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.

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。

倾斜工作面面向断层开采煤柱失稳机制及稳定性控制

以某矿面向断层开采的5302工作面为工程背景,模拟了工作面回采过程中断层及断层周围煤岩体垂直应力、塑性区及断层滑移演化规律,提出了断层煤柱的合理尺寸,并制定了卸压方案。研究结果表明:工作面回采期间断层及断层周围煤岩体受构造应力与采动应力叠加作用下应力高度集中,容易诱发冲击地压;微震、应力在线监测结果对比分析表明,制定的爆破断顶、钻孔卸压技术方案能够有效降低应力集中程度,避免工作面面向断层开采时诱发冲击危险。

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。

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。

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。

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。

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。

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。

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.

蚀变断层破碎金矿高分段空场嗣后充填法采场稳定性研究

针对矿山采用高分段空场嗣后充填法时出现分段高度增大、单次爆破后空区暴露面积增大、容易引发采场围岩冒落等安全问题,以某蚀变断层破碎金矿山的采矿方法研究为例,在钻孔岩芯调查、点荷载及室内测试的基础上,使用RMR法及Q系统进行了岩体分类,引用量化性等概率线Mathews合成图,对60 m中段高度下,采宽8~15 m、采长20~50 m变化范围进行了稳定概率横向梯度对比。通过比较分析结果与试验结果表明,受断层及平行蚀变带影响,矿体东端、西端局部破碎,随矿体埋深增大,上盘平行矿脉尖灭,西区矿岩完整性提高;东区矿体(厚度30 m)推荐采场宽度设置为11 m,西区矿体(厚度25 m),采场宽度宜控制在10.5 m以内,此时采场各暴露面稳定概率均大于80%。该方法的应用为矿山安全开采起到了关键技术作用,可以为采用类似采矿方法的矿山提供重要技术支撑。

基于ESPRIT-PSA与LightGBM算法的感应电动机转子断条数目诊断新方法

提出一种基于旋转不变信号参数估计技术ESPRIT(Estimation of signal parameters via rotational invariance technique)、模式搜索算法PSA(Pattern search algorithm)与轻型梯度提升机LightGBM(Light gradient boosting machine)结合的感应电动机转子断条数目诊断新方法。模拟了转子断条故障下的瞬时无功功率信号并用其衡量ESPRIT-PSA的性能。结果表明:ESPRIT-PSA只需短时数据就能准确测量瞬时无功功率信号中的转子断条故障特征分量。随后,为解决现有的电机瞬时无功功率信号分析MIRPSA(Motor instantaneous reactive power signal analysis)类方法无法准确诊断转子断条数目的问题,引入LightGBM对转子断条故障进行多分类以准确诊断转子断条数目。最后针对一台异步电动机进行转子断条诊断实验,结果表明:该方法是有效的,并且因将瞬时无功功率作为分析信号而适用于电机低转差率的情况。

高速列车牵引电机转子断条和速度传感器联合诊断方法

为提升高速列车牵引系统的稳定性和可靠性,针对其牵引电机提出一种基于未知输入观测器的转子断条和速度传感器故障联合诊断方法.首先,通过非奇异坐标变换,将牵引电机系统解耦为两个分别只包含转子断条故障和速度传感器故障的子系统,实现转子断条故障与速度传感器故障的解耦,并进一步利用一阶低通滤波器将含速度传感器故障的子系统转化为增广系统.其次,对含转子断条故障的子系统和速度传感器故障增广系统分别设计未知输入区间观测器和未知输入滑模观测器.在此基础上,采用未知输入区间观测器上界和下界构建转子断条故障诊断的检测变量和自适应阈值,利用未知输入滑模观测器的等效输出控制原理实现速度传感器故障估计.最后,通过仿真和TDCS-FIB平台实验验证了所提方法的有效性和鲁棒性.

断层蠕滑作用下活动断裂带地应力分布研究

采用非连续变形分析(DDA)方法研究断层蠕滑过程中嘉黎活动断裂带区域应力分布的变化规律。结果表明,断层蠕滑会导致岩体中应力明显增长,随着深度的增加应力有相近的变化趋势;断层破碎带中的应力普遍低于两侧岩体中的应力;断层蠕滑会使岩体内发育大量裂隙,造成破碎岩体中应力分布不连续;断层破碎带及其两侧岩体中出现应力集中的区域在不同深度下的分布有一定的相似性。

基于降噪重采样与LSTM网络的风力机断齿故障诊断

齿轮箱是风电机组传动链上的关键部件,若其发生断齿等故障,对风力机整体的运行有很大影响,维修不及时会导致损失增大,因此有必要对其进行健康状态监测。由于风力机具有工况不稳定、故障特征不明显、有效标签稀缺、风力机类别差异对分类结果影响大等问题,且常规方法预测精度低,分类模型难以有效推广。针对这些问题,本文提出了基于降噪重采样与LSTM网络的风力机断齿故障诊断方法,将单位周期内的样本点降噪处理后重采样到相同长度,输入到LSTM网络对处理后的数据进行分类,实现了较高精度的风力机断齿故障诊断。最后,通过某风场的实际数据验证了所提出方法的精确性和有效性。