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Recent Advances in Electrical & Electronic Engineering

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ISSN (Print): 2352-0965
ISSN (Online): 2352-0973

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Research Article

Application of Adaptive Wavelet Transform for Gear Fault Diagnosis Using Modified-LLMS Based Filtered Vibration Signal

Author(s): Sudarsan Sahoo* and Jitendra K. Das

Volume 12, Issue 3, 2019

Page: [257 - 262] Pages: 6

DOI: 10.2174/2352096511666180525123616

Price: $65

Abstract

Background: Vibration signature acquired from a gear mesh can be used to identify the defect present in a gear mesh hence can be used to diagnose the condition of a gear mesh. But the signal acquired from the subject may not be noise free and may be non stationary.

Methods: Before going for the analysis of the acquired signal a preprocessing on the acquired signal is required to make it noise free. In the present work in first phase, the acquired vibration signal is filtered to reduce the noise and to improve the SNR (signal to noise ratio). The filtering is done by an Adaptive Noise Cancellation (ANC) technique. A modified Leaky Least Mean Square (LLMS) based adaptive algorithm along with a digital filter is used to achieve the ANC. The signal acquired from a healthy gear is used as the reference signal for the adaptive filter based de-noising process. In the second phase of the present work Adaptive Wavelet Transform (AWT) is used to detect the fault by extracting the features from the filtered vibration signal. From the signal pattern the adaptive wavelet is designed. The adaptive wavelet scalogram is compared with the standard wavelet scalogram.

Results: The result shows that the adaptive wavelet scalogram is better in analyzing the vibration signal. In this work a gear drive experimental set-up is made. Two different types of defective gears are used for the experiment. In type-1 defective gear one tooth is broken and in type-2 defective gear two teeth are broken. Initially, the vibration signal is acquired from a healthy gear which is used as the reference signal. Then the vibration signal from type-1 defective gear and type-2 defective gear is acquired and processed for the analysis and to identify the defects.

Conclusion: The present work shows that with the application of modified-LLMS algorithm and AWT the proposed technique of signal processing is more suitable for the fault identification and hence for the condition monitoring of the gear.

Keywords: Adaptive wavelet, Adaptive Noise Cancellation (ANC), gear fault, modified-LLMS, adaptive algorithm, leaky least mean square.

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