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Vibration-Based Gearbox Fault Diagnosis by DWPT and PCA Approaches and an Adaptive Neuro-Fuzzy Inference System

Auteurs : Issam Attoui, Adel BOUDIAF, Nadir FERGANI, Brahim OUDJANI, Nadir Boutasseta, Adel DELIOU
Année : 2015
Domaine : Génie électrique
Type : Communication
Conférence: 16th international conference on Sciences and Techniques of Automatic control & computer engineering
Résumé en PDF : (résumé en pdf)
Fulltext en PDF : (.pdf)
Mots clés : gearfaults, vibration signal, Fault Diagnosis, ANFIS, D\WPT, FFT

Résumé :

In a wide variety of configurations in all sorts of rotating machines, gearboxes which can be subject to breakdowns or dysfunctions in its time-of-use, represents an essential part to transfer rotating power source to other devices and provide speed and torque conversions. The gearbox faults are a critical defect in rotating machinery that may have a direct influence on the availability of the machine itself and also on those of the surrounding systems. Hence, the continuous condition assessment and estimation of the state of this component is very important issue to increase production with quality assurance as per given specification at a reasonable cost and avoid unanticipated breakdowns and equipment failures. In this paper, a particular interest is carried to the analysis and diagnosis of these defects which can appear in the gear and bearing with various combinations under different speeds and loads. This paper consists of the application of the Wavelet Packet Transform WPT and Principal Component Analysis PCA to extract the features of the different sub-bands frequencies in the vibration signal from a rotating machine. These parameters will be used by the Adaptive Neural Fuzzy Inference System ANFIS to automate the fault detection and diagnosis process. Experimental results show that the proposed procedure can classify with precision various types of faults according to the fault location and type.