It is a challenging issue to detect bearing fault under nonstationary conditions and gear noise interferences. Meanwhile, the application of the traditional methods is limited by their deficiencies in the aspect of co...It is a challenging issue to detect bearing fault under nonstationary conditions and gear noise interferences. Meanwhile, the application of the traditional methods is limited by their deficiencies in the aspect of computational accuracy and e ciency, or dependence on the tachometer. Hence, a new fault diagnosis strategy is proposed to remove gear interferences and spectrum smearing phenomenon without the tachometer and angular resampling technique. In this method, the instantaneous dominant meshing multiple(IDMM) is firstly extracted from the time-frequency representation(TFR) of the raw signal, which can be used to calculate the phase functions(PF) and the frequency points(FP). Next, the resonance frequency band excited by the faulty bearing is obtained by the band-pass filter. Furthermore, based on the PFs, the generalized demodulation transform(GDT) is applied to the envelope of the filtered signal. Finally, the target bearing is diagnosed by matching the peaks in the spectra of demodulated signals with the theoretical FPs. The analysis results of simulated and experimental signal demonstrate that the proposed method is an e ective and reliable tool for bearing fault diagnosis without the tachometer and the angular resampling.展开更多
为实现电力系统次/超同步振荡的快速、准确辨识,提出了一种基于同步压缩广义S变换(synchrosqueezing generalized S transform, SSGST)和改进稀疏时域法(improved sparse time domain method,ISTD)结合的次/超同步振荡辨识方法。该方法...为实现电力系统次/超同步振荡的快速、准确辨识,提出了一种基于同步压缩广义S变换(synchrosqueezing generalized S transform, SSGST)和改进稀疏时域法(improved sparse time domain method,ISTD)结合的次/超同步振荡辨识方法。该方法首先利用能量比函数对电力系统广域量测信息实时检测,当检测到信号能量发生突变时,利用SSGST对检测到的振荡信号分解得到相应的SSGST时频系数矩阵;然后通过改进的脊线提取方法在时频域实现对各振荡分量的最优轨迹搜索;进一步,结合最优轨迹时频索引重构各振荡分量的时域分量,并利用ISTD辨识方法计算出各振荡分量的频率和阻尼比系数;最后,通过自合成模拟信号、双馈风电场经串补并网系统仿真信号和某实际风电场实测数据验证了所提方法的准确性和有效性。展开更多
为了从复杂的非平振动信号中追踪直升机的传动系统轴转速信息,开发了一种改进的广义解调变换方法。首先,利用广义解调变换对目标信号进行移频,移频后目标分量将由非平稳转换为近似平稳;其次,基于频谱细化的短时傅里叶变换(short time Fo...为了从复杂的非平振动信号中追踪直升机的传动系统轴转速信息,开发了一种改进的广义解调变换方法。首先,利用广义解调变换对目标信号进行移频,移频后目标分量将由非平稳转换为近似平稳;其次,基于频谱细化的短时傅里叶变换(short time Fourier transform,简称STFT)方法计算得到解调信号时频图;然后,估计信号的瞬时频率,利用迭代计算提升频率估计精度;最后,基于估计的转速信息开展同步分析,提高传动系统齿轮故障振动的信噪比。仿真分析和实测分析结果表明:所提出方法估计的轴速度信息具有较高的精度,满足基于速度信息开展同步分析的需求;利用估计的速度开展同步分析可有效提高信噪比,增强阶次谱上的故障特征,便于准确提取齿轮故障特征。展开更多
基金Supported by National Natural Science Foundation of China(Grant Nos.51335006 and 51605244)
文摘It is a challenging issue to detect bearing fault under nonstationary conditions and gear noise interferences. Meanwhile, the application of the traditional methods is limited by their deficiencies in the aspect of computational accuracy and e ciency, or dependence on the tachometer. Hence, a new fault diagnosis strategy is proposed to remove gear interferences and spectrum smearing phenomenon without the tachometer and angular resampling technique. In this method, the instantaneous dominant meshing multiple(IDMM) is firstly extracted from the time-frequency representation(TFR) of the raw signal, which can be used to calculate the phase functions(PF) and the frequency points(FP). Next, the resonance frequency band excited by the faulty bearing is obtained by the band-pass filter. Furthermore, based on the PFs, the generalized demodulation transform(GDT) is applied to the envelope of the filtered signal. Finally, the target bearing is diagnosed by matching the peaks in the spectra of demodulated signals with the theoretical FPs. The analysis results of simulated and experimental signal demonstrate that the proposed method is an e ective and reliable tool for bearing fault diagnosis without the tachometer and the angular resampling.
文摘为实现电力系统次/超同步振荡的快速、准确辨识,提出了一种基于同步压缩广义S变换(synchrosqueezing generalized S transform, SSGST)和改进稀疏时域法(improved sparse time domain method,ISTD)结合的次/超同步振荡辨识方法。该方法首先利用能量比函数对电力系统广域量测信息实时检测,当检测到信号能量发生突变时,利用SSGST对检测到的振荡信号分解得到相应的SSGST时频系数矩阵;然后通过改进的脊线提取方法在时频域实现对各振荡分量的最优轨迹搜索;进一步,结合最优轨迹时频索引重构各振荡分量的时域分量,并利用ISTD辨识方法计算出各振荡分量的频率和阻尼比系数;最后,通过自合成模拟信号、双馈风电场经串补并网系统仿真信号和某实际风电场实测数据验证了所提方法的准确性和有效性。
文摘为了从复杂的非平振动信号中追踪直升机的传动系统轴转速信息,开发了一种改进的广义解调变换方法。首先,利用广义解调变换对目标信号进行移频,移频后目标分量将由非平稳转换为近似平稳;其次,基于频谱细化的短时傅里叶变换(short time Fourier transform,简称STFT)方法计算得到解调信号时频图;然后,估计信号的瞬时频率,利用迭代计算提升频率估计精度;最后,基于估计的转速信息开展同步分析,提高传动系统齿轮故障振动的信噪比。仿真分析和实测分析结果表明:所提出方法估计的轴速度信息具有较高的精度,满足基于速度信息开展同步分析的需求;利用估计的速度开展同步分析可有效提高信噪比,增强阶次谱上的故障特征,便于准确提取齿轮故障特征。
