Adaptive wavelet transform for vibration signal modelling and application in fault diagnosis of water hydraulic motor

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.

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Induction Machine Bearing Fault Diagnosis With Discrete Wavelet Transform Using Vibration Signal

i-manager’s Journal on Electrical Engineering ◽

10.26634/jee.5.4.1858 ◽

2012 ◽

Vol 5 (4) ◽

pp. 11-17

Author(s):

Ashwani Kumar Chandel ◽

◽

Raj Kumar Patel ◽

Keyword(s):

Wavelet Transform ◽

Fault Diagnosis ◽

Discrete Wavelet Transform ◽

Induction Machine ◽

Vibration Signal ◽

Discrete Wavelet ◽

Bearing Fault ◽

Bearing Fault Diagnosis

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Fault Diagnosis of a Helical Gearbox Based on an Adaptive Empirical Wavelet Transform in Combination with a Spectral Subtraction Method

Applied Sciences ◽

10.3390/app9081696 ◽

2019 ◽

Vol 9 (8) ◽

pp. 1696 ◽

Cited By ~ 2

Author(s):

Wang ◽

Lee

Keyword(s):

Wavelet Transform ◽

Fault Diagnosis ◽

Background Noise ◽

Vibration Signal ◽

Spectral Subtraction ◽

Accurate Identification ◽

Side Band ◽

Empirical Wavelet Transform ◽

Gear Fault ◽

Fault Characteristic

Fault characteristic extraction is attracting a great deal of attention from researchers for the fault diagnosis of rotating machinery. Generally, when a gearbox is damaged, accurate identification of the side-band features can be used to detect the condition of the machinery equipment to reduce financial losses. However, the side-band feature of damaged gears that are constantly disturbed by strong jamming is embedded in the background noise. In this paper, a hybrid signal-processing method is proposed based on a spectral subtraction (SS) denoising algorithm combined with an empirical wavelet transform (EWT) to extract the side-band feature of gear faults. Firstly, SS is used to estimate the real-time noise information, which is used to enhance the fault signal of the helical gearbox from a vibration signal with strong noise disturbance. The empirical wavelet transform can extract amplitude-modulated/frequency-modulated (AM-FM) components of a signal using different filter bands that are designed in accordance with the signal properties. The fault signal is obtained by building a flexible gear for a helical gearbox with ADAMS software. The experiment shows the feasibility and availability of the multi-body dynamics model. The spectral subtraction-based adaptive empirical wavelet transform (SS-AEWT) method was applied to estimate the gear side-band feature for different tooth breakages and the strong background noise. The verification results show that the proposed method gives a clearer indication of gear fault characteristics with different tooth breakages and the different signal-noise ratio (SNR) than the conventional EMD and LMD methods. Finally, the fault characteristic frequency of a damaged gear suggests that the proposed SS-AEWT method can accurately and reliably diagnose faults of a gearbox.

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