搜索资源列表
bearingcodes2
- bearing faults matlab code
pint_2014_Applied-Acoustics
- The main problem in dustrial application of bearing vibration diagnostics is the masking of informative bearing signal by machine noise. The vibration signal of the rolling bearing is often covered or concealed by other structural vibrations sour
diagnosis--based-on-LEM
- A novel fault feature extraction method based on the local mean decomposition technology and multi-scale entropy is proposed in this paper. When fault occurs in roller bearings, the vibration signals picked up would exactly display non-stationary
Vibration-Monitoring
- The HHT represents a time-dependent series in a two-dimensional (2-D) time-frequency domain by extracting instan eous frequency components within the signal through an Empirical Mode Decomposition (EMD) process. The analytical background of t
FSWT-and-Spectrum-Kurtosis
- 本文提出一种基于频率切片小波变换和谱峭度的综合算法。首先对轴承端的振动信号时频分析，采用FFT、包络谱、频率切片小波变换对其频域性能进行分析，再求其峭度谱与对应包络谱，结合其时域、频域性能，综合分析轴承故障。-This paper presents a synthesis algorithm based on frequency slice wavelet transform and spectral kurtosis. Firstly, the time-frequency analysis
VMD
- 本文介绍了一种自适应信号分解新方法-变分模态分解，并且针对滚动轴承早期故障识别困难这一问题，提出了基于VMD的诊断方法。-In this paper, a new adaptive signal decomposition method, variational mode decomposition, is introduced. Aiming at the problem of early fault identification of rolling bearing, a diagnosis
Parameter-optimization
- 针对滚动轴承早期故障特征提取困难的问题，提出一种基于参数优化变分模态分解的轴承早期故障诊断方法。首先利用粒子群优化算法对变分模态分解算法的最佳影响参数组合进行搜索，搜索结束后根据所得结果设定变分模态分解算法的惩罚参数和分量个数，并利用参数优化变分模态分解算法对故障信号进行处理。-Aiming at the difficult problem of early fault feature extraction of rolling bearing, an early fault diagnosis
VMD-Parameter-Estimation
- 变分模态分解在信号分解精度和噪声鲁棒性方面具有明显优势，但需预先确定模态数K，而目前K 只能靠先验知识进行预估，如果预估的K 与实际信号存在差异，会导致分解误差较大。针对以上问题，利用EMD 不需预先设定模态数的自适应分解特点，通过对EMD 分解结果的分析，进行VMD 分解模态数的估计，并通过仿真信号分析及滚动轴承故障信息提取-Variational modal decomposition has obvious advantages in signal decomposition accura