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In this paper, a fault detection method based on ensemble multiwavelet transform is proposed for assembly looseness fault detection in aero-engine rotors. Benefiting from characteristics of multi-resolution analysis and the multiple basis functions, multiwavelet has great advantage when describing non-stationary vibration data. However, the fixed basis functions of conventional discrete multiwavelet transform, which is not related to vibration data, may decrease the preciseness of fault detection. Moreover, multiwavelet transform does not realize multi-resolution analysis in the high frequency band, which may result in omitting some useful fault feature information. To overcome these limitations, a customized multiwavelet basis function is constructed via symmetric lifting scheme. Then, the measured vibration data from the mechanical equipment is processed by the ensemble multiwavelet transform. The relative energy in a decomposed frequency band of the ensemble multiwavelet transform coefficients means a percentage of the whole signal energy is taken as probability. Normalized information entropy is computed on the basis of the relative energy to reflect the current state of a mechanical system. The proposed method is first applied to the fault detection of an experimental aero-engine rotor. Finally, the proposed approach is used for the engineering application and it successfully identified weak crack fault in a demountable disk-drum aero-engine rotor. The results show that the proposed method possesses excellent performance in fault detection of aero-engine rotor.

Adopting a structural approach, repetitive and restitutive readings arise from different syntactic positions of repetitive modifiers (von Stechow 1996; Bale 2007; Lechner et al. 2015): If again attaches to the result-denoting phrase, it takes only the result state in its scope (43a). In contrast, if again attaches to the full verb phrase, i.e. to VoiceP, it necessarily takes wide scope over the whole complex event, including the causing action and the result state (43b). 2ff7e9595c