基于变分模式分解和微积分增强能量算子的滚动轴承故障诊断

针对滚动轴承故障振动信号的特点，考虑变分模式分解在复杂信号分解及微积分增强能量算子在瞬态成分检测方面的优势，提出基于变分模式分解和微积分增强能量算子的滚动轴承故障诊断方法。首先利用变分模式分解将复杂信号分解为多个本质模式函数，以削弱背景噪声的影响和满足能量算子对信号单分量的要求；然后根据提出的敏感分量选取原则，从本质模式函数中选出包含主要故障信息的本质模式函数为敏感分量；最后利用微积分增强能量算子强化敏感分量中的瞬态冲击，并根据敏感分量瞬时能量的时域波形及Fourier频谱诊断滚动轴承故障。分析结果表明该方法能够有效诊断滚动轴承故障。

Fault diagnosis of rolling bearings based on variational mode decomposition and calculus enhanced energy operator

Aiming at the characteristics of rolling bearing fault vibration signals and considering the merits of variational mode de-composition in mono-component separation and calculus enhanced energy operator in transient impulse detection, this article introduces a new method termed fault diagnosis of rolling bearings based on variational mode decomposition and calculus enhanced energy opera-tor. Firstly, the vibration signal is decomposed into several intrinsic mode functions by variational mode decomposition to reduce the noise interferences and to satisfy the mono-component requirement by energy operator. Then, the sensitive intrinsic mode function con-taining the main fault information about the bearing is selected by the proposed criterion. Finally, the impulses are strengthened using calculus enhanced energy operator, and the bearing fault is diagnosed by the time domain waveform and Fourier spectrum of the sensi-tive mono-component instantaneous energy. The analysis results show that the proposed method can effectively diagnose the rolling bearing faults.