Research on Instantaneous Angular Speed Signal Separation Method for Planetary Gear Fault Diagnosis

Planetary gear train is a critical transmission component in large equipment such as helicopters and wind turbines. Conducting damage perception of planetary gear trains is of great significance for the safe operation of equipment. Existing methods for damage perception of planetary gear trains mainly rely on linear vibration analysis. However, these methods based on linear vibration signal analysis face challenges such as rich vibration sources, complex signal coupling and modulation mechanisms, significant influence of transmission paths, and difficulties in separating damage information. This paper proposes a method for separating instantaneous angular speed (IAS) signals for planetary gear fault diagnosis. Firstly, this method obtains encoder pulse signals through a built-in encoder. Based on this, it calculates the IAS signals using the Hilbert transform, and obtains the time-domain synchronous average signal of the IAS of the planetary gear through time-domain synchronous averaging technology, thus realizing the fault diagnosis of the planetary gear train. Experimental results validate the effectiveness of the calculated IAS signals, demonstrating that the time-domain synchronous averaging technology can highlight impact characteristics, effectively separate and extract fault impacts, greatly reduce the testing cost of experiments, and provide an effective tool for the fault diagnosis of planetary gear trains.

Dynamic Distribution Adaptation Based Transfer Network for Cross Domain Bearing Fault Diagnosis

In machinery fault diagnosis,labeled data are always difficult or even impossible to obtain.Transfer learning can leverage related fault diagnosis knowledge from fully labeled source domain to enhance the fault diagnosis performance in sparsely labeled or unlabeled target domain,which has been widely used for cross domain fault diagnosis.However,existing methods focus on either marginal distribution adaptation(MDA)or conditional distribution adaptation(CDA).In practice,marginal and conditional distributions discrepancies both have significant but different influences on the domain divergence.In this paper,a dynamic distribution adaptation based transfer network(DDATN)is proposed for cross domain bearing fault diagnosis.DDATN utilizes the proposed instance-weighted dynamic maximum mean discrepancy(IDMMD)for dynamic distribution adaptation(DDA),which can dynamically estimate the influences of marginal and conditional distribution and adapt target domain with source domain.The experimental evaluation on cross domain bearing fault diagnosis demonstrates that DDATN can outperformance the state-of-the-art cross domain fault diagnosis methods.

Multi-Fault Diagnosis for Autonomous Underwater Vehicle Based on Fuzzy Weighted Support Vector Domain Description

This paper addresses the multi-fault diagnosis problem of thrusters and sensors for autonomous underwater vehicles (AUVs). Traditional support vector domain description (SVDD) has low classification accuracy in the process of AUV multi-fault pattern classification because of the effect of sample sparse density and the uneven distribution of samples, and so on. Thus, a fuzzy weighted support vector domain description (FWSVDD) method based on positive and negative class samples is proposed. In this method, the negative class sample is introduced during classifier training, and the local density and the class weight are introduced for each sample. To improve the multi-fault pattern classifier training speed and fault diagnosis accuracy of FWSVDD, a multi-fault mode classification method based on a hierarchical strategy is proposed. This method adds fault contain detection surface for each thruster and sensor to isolate fault components during fault diagnosis. By considering the problem of pattern classification for a fuzzy sample, which may be located in the overlapping area of hyper-spheres or may not belong to any hyper-sphere in the process of multi-fault classification based on FWSVDD, a relative distance judgment method is given. The effectiveness of the proposed multi-fault diagnosis approach is demonstrated through water tank experiments with an experimental AUV prototype.

Parameter identification algorithm for fault location using one terminal data based on frequency domain

This paper presents a novel algorithm of fault location for transmission line.Solving the network spectrum equations for different frequencies the fault can be located accurately by this algorithm with one terminal data of voltage and current,and the identified parameters,such as fault distance, fault resistance,and opposite terminal system resistance and inductance.The algorithm eliminates the influence of the opposite system impedance on the fault location accuracy,which causes the main error in traditional fault location methods using one terminal data.A method of calculating spectrum from sampled data is also proposed.EMTP simulations show the validity and higher accuracy of the fault location algorithm compared to the existing ones based on one terminal data.

