Condition Monitoring Using Automatic Spectral Analysis
Abstract
Within the frame of machinery maintenance, spectral analysis is a helpful tool. Therefore, an automatic spectral analysis tool, capable to identify each component of a measured signal would be of interest. This paper studies a new spectral analysis strategy for detecting, characterizing and classifying all spectral components of an unknown process. Indeed, any vibration signal can be considered as a mixture of components, a component being either a sinusoidal wave, or a narrow band one. We assume that a sum of an unknown number of these components is embedded in an unknown colored noise. The complete methodology we propose provides a way to feature each component in the spectral domain. The first idea is not to choose one specific spectral analysis method but, rather, to concatenate the results of complementary algorithms. For each one, the noise spectrum is estimated by a nonlinear filter and spectral component detection is managed with a local Bayesian hypothesis testing. This test is defined in frequency and takes account of the noise spectrum estimator. Thanks to a matching with the corresponding spectral window, each component detected is classified into one of the following four classes: Pure Frequency, Narrow Band, Alarm and Noise. The second main idea is then to propose a fusion of the classification results, leading to a complete description of each spectral component present in the signal. This spectral classification is particularly interesting within the context of condition monitoring. Examples are given on real vibratory signals and show the performance of the proposed automatic method, which is particularly well adapted to signals having a high number of components.
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