Gear fault diagnosis using active noise cancellation and adaptive wavelet transform

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.

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

Shock and Vibration ◽

10.1155/2016/6748469 ◽

2016 ◽

Vol 2016 ◽

pp. 1-10 ◽

Cited By ~ 11

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

Advances in Mechanical Engineering ◽

10.1177/1687814020927208 ◽

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.

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