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汽轮机转子与静子间的碰磨严重影响着机组的安全运行。为了解决汽轮机转子发生在早期和中期的碰磨故障难以通过基于振动信号检测诊断方法进行有效识别的问题,本文提出一种基于EEMD-LSTM的汽轮机转子碰磨故障诊断方法。首先,该方法通过声发射技术监测汽轮机转子的碰磨故障信号;然后,利用EEMD信号分解方法处理获取的声发射信号,并提取能量特征参数和相关的时域特征参数,从而获得碰磨故障特征数据集;最后,利用划分的数据集对LSTM神经网络进行训练与测试,从而获得碰磨故障诊断模型。工程应用结果表明,本文提出的方法能够有效识别机组在不同转速时期的早期碰磨故障,且故障诊断的准确率较高。 相似文献
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大型机电设备工作环境复杂,故障频发,需要对故障信号进行实时采集和有效检测。传统方法采用时频分析的统计信号处理进行故障信号采集,对低信噪比的故障信号检测性能不好。提出一种基于非线性时间序列分析和相空间重构的大型机电设备故障信号实时采集方法,实现故障的检测和诊断。构建大型机电设备的故障数据检测模型,基于相空间重构分析的故障诊断方法,通过经验模态分解方法提取故障信号的特征信息,以此为数据基础建立智能专家系统,实现大型机电设备的故障诊断。在大型机电设备3种工况模式下测试大型机电设备的性能,得到信号采集时域波形,并进行相空间重构分析,实现故障分类诊断仿真,仿真实验表明,采用该方法进行故障信号的实时采集和故障诊断,故障分类性好,故障检测和诊断的效率较高。 相似文献
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针对现有基于时域特征的高压油泵故障诊断准确率低的问题,笔者提出一种参数优化变分模态分解(VMD)算法和散布熵的特征提取方法,并采用支持向量机(SVM)进行故障诊断.首先,基于对高压油泵工作原理及典型故障的分析,利用AMESim平台搭建高压油泵仿真模型进行故障模拟和信号采集.然后,针对VMD效果受限于分解个数和惩罚因子选取的问题,采用改进灰狼优化(IGWO)算法对VMD进行参数寻优.通过计算各模态的散布熵值形成故障特征向量,最后,采用SVM对故障特征向量进行训练和诊断,实现高压油泵的故障诊断.该方法的故障诊断准确率可达到95%以上,能有效地实现高压油泵故障诊断. 相似文献
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利用凸轮轴动态扭矩诊断柴油机燃油喷射系统故障 总被引:1,自引:0,他引:1
本文提出了一种新的诊断技术—用凸轮轴扭矩信号作为判别柴油机燃油喷射系统工作状态的参数。作者对凸轮轴扭矩进行了分析,提出了诊断参数,开发了计算机故障诊断系统,在台架上进行了故障模拟试验,并对6135柴油机进行实机监测。理论和试验表明:用扭矩诊断技术可以诊断喷油系统的常见故障,用凸轮轴扭矩信号可以在试验台上或在柴油机上对多至18缸的组合式油泵各缸的喷油正时、供油量均匀性进行监测和调整。本技术除了适用于柴油机监测,还可望用于高压油泵出厂试验之传统方法的改进。 相似文献
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风力机齿轮箱振动信号是一种时频特性复杂的非平稳信号,常规的时域和频域分析方法难以有效的分析齿轮箱故障及提取故障特征。提出一种基于小波分析和神经网络的风力机齿轮箱故障诊断方法,该方法采用小波时频分析技术对风力发电机故障振动信号进行消噪滤波,通过小波包分解系数求取频带能量,根据各个频带能量的变化提取故障特征,为实现智能诊断提供故障特征值。应用BP神经网络进行故障识别,并采用LabVIEW和matlab软件予以实现。结果表明,该方法能有效提高风力发电机组齿轮箱故障诊断的准确性。 相似文献
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Planetary gearboxes play an important role in wind turbine (WT) drivetrains. WTs usually work under time-varying running conditions due to the volatile wind conditions. The planetary gearbox vibration signals in such an environment are hence highly nonstationary. Conventional spectral analysis and demodulation analysis methods are unable to identify the characteristic frequency of gear fault from such nonstationary signals. As such, this paper presents a time–frequency analysis methods to reveal the constituent frequency components of nonstationary signals and their time-varying features for WT planetary gearbox monitoring. More specifically, we exploit the adaptive optimal kernel (AOK) method for this challenging application because of its fine time–frequency resolution and cross-term free nature, as demonstrated by our simulation analysis. In this study, the AOK method has been applied to identify the time-varying characteristic frequencies of gear fault or to extract different levels of impulses induced by gear faults from lab WT experimental signals and in-situ WT signals under time-varying running conditions. We have demonstrated that the AOK is effective diagnosis of: (a) both the local damage (a single chipped tooth) and distributed faults (wear of all teeth), (b) both sun gear and planet gear faults, and (c) faults with very weak signature (e.g., the sun gear fault at the low speed stage of a WT planetary gearbox). 相似文献
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风电机组一般采用滚动轴承支撑结构,滚动轴承不同故障模式对应的振动冲击间隔频率存在差异。为了准确地从振动信号中提取滚动轴承故障征兆,在分析风电机组滚动轴承故障机理、信号特征的基础上,提出了基于小波变换的风电机组滚动轴承故障KPI计算方法,首先对风电机组的振动信号进行小波变换及阈值去噪,并计算振动信号的小波能量谱分布图,然后以小波能量谱分布图的统计参数作为滚动轴承故障诊断的KPI,采用椭圆型判决函数法实现滚动轴承的故障诊断,现场实测信号的诊断结果验证了该方法的有效性。 相似文献
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风电机组一般采用滚动轴承支撑结构,滚动轴承不同故障模式对应的振动冲击间隔频率存在差异。为了准确地从振动信号中提取滚动轴承故障征兆,在分析风电机组滚动轴承故障机理、信号特征的基础上,提出了基于小波变换的风电机组滚动轴承故障KPI计算方法,首先对风电机组的振动信号进行小波变换及阈值去噪,并计算振动信号的小波能量谱分布图,然后以小波能量谱分布图的统计参数作为滚动轴承故障诊断的KPI,采用椭圆型判决函数法实现滚动轴承的故障诊断,现场实测信号的诊断结果验证了该方法的有效性。 相似文献
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发动机稳态与非稳态振动信号分析比较 总被引:4,自引:0,他引:4
