Diagnosis of gear tooth fault in a bevel gearbox using discrete wavelet transform and autoregressive modeling

Sharma, Snehsheel; Tiwari, Sanjay; Singh, Sukhjeet

doi:10.1007/s41872-018-0061-9

Cited by 15 publications

(5 citation statements)

References 26 publications

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“…The complexity of Discrete Fourier Transform is 𝑂(𝑛 2 ) , whereas Fast Fourier Transform has a complexity of 𝑂(𝑛𝑙𝑜𝑔 2 𝑛) , where 'n' represents the signal length, 𝑦(𝑛) is the nth sample, and 𝑁 is the length of the signal. The Fast Fourier Transform formula is as follows: Transform and Adaptive Neuro-Fuzzy Inference, gear defect identification was carried out, revealing that this method achieves a high level of accuracy compared to traditional visual inspection methods [20]. Using Discrete Wavelet…”

Section: Gear Tooth Surface Modificationmentioning

confidence: 99%

Development of Defect Detection Models for Standard and Modified Gear Tooth Surfaces based on FFT/DWT

Huang,

Wu,

Lai

2024

Preprint

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Gears play a pivotal role in power transmission systems. Over prolonged periods of high-load transmission, the issue of gear lifespan has become a challenging one to address. Monitoring systems are vital in today's smart manufacturing landscape, contributing to safety and sustainable development. Presently, there is a dearth of research focused on the vibration of gears with surface modification. Consequently, this study has devised a novel defect detection model for surface modification gears. It employs time-domain, frequency-domain, and discrete wavelet transform (DWT) analysis in combination with deep neural network (DNN) deep learning to validate the effectiveness of this approach. The study deals with gears in two states: undamaged and wear. Vibration data was sampled for 800 seconds at a rate of 1024 Hz. Leveraging Artificial Intelligence, this research opens the door to innovative applications and services in the field of gear manufacturing. Notably, DWT exhibits a significant reduction in the amplitude of vibrations between surface modification helical gears and surface modification spur gears. Furthermore, from the DNN training and accuracy, it is evident that there is no substantial difference in accuracy between epochs 30 and epochs 100.

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Section: Gear Tooth Surface Modificationmentioning

confidence: 99%

Development of Defect Detection Models for Standard and Modified Gear Tooth Surfaces based on FFT/DWT

Huang,

Wu,

Lai

2024

Preprint

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“…Compared with CWT, the signal after DWT not only has the characteristics of no redundant decomposition and accurate reconstruction, but also can show the time-frequency characteristics of the fault fully. At the same time, the calculation time is also reduced greatly [24].…”

Section: Discrete Wavelet Transformmentioning

confidence: 99%

Convolutional neural network intelligent fault diagnosis method for rotating machinery based on discriminant correlation analysis multi-domain feature fusion strategy

Lan,

Shi

2024

J. vibroeng.

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Aiming at solving the problems of limited training data, single input information, and limited diagnostic accuracy under the influence of strong background noise in fault diagnosis of rotating machinery, this paper proposes a fault diagnosis method based on the combination of discriminant correlation analysis (DCA) and convolutional neural network (CNN). Firstly, the original vibration signal is divided into several segments in the time domain, and the training data is directly processed by one CNN branch to extract multi-scale time domain features. Simultaneously, the divided data is subjected to discrete wavelet transform (DWT), and processed by another branch of CNN to extract multi-scale time-frequency features. Then, the DCA feature fusion mechanism is adopted to fuse the two-domain features extracted in the parallel branches to improve the model’ detection ability. Finally, the fused features are input into the deep CNN for training and learning to extract new features and output the classification results. Through the experimental analysis of two different types of data, the results show that the proposed method can be used for fault diagnosis of rotating machinery effectively. Compared with the single CNN network, the proposed method combines the multi-domain multi-scale feature extraction module with the DCA feature fusion module to