轴承变工况故障的域自适应迁移深度学习诊断

为了实现变设备、变工况条件下的轴承故障精确识别,提出了基于域自适应迁移深度卷积神经网络的诊断方法。对于具有不同分布特征(即不同域)的训练集和测试集,在深度卷积神经网络中构造了故障特征提取模块、域识别模块、标签分类模块,以特征提取模块与域识别模块对抗训练的方式实现域自适应迁移能力,使深度卷积神经网络能够有效提取不同域的共同特征参数。使用凯斯西储大学和智能维护系统中心数据设计了4组迁移实验,传统深度卷积神经网络的识别精度均值为64.5%,域自适应迁移卷积神经网络的识别精度均值为94.9%,充分说明了域自适应迁移深度卷积神经网络能够有效识别变设备、变工况条件下的轴承故障。

架空配电线路故障电弧的电磁辐射特性及故障定位应用

针对架空配电线路电弧接地故障点定位难题,该文研究架空配电线路故障电弧的电磁辐射特性,探索基于电磁辐射信号的电弧故障定位方法的可行性。通过10 k V配网真型故障模拟试验平台,分析接地电弧电磁辐射的时域与频域特性及传播衰减规律,结果表明:电弧电流的电磁辐射特征频段为20~30 MHz,该特征频段不会受到中性点接地方式、电弧接地介质与线路结构参数的显著影响,且特征频段内辐射信号在传播过程中衰减较慢。在此基础上,设计一种小型化三角形单极子–环形组合平面天线,工作频率为20~500 MHz。利用自制天线开展小型电弧故障定位实验,为后续配网电弧故障定位的应用研究提供基础。

基于振动监测的电机轴承故障智能诊断

电机轴承故障是电机常见故障之一。对电机轴承故障进行精确诊断是确保电机安全稳定运行的必要措施。现提出了一种基于振动监测的电机轴承故障智能诊断方法。利用振动加速度传感器监测电机轴承振动,采集电机振动加速度数据,然后对电机振动加速度数据进行时域分析和频域分析,提取与电机轴承故障相关的时域特征参数和频域特征参数,再依据电机故障故障特征参数判断电机是否存在轴承故障。实际应用表明,电机轴承故障智能诊断方法可对电机轴承故障进行精确诊断。

中性点柔性接地配电网故障相恢复电压暂态时域特征分析

配电网接地故障过零熄弧后的电压暂态恢复特性决定了系统过电压水平及电弧重燃特性,目前,中性点柔性接地配电网故障熄弧后暂态恢复电压变化机理尚不明确,为此建立柔性接地配电网接地故障暂态等值电路,理论解析故障过零熄弧后故障恢复电压的时域表达式。在此基础上,对比分析配电网谐振接地与柔性接地方式下,系统参数变化对故障相恢复电压暂态峰值与恢复速度的影响,揭示暂态时间尺度下故障相恢复电压特征随注入零序电流值与电流注入时机的变化规律,阐明通过中性点注入可控零序电流抑制故障暂态过电压的机理。在PSCAD/EMTDC仿真环境与10 kV真型配电网实验场中模拟各种运行和故障工况,仿真与真型实验结果均验证柔性接地配电网故障相恢复电压时域解析特征与变化规律的正确性。

ToMFIR-based fault detection approach in frequency domain

This paper is to explore further results for total measurable fault information-based residual（ToMFIR） approach to fault detection in dynamic systems.The ToMFIR contains the essential fault information and remains unaffected by control actions in a closed-loop system.It is composed of controller residual and output residual and some of further results are developed in frequency domain.Besides the ability of detecting actuator and sensor faults,it is able to detect faults/failures resulting from the computer used for control purpose that generates control signals.Currently,all of existing fault detection schemes cannot achieve the same task at all.A practical DC motor example,with a PID controller,is used to demonstrate the effectiveness of the ToMFIR-based fault detection.A comparison with the standard observer-based technique is also provided.

A Model-free Approach to Fault Detection of Continuous-time Systems Based on Time Domain Data

In this paper, a model-free approach is presented to design an observer-based fault detection system of linear continuoustime systems based on input and output data in the time domain. The core of the approach is to directly identify parameters of the observer-based residual generator based on a numerically reliable data equation obtained by filtering and sampling the input and output signals.

