基于MMFD-FCM的退化状态识别方法及其应用研究

Degraded Status Recognition Method Based on MMFD-FCM  and Its Application Research

摘要:

为了有效地对机械设备运行状态进行监测,进而对其性能退化状态进行识别,提出一种基于形态多重分形维数(MMFD)与模糊C均值聚类(FCM)的性能退化状态识别方法；该方法首先计算机械设备振动信号的形态多重分形维数,以此作为性能退化特征指标；该特征指标能够有效反映峰值在振动信号中概率分布的不均匀程度,从而定量描述振动信号的性能退化状态,并且与多重形态分形维数相比,利用数学形态学计算的MMFD精度更高,计算速度更快；在此基础上,鉴于不同退化状态之间的模糊性,针对性地采用模糊C均值聚类方法对特征指标进行模糊聚类,从而有效识别性能退化状态；将该方法应用于滚动轴承全寿命周期振动信号中,分析结果验证了该方法的有效性。

Abstract:

In order to monitor mechanical equipment operational condition, and then indentify the performance degradation, a performance degradation identification method based on MMFD and FCM is proposed in this paper. For this method, MMFD of mechanical equipment vibrating signal is calculated and chosen as performance degradation characteristic index first. Multi-fractal dimension of vibration signal can reflect the peaks’ non uniform probability distribution in the entire signal, and it can quantitative characterize the vibration intensity in the process of performance degradation. Compared with multi-fractal dimension, MMFD calculated based on mathematical morphology has a better performance in precision and computation speed. On this basis, in consideration of the fuzziness among different performance degradation, FCM is introduced into fuzzy clustering for characteristic index, and performance degradation could be recognized effectively. The rolling bearing whole life data verifies the validity of these methods above.