CAJ | 학술논문

针对滚动轴承不同运行状态振动信号具有不同复杂性的特点，提出一种新的基于多尺度熵(multiscale entropy, MSE)和概率神经网络(probabilistic neural networks, PNN)的滚动轴承故障诊断方法。该方法首先利用MSE方法对滚动轴承振动信号进行特征提取，并将其作为PNN神经网络的输入，再利用PNN自动识别轴承故障类型及故障程度。实验数据包括不同故障类型和不同故障程度样本，结果表明，相比于小波包分解和PNN结合的诊断方法，提出的方法具有更高的诊断精度，能有效实现滚动轴承故障类型及程度的诊断。
침대곤동축승불동운행상태진동신호구유불동복잡성적특점，제출일충신적기우다척도적(multiscale entropy, MSE)화개솔신경망락(probabilistic neural networks, PNN)적곤동축승고장진단방법。해방법수선이용MSE방법대곤동축승진동신호진행특정제취，병장기작위PNN신경망락적수입，재이용PNN자동식별축승고장류형급고장정도。실험수거포괄불동고장류형화불동고장정도양본，결과표명，상비우소파포분해화PNN결합적진단방법，제출적방법구유경고적진단정도，능유효실현곤동축승고장류형급정도적진단。
Considering different levels of complexity of vibration signals of rolling bearings in different operating conditions, a novel fault diagnosis method has been proposed based on the multiscale entropy (MSE) and probabilistic neural networks (PNN). Fault feature vector is firstly extracted from the vibration signals using MSE and then provided to PNN neural network as the input. The PNN network will identify the bearing fault type and severity level simultaneously. The experimental data are collected from an induction motor bearing involving various fault types and severity levels. The results demonstrate that the proposed method has a higher accuracy in rolling bearing fault diagnosis than the method of the combination of wavelet packet decomposition with PNN.