CLASSIFICATION OF HELICAL GEAR FAULT LEVELS USING FREQUENCY COMPONENT BASED STATISTICAL ANALYSIS WITH ANN

Year 2018, Volume: 1 Issue: 2, 76 - 86, 30.12.2018

Rafet Can Ümütlü , Berkan Hızarcı , Hasan Ozturk , Zeki Kıral

Abstract

Gearboxes
that are frequently used as power transmission elements are experienced various
faults over time. The pitting fault, which is one of these faults, is usually
caused by insufficient lubrication and overloading. Detecting pitting fault and
identifying different pitting levels are challenging subjects in gear fault
detection. This study aims to propose a method to classify the different levels
of pitting fault in helical gearbox. It is known that gear defects illustrate
themselves in vibration signal at gear mesh frequency (GMF) and its harmonics. As
the severity of faults on the tooth surface grows, the amplitude of these
frequencies usually increases in the frequency spectrum. Frequency component
based statistical analysis (FCSA) method is utilized to obtain stronger
indicators for fault classification. In this study, frequency component based
statistical analysis calculates the mean, standard deviation, RMS and Kurtosis
values of narrowband gear vibrations obtained around the GMF and its harmonics
in order to detect these increases in the frequency spectrum. Moreover, these
statistical parameters are then used as an input for training and testing of
artificial neural network (ANN) for classification of pitting faults.
Furthermore, the pitting fault is detected and different pitting levels are classified.
It has been found that the proposed approach is quite beneficial for not only
detection, but also classification of pitting fault levels in helical
gearboxes.

Keywords

ANN, fault classification, helical gear, pitting, vibration, spectrum analysis

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