基于自适应随机共振的异步电动机转子断条故障检测

异步电动机转子断条故障检测属于强噪声背景下故障的微弱信号检测问题。由于不同转差率下电机具有不同的转子断条故障特征频率，给随机共振最佳系统参数调节增加了难度。针对上述问题，在已有Hilbert模量法的基础上，提出了基于自适应随机共振的异步电动机转子断条故障检测方法。通过应用小波消噪、随机共振技术，改进了转子断条故障特征信号的检测灵敏度。随机共振参数的自适应调节进一步增强了该方法的实用性，与传统方法相比，该方法在各种情况下尤其是在噪声强度、电机类型及运行状况未知时，仍能有效检测转子断条故障，具有较好的自适应性。数值仿真和实验分析证明了该方法的有效性。

New Approach of Broken Rotor Bar Detection in Induction Motor Based on Adaptive Stochastic Resonance

Broken rotor bar detection in induction motor is a problem of detecting weak signal in heavy noise. In different slips, stator current have different rotor fault characteristic frequencies. As a consequence, it is more difficult to get the optimal system parameters of stochastic resonance. To solve these problems, a new method is proposed on the basis of the traditional Hilbert modulus. This technique improves the detecting sensitivity of broken rotor bar fault by employing wavelet transform and adaptive stochastic resonance. The self-regulating of stochastic resonance parameters enhance the practicability of this method. Compared with traditional way, this method can detect rotor fault more effectively in many conditions with better adaptability, especially in the case of unknowing the noise intensity, type of electric machine and running status. The simulations and experimental results prove the availability of this technique.