非传统油气资源:现实且潜力巨大的油气勘探开发新领域

依据是否编入目前的石油地质学,将圈闭(油气藏)类型分为传统圈闭(油气藏)和非传统圈闭(油气藏)。与传统圈闭(油气藏)相比,非传统圈闭(油气藏)具有形态特殊、内部结构复杂、储层非均质性强且生储盖组合多变、成藏机理奇特、分布位置更加隐蔽等特征,甚至有的非传统油气藏位于传统石油地质理论认为的禁区。目前发现和开发的非传统油气藏有露头油气藏、断溶体油气藏、断缝体油气藏和断壳体油气藏等,非传统油气藏正逐渐成为油气勘探开发的热点和亮点。我国地质条件和成藏环境极为复杂,仍然有相当数量的圈闭(油气藏)没有收入经典的石油地质学,非传统油气资源潜力巨大,它们多分布在(超)深层、(超)深水、高原、极地、特殊气候地区,甚至盆地外,是现实的油气勘探开发新目标和新领域。

基于信息融合子域适应的不同工况下谐波减速器故障诊断方法

针对工业机器人谐波减速器不同工况数据分布差异大,部分工况数据标签缺失以及单一传感器获取信息不全面,导致诊断准确率不高的问题,提出一种信息融合子域适应的不同工况下谐波减速器故障诊断方法。该方法将源域和目标域一维振动数据利用小波变换构建时频图;使用基于小波变换的图像融合方法整合多个传感器的时频信息并构建融合图像;提出多表示特征提取结构的改进残差网络以充分挖掘融合样本多表示特征,同时,在无监督场景下将源域和目标域融合样本的多表示特征进行子域适应处理,减小两域的各个子域间的分布差异,从而将知识从标签丰富的源域迁移到标签缺失的目标域,最终实现不同工况下谐波减速器的故障诊断。通过搭建工业机器人谐波减速器故障实验台并进行实测,所提方法在所有迁移任务中平均准确率可达98.8%,能够有效实现无监督场景中不同工况下谐波减速器的故障诊断。

不同工况及类别下热力系统故障诊断的多源域自适应方法

针对不同负荷工况下,热工参数数据分布差异大且故障类别不一致的问题,提出了一种基于多源样本加权域对抗网络的热力系统故障诊断方法。首先,构建领域共享的一维卷积神经网络以提取多个源域和目标域的深度判别特征;其次,引入加权机制和域一致性损失度量样本,以降低仅存在于源域的故障类别的负迁移影响;然后,通过多域判别器的对抗学习实现每对源域和目标域的特征差异对齐;最后,构建多分类器对齐模块以提高预测的一致性,从而实现多源域不同工况下热力系统故障的准确诊断。借助某600MW超临界机组全范围仿真系统进行故障仿真实验,结果验证了所提方法的鲁棒性和优越性。

基于时频域参数分析的直流电弧故障检测装置

针对现有的直流电弧故障检测方法容易受到环境影响,无法实现精准检测,且算法结构复杂,难以部署于实际应用,基于短时傅里叶变换设计一种直流电弧故障检测算法。通过分析电弧电流信号在频域上的幅值变化趋势,将频谱划分为不同的阶段,计算不同阶段频率积分的比值,根据大量实验结果得到判定的阈值从而实现电弧故障检测。最后,选择合适的单片机实现检测算法并进行测试,验证所提算法的有效性。结果表明,所提算法不仅能在多种类型负载并联的情况下及时检测出电弧故障,检测的准确率可达94.4%,而且在面对正常状态电流时的误判率为0,证明其具有较好的稳健性。

一类伴有部分解耦干扰的非线性系统故障诊断

针对Lipschitz非线性系统执行器故障检测和传感器故障估计问题,本文提出了一种基于H_/L_(∞)未知输入观测器的有限频域故障诊断策略.首先,将系统处理成包含传感器故障的增广系统.然后,将该系统的未知输入干扰分为可解耦与不可解耦两部分.针对可解耦部分,利用观测器匹配条件将其从估计误差中消除.针对不可解耦部分,设计L_(∞)指标抑制其对残差的影响并结合有限频域H_指标提高执行器故障检测灵敏度.接着,给出观测器存在的充分条件并将其转化为受LMIs约束的线性优化问题,实现了执行器故障的鲁棒检测及传感器故障的鲁棒估计.最后,结合仿真算例验证了所提方法的正确性与有效性.

