基于多尺度流形的设备故障诊断方法研究
首发时间:2013-09-18
摘要:小波变换具有可变时频分辨率的优点,因此被广泛应用于机械设备故障诊断领域。本文采用小波提取包络的方法进行机械故障诊断,并提出了一种增强的包络解调方法,称为多尺度流形。其主要思想是对多个尺度上的高维小波包络进行流形学习。该方法分三步实现:1)对测量信号进行连续小波变换,获取时间-尺度域上的非平稳信息;2)选取包含故障脉冲信息的尺度带;3)对所选尺度上的小波包络进行流形学习,以提取故障脉冲的内在流形特征。多尺度流形采用非线性的方法综合信号多个尺度上的包络信息,因此可以保留机械故障的真实脉冲。该方法尤其适合探测旋转机械的故障特征频率。仿真和齿轮箱故障诊断的应用验证了所提方法的有效性。
关键词: 机械设备故障诊断 多尺度流形 连续小波变换 小波包络 流形学习
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Machine Fault Diagnosis Based on Multi-Scale Manifold
Abstract:The wavelet transform has been widely used in the field of machinery fault diagnosis for its merit in flexible time-frequency resolution. This paper focuses on wavelet enveloping, and proposes an enhanced envelope demodulation method, called multi-scale manifold (MSM), for machinery fault diagnosis. The MSM addresses manifold learning on the high-dimensional wavelet envelopes at multiple scales. Specifically, the proposed method is conducted by three following steps. First, the continuous wavelet transform (CWT) with complex Morlet wavelet is introduced to obtain the non-stationary information of the measured signal in time-scale domain. Second, a scale band of interest is selected to include the fault impulse envelope information of measured signal. Third, the manifold learning algorithm is conducted on the wavelet envelopes at selected scales to extract the intrinsic manifold of fault-related impulses. The MSM combines the envelope information of measured signal at multiple scales in a nonlinear approach, and may thus preserve the factual impulses of machinery fault. The new method is especially suited for detecting the fault characteristic frequency of rotating machinery, which is verified by means of a simulation study and a case of practical gearbox fault diagnosis in this paper.
Keywords: Machinery Fault Diagnosis Multi-Scale Manifold Continuous Wavelet Transform Wavelet Envelope Manifold Learning
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