scholarly journals New method for gear fault diagnosis using empirical wavelet transform, Hilbert transform, and cosine similarity metric

2020 ◽  
Vol 12 (6) ◽  
pp. 168781402092720
Author(s):  
Toufik Bettahar ◽  
Chemseddine Rahmoune ◽  
Djamel Benazzouz ◽  
Boualem Merainani
Keyword(s):  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Hilbert Transform ◽  
Cosine Similarity ◽  
Similarity Metric ◽  
Feature Extraction Method ◽  
Empirical Wavelet Transform ◽  
Gear Fault ◽  
Gear Fault Diagnosis ◽  
New Feature

In this article, a new feature extraction method is proposed for gear fault diagnosis by combining the empirical wavelet transform, Hilbert transform, and cosine similarity metric. In the first place, a number of empirical mode components acquisitions are done, using empirical wavelet transform. Since different empirical modes have different sensitivities to fault, not all of them are needed for further analysis. Therefore, the most sensitive empirical modes are selected using the cosine similarity metric method. Hilbert transform was then used to obtain the envelope for amplitude modulation. Finally, spectral analysis using fast Fourier transform is applied on the obtained envelope. Gear test rig with gears under different fault states has revealed an effective outcome and a solid stability of this new approach. The obtained results show that our approach is efficiently able to detect and expose the gear faults signatures, that is, it highlights their frequencies and the corresponding harmonics with respect to the rotary frequency. Furthermore, this proposed method demonstrates more satisfactory and advantageous performances compared to those of fast kurtogram, or the autogram.

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

2019 ◽  
Vol 9 (8) ◽  
pp. 1696 ◽  
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.

Gear Fault Diagnosis Based on Wavelet Transform

2012 ◽  
Vol 503-504 ◽  
pp. 1550-1553
Author(s):  
Gui Ji Tang ◽  
Jiao Wu ◽  
Zi Rui Wang
Keyword(s):  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Paper Analysis ◽  
Fault Analysis ◽  
Test Bed ◽  
Monitoring And Diagnosis ◽  
Operation Conditions ◽  
Site Monitoring ◽  
Gear Fault ◽  
Gear Fault Diagnosis

Gearbox operation conditions have a great effect on the equipment of the whole machine directly; therefore, the gear is the main object in the site monitoring and diagnosis. This paper analysis pitting corrosion signals getting from the test-bed. The signals are analyzed and processed in wavelet and Hilbert transform on Matlab. It is shown that wavelet analysis transformation are a kind of effective method on gear fault analysis.

scholarly journals Feature Extraction Using Discrete Wavelet Transform for Gear Fault Diagnosis of Wind Turbine Gearbox

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Rusmir Bajric ◽  
Ninoslav Zuber ◽  
Georgios Alexandros Skrimpas ◽  
Nenad Mijatovic
Keyword(s):  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Discrete Wavelet Transform ◽  
High Speed ◽  
Discrete Wavelet ◽  
New Approach ◽  
Condition Indicators ◽  
Gear Fault ◽  
Gear Fault Diagnosis

Vibration diagnosis is one of the most common techniques in condition evaluation of wind turbine equipped with gearbox. On the other side, gearbox is one of the key components of wind turbine drivetrain. Due to the stochastic operation of wind turbines, the gearbox shaft rotating speed changes with high percentage, which limits the application of traditional vibration signal processing techniques, such as fast Fourier transform. This paper investigates a new approach for wind turbine high speed shaft gear fault diagnosis using discrete wavelet transform and time synchronous averaging. First, the vibration signals are decomposed into a series of subbands signals with the use of a multiresolution analytical property of the discrete wavelet transform. Then, 22 condition indicators are extracted from the TSA signal, residual signal, and difference signal. Through the case study analysis, a new approach reveals the most relevant condition indicators based on vibrations that can be used for high speed shaft gear spalling fault diagnosis and their tracking abilities for fault degradation progression. It is also shown that the proposed approach enhances the gearbox fault diagnosis ability in wind turbines. The approach presented in this paper was programmed in Matlab environment using data acquired on a 2 MW wind turbine.

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