基于CEEMDAN-DRT的滚动轴承故障诊断方法研究

Research of Fault Diagnosis Method of Rolling Bearing based on CEEMDAN-DRT

针对滚动轴承故障信息不易提取的特性,提出了完全集合经验模态分解(CEEMDAN)自适应消噪和共振解调技术(DRT)相结合的故障诊断方法。首先,利用CEEMDAN自适应地将信号分解成多个分量,通过互相关系数方法进行重构以达到消噪的目的;然后,对重构的信号进行谱峭度分析,得到冲击成分所在的频带,并据此设计带通滤波器对重构信号进行滤波处理;最后,对滤波后的信号进行Hilbert包络谱分析,提取冲击成分的频率,并与滚动轴承故障特征频率对比,进行故障模式识别。通过动力学仿真和滚动轴承实验对该方法进行了有效性论证。结果表明,该方法可以有效识别滚动轴承的故障信息。

Aiming at the feature extraction for the rolling bearing,a fault diagnosis method based on CEEMDAN adaptive denoising combining with DRT(demodulated resonance technology) is presented.Firstly,the original vibration signal is decomposed into several components with CEEMDAN adaptively,and the reconstruction is performed on regarding the correlation coefficient method to realize the purpose of eliminating noise.Then,the reconstructed signal is analyzed by spectral kurtosisto to achieve the frequency band of the impact component.Based on this,a bandpass filter is designed for the reconstructed signal.Finally,the filtered signals are analyzed by energy Hilbert envelope spectrum.With reference on the characteristic frequency of the rolling bearing failure modes,the pattern of the bearing is recognized.The effectiveness of the method is demonstrated by dynamics simulation and rolling bearing experiments.The result shows that the method can be used to identify the fault information of the rolling bearings effectively.