Crack Fault Diagnosis of Gun Automatic Mechanism Based on Multifractal Features

doi:10.3969/j.issn.1000-1093.2018.03.006

Abstract

Abstract: In order to make better use of vibration signals to diagnose the crack faults of gun automatic mechanism, a fault diagnosis method based on multifractal features of vibration signals is proposed. The proposed method uses Wavelet Leader to estimate the multifractal spectrum of vibration signals. 6 feature quantities are used to describe the morphological features of multifractal spectrum,and the dimensionality reduction of multifractal spectrum is realized. A classifier based on Mahalanobis distance is used to classify different crack faults. This method is applied to diagnose the crack faults of locking mechanism in a 12.7 mm antiaircraft machine gun, and the diagnostic accuracy is up to 82.5%, which verifies the feasibility of applying the multifractal features of vibration signals to the crack fault diagnosis of gun automatic mechanism. Key

Key words: gunautomaticmechanism, multifractalfeature, WaveletLeadermethod, Mahalanobisdistance, crackfaultdiagnosis

CLC Number:

TJ203⁺.2

REN Hai-feng, PAN Hong-xia. Crack Fault Diagnosis of Gun Automatic Mechanism Based on Multifractal Features[J]. Acta Armamentarii, 2018, 39(3): 457-462.

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第39卷
第3期2018 年3月兵工学报ACTA
ARMAMENTARIIVol.39No.3Mar.2018