云模型和集成极限学习机相结合的滚动轴承故障诊断方法

针对轴承振动信号的不确定性和非平稳性以及BP神经网络学习算法收敛速度慢、稳定性差等问题,提出了基于云模型和集成极限学习机的滚动轴承故障模式识别方法.将经预处理之后的信号进行云化,产生滚动轴承在不同状态下的信号云;提取出决定信号云分布的期望、熵和超熵三个参数作为表征轴承状态的特征量并依此构造出原始的轴承状态数据集;再将故障特征数据集经归一化处理后送入集成极限学习机进行识别.研究结果表明:云-集成极限学习机方法可以有效地实现轴承故障模式识别,与传统神经网络识别方法相比,该方法拥有更高的识别准确率和稳定性,并且集成极限学习机在抗噪性方面有较好的表现.

Fault diagnosis method for rolling bearing based on cloud model and ensemble extreme learning machine

Aiming at the uncertainty and non-stationarity of bearing vibration signal as well as the slow convergence rate and poor stability of BP neural network learning algorithm, a rolling bearing fault pattern recognition method based on cloud theory and ensemble extreme learning machine was proposed. The pre-processed signal was cloudized to generate the signal cloud under different states of rolling bearing. Three parameters that can determine the signal cloud distribution were as follows: expectation, entropy and hyper entropy. These three parameters were taken as the characteristic quantities that characterize the bearing state and the original bearing state data set was constructed. Then, the fault feature data set that was normalized sent to the ensemble extreme learning machine for identification. The results showed that the cloud-ensemble extreme learning machine method could effectively realize bearing fault pattern recognition. Compared with the traditional neural network recognition method, the method had higher recognition rate and stability, and the ensemble extreme learning machine had a better performance in anti-noise.