基于多领域耦合建模的轴向柱塞泵故障诊断方法

针对轴向柱塞泵传统单一领域建模方法存在的建模困难、仿真精度低以及故障诊断所需故障样本不足的问题,开展基于多领域耦合建模的轴向柱塞泵故障诊断方法。利用Simscape构建轴向柱塞泵多领域耦合模型,并对柱塞泄漏、主轴轴承磨损以及组合故障3种常见的故障进行模拟,再通过故障注入技术和MATLAB快速重启功能获取多种工况、不同故障程度下的压力和流量数据;随后从时域和频域对故障数据进行特征提取,同时利用单因素方差分析对故障特征进行选择;最后利用得到的特征对K邻近、朴素贝叶斯、决策树、神经网络、支持向量机等5种故障诊断算法进行训练,得到故障诊断准确率最高的算法,其平均诊断准确率为98.5%。该方法提高了轴向柱塞泵多领域耦合建模的精确性,实现了对轴向柱塞泵的有效故障诊断。

基于tSNE多特征融合的JTC轨旁设备故障检测

无绝缘轨道电路(Jointless Track Circuit,JTC)的轨旁设备在室外长期运营过程中,其可靠性会逐渐降低,进而给列车行车安全带来严重威胁。以轨道电路读取器(Track Circuit Reader,TCR)感应电压为基础,针对JTC故障诊断研究中轨旁设备故障类型复杂和故障特征提取不充分等问题,提出一种基于t分布随机邻域嵌入(t-distribution Stochastic Neighbor Embedding,tSNE)多特征融合的JTC轨旁设备故障检测模型。首先,根据不同轨旁设备故障对TCR感应电压信号的影响,分析各轨旁设备的故障特性。其次,提取TCR感应电压信号的方差、有效值、峰值因子等幅值域特征,以及排列熵、散布熵特征构成原始故障特征集。为了去除其中的冗余信息,得到具有较高判别性的融合流形特征,利用tSNE算法进行特征融合。最后输入深度残差网络(Deep Residual Network,DRN)得到故障检测混淆矩阵,实现轨旁设备故障定位。实验结果表明:tSNE算法融合后的特征在异类和同类故障样本之间分别有较大的类间间距和较小的类内间距,相比主成分分析(Principal Component Analysis, PCA)、随机相似性嵌入(Stochastic Proximity Embedding, SPE)、随机邻域嵌入(Stochastic Neighbor Embedding,SNE)算法具有更优的融合特征提取效果。此外,结合DRN可以有效识别多种轨旁设备故障,达到98.28%的故障检测准确率。通过现场信号进行实例验证,结果表明该故障检测模型能满足铁路现场对室外设备进行故障定位的实际需求。

基于小波域威布尔分布模型的电机滚动轴承故障诊断分析

阐述小波域威布尔分布模型的原理,探讨基于小波域威布尔分布模型的电机滚动轴承故障诊断方法,包括数据采集和预处理、小波变换和威布尔分布模型的应用、故障特征提取和诊断准确性评估。

面向联合收割机故障领域的命名实体识别研究

联合收割机作为一种机械化设备不可避免地会出现机械故障,为快速地找出并解决机械故障,提出一种面向联合收割机故障领域的命名实体识别模型RP-TEBC(RoBERTa-wwm-ext+PGD+Transformer-Encoder+BiGRU+CRF)。RP-TEBC使用动态编码的RoBERTa-wwm-ext预训练模型作为词嵌入层,利用自适应Transformer编码器层融合双向门控单元(BiGRU)作为上下文编码器,利用条件随机场(CRF)作为解码层,使用维特比算法找出最优的路径输出。同时,RP-TEBC模型在词嵌入层中通过添加一些扰动,生成对抗样本,经过对模型不断的训练优化,可以提高模型整体的鲁棒性和泛化性能。结果表明,在构建的联合收割机故障领域命名实体识别数据集上,相比于基线模型,该模型的准确率、召回率、F1值分别提高1.79%、1.01%、1.46%。

深度多模态迁移学习在轴承故障诊断中的研究

在工程实际中,设备数据样本往往以正常数据居多。故障样本稀缺且模态单一使得可用于训练的故障信息特征提取不足,同时训练和测试数据分布往往存在差异,从而导致模型迁移诊断能力较弱。针对该问题,提出将深度学习模型CNN与多模态融合迁移学习技术相结合(Deep Multimodal Fusion Transfer Learning,DMFTL)应用于轴承的故障诊断中。首先以CNN为基本学习框架,将原始一维振动信号的时域和频域进行多模态信息融合对模型预训练;然后以最大均值差异(MMD)为度量准则,通过域自适应来最小化源域和目标域的差异;最后引入构造的正则项到模型中,以完成跨域诊断。通过对CWRU轴承数据集的迁移诊断试验及对比分析,验证了该方法的有效性和优越性。