Extraction of Gearbox Fault Features from Vibration Signal Using Wavelet Transform

Purpose -The purpose of this paper is to detect gears' faults with an automatic decision-making process and find a reliable method to detect faults on gear systems using a composition of conventional methods. Design/methodology/approach -First, the vibration behavior of gears during engagement is investigated. Then, after studying different methods of fault detection using vibration signals analysis, a suitable method is proposed for the case of gears. For this purpose, a fuzzy model is employed based on available knowledge about fault detection of gears and results obtained from vibration behavior of gears. In the mentioned fuzzy model, a feature extracted from wavelet transform and also a couple of statistical indexes are used as fault criteria. Findings -Using fuzzy systems instead of numerous data in training the decision-making system and also utilizing available knowledge of gears' signals and information of fault effects can significantly simplify the decision-making process in auto-detecting gears faults, considering difficulty of laboratory set up, manufacturing and different faults creation, as well as, lack of sufficient data. Practical implications -In order to validate and enhance the proposed model, an empirical set up is manufactured and tested. Later on, the model is tested on another set of gears. Originality/value -Although the gears' faults were completely different from those of experimental set up, promising results in detecting faults were obtained. Moreover, it is shown that it is possible to determine the level of gears' health, as well as to estimate the gears' status, owing to fuzzy logic. This issue can be observed in the change of fault parameter while analyzing signals related to the fault growth in gears.

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“…This transform is a kind of linear transform in two-dimensional space and is time-based interpretation of a signal (Ren et al, 2006).…”

Section: Wavelet Transformmentioning

confidence: 99%

“…Wavelet transform is decomposition of a signal with a set of functions called wavelet. This transform is a kind of linear transform in two‐dimensional space and is time‐based interpretation of a signal (Ren et al , 2006).…”

Section: Wavelet Transformmentioning

confidence: 99%

Design of a fuzzy model based on vibration signal analysis to auto‐detect the gear faults

Hashemi

Safizadeh²

2013

Industrial Lubrication and Tribology

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A proposed approach to the classification of bearing condition using wavelets and random forests

Ferenc¹,

Lutovac²,

Kvrgić³

et al. 2013

2013 2nd Mediterranean Conference on Embedded Computing (MECO)

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This paper presents a proposed approach to the classification of rolling element bearing faults. The approach consists of vibration signal acquisition, digital signal processing, feature extraction from the vibration signal and classification into functional or defective rolling element bearing. Digital signal processing includes signal decomposition and de-nosing using wavelets. An I8-dimensional vector of the vibration signal feature is obtained as a result of feature extraction. Characterization of each recorded vibration signal is performed by a combination of signal's time varying statistical parameters and characteristic rolling element bearing fault frequency components. The classification is performed using random forests algorithm.

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Vibration Based Fault Detection of Centrifugal Pump by Fast Fourier Transform and Adaptive Neuro-Fuzzy Inference System

Farokhzad¹

2013

JMET

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Extraction of Gearbox Fault Features from Vibration Signal Using Wavelet Transform

Cited by 4 publications

References 2 publications

Design of a fuzzy model based on vibration signal analysis to auto‐detect the gear faults

Design of a fuzzy model based on vibration signal analysis to auto‐detect the gear faults

A proposed approach to the classification of bearing condition using wavelets and random forests

Vibration Based Fault Detection of Centrifugal Pump by Fast Fourier Transform and Adaptive Neuro-Fuzzy Inference System

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