主要探索了在发动机机械故障诊断中采用非稳态信号分析方法进行故障诊断。介绍了定转速非稳态数据采集器测试原理,讨论了短时傅里叶(Fourier)变换的原理与窗函数选择方法。应用它分析了发动机稳态与非稳态加速振动信号并进行了比较。试验与分析结果表明:定转速非稳态数据采集器能准确地测取发动机加速过程中所设定转速的振动信号;采用短时Fourier变换和窄带能量累加方法能有效提取加速振动信号中分析对象的故障特征,具有良好的重复性和稳定性;加速振动的信噪比远比稳态振动大得多。 相似文献
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分析了目前汽轮发电机组振动故障诊断方法存在的问题,提出了根据机组故障范围、振动频率特征等对非线性油膜涡动故障进行分层次诊断的正向推理振动诊断方法。并以诊断实例说明如何通过正向推理诊断振动故障,提高故障诊断准确率。 相似文献
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Xingwang Zhao Liangfei Xu Jianqiu Li Chuan Fang Minggao Ouyang 《International Journal of Hydrogen Energy》2017,42(29):18524-18531
Fuel cell vehicles are becoming more popular and attracting more attention from industries, but stability and reliability of the fuel cell system (FCS) are still problems for its commercial progress. Therefore, a fault diagnosis system is essential for a reliable and long working lifetime FCS. In this work, a fault diagnosis method based on multi-sensor signals and principle component analysis (PCA) is proposed to improve FCS performance. By using this method, the correlation among different sensor signals are analyzed based on multi-sensor signals, and a simplified statistic index for fault diagnosis is deduced based on the PCA. The FCS operation conditions are monitored online, and faults in sensor and system levels are diagnosed. Experimental results show that, two typical fault scenarios, i.e., a single sensor fault and a serious system failure, can be successfully diagnosed and distinguished. For the single sensor fault, the sensor signal is reconstructed immediately to ensure that fuel cell vehicles operate normally. For the system failure, the fault can be detected in 17 s and the fault source signals can be located in 31 s, so the fuel cell stack can be protected timely. The main contribution of this work is to deduce a simplified statistic index for fault diagnosis based on multi-sensor signals and PCA method, and to provide an experimental study on identifying faults in sensor and system levels of a PEM fuel cell system. 相似文献
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Based on the Morlet wavelet transformation and Wigner-Ville distribution (WVD), we present a wind turbine fault diagnosis method in this paper. Wind turbine can be damaged by moisture absorption, fatigue, wind gusts or lightening strikes. Due to this reason, there is an increasing need to monitor the health of these structures. Vibration analysis is the best-known technology applied in wind turbine condition monitoring, in which the time-frequency analysis techniques such as Wigner-Ville distribution (WVD) are widely used. Theoretically WVD has an infinite resolution in time-frequency domain. For early wind turbine fault signals, however, there are two main difficulties in WVD analysis. One is strong noise signals in the background and the other is cross terms in WVD itself. In this paper, continuous wavelet transformation (CWT) is employed to filter useless noise in raw vibration signals, and auto terms window (ATW) function is used to suppress the cross terms in WVD. In the CWT de-noising process, the Morlet wavelet, whose shape is similar to mechanical shock signals, is chosen to perform CWT on the raw vibration signals. The appropriate scale parameter for CWT is optimized by the cross validation method (CVM). An ATW based on the Smoothed Pseudo Wigner-Ville distribution (SPWVD) spectrum is taken to be a window function to suppress the cross terms in WVD. The new method can not only remove cross terms faraway from the auto terms, but also keep high energy close to every instantaneous frequency, the virtues such as high time-frequency resolution, and good energy aggregation etc. The wind turbine gear fault diagnosis experiment results indicate that the proposed method has a good de-nosing performance and is effective in suppressing the cross terms and extracting fault feature. 相似文献