Blind vibration filtering using envelope spectrum indicators for bearing and gear fault detection without knowledge of machine kinematics

Abstract : The central idea behind this paper is to propose a means to filter out vibration signals of interest from a fault detection perspective without actually having knowledge about the kinematics of the machine. In other words, this paper investigates blind filters that do not require a-priori knowledge about the fault frequencies, e.g. of a bearing or gear. This kind of approach opens the door for the condition monitoring of complex machines where insufficient information is available about the inner components or where replacements have been carried out that changed characteristic frequencies and that were not logged. This feat is achieved by employing the squared envelope as a metric for the blind filter. The main assumption of the proposed method is that when a fault occurs, it introduces a second-order cyclostationary (CS2) component in the vibration signal which manifests itself in the squared envelope (SE) as a harmonic sine modulation at its corresponding fault frequency. This modulation correspondingly also increases the sparsity of the envelope spectrum. To avoid interfering influences of CS1 components, the signal is typically pre-whitened, e.g. through linear prediction filtering, cepstrum editing, etc. The paper investigates the minimization of the relative prediction error of the linear prediction of the squared envelope for use in the iterative updating procedure of the blind filter.
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Cédric Peeters, Jérôme Antoni, Jan Helsen. Blind vibration filtering using envelope spectrum indicators for bearing and gear fault detection without knowledge of machine kinematics. Surveillance, Vishno and AVE conferences, INSA-Lyon, Université de Lyon, Jul 2019, Lyon, France. ⟨hal-02188556⟩

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