度薄、横向连续性差、地震反射紊乱等特征,采用常规的储层预测方法很难得到好的预测效果。针对这一问题,进行了储层预测综合方法研究:①采用高分辨率层序地层分析方法和相干体技术建立高精度地层约束模型;②采用地质约束的测井归一化处理、多道记录自相关统计、精确的井震标定等手段建立精确的测井约束模型;③利用沉积相约束反演过程,地质因素约束储层解释过程,使预测结果更符合地质规律。利用以上方法在东濮凹陷某三维地震工区进行了储层预测,预测的砂体分布趋势与沉积微相的解释结果相关程度较高,符合该区的地质规律。

基于Hilbert模量频谱分析的异步电机转子断条故障研究

转子绕组断条是异步电机最常见故障之一。在分析当前主要诊断方法的基础上,提出Hilbert模量频谱分析方法对异步电机转子断条故障进行研究。将Hilbert模量定义为原始信号与其共轭信号的平方和,利用Hilbert模量将原相电流中的基波分量转变为直流分量,原频谱分析的转绕组断条的故障特征分量转变为频率为二倍转差频率的电流分量,决了相电流频谱分析方法中断条故障特征成分容易被基波湮没而难以突出故障特征的问题。文中研究了Hilbert模量频谱分析方法的原理和实现方法,并对偏心故障电机和转子断条电机分别进行仿真和实验研究。仿真分析和实验结果均表明,该方法对异步电机转子断条故障诊断具有较高的诊断精度和很好的频率分辨率,能克服传统的电流频谱法主频泄漏的影响,且工程实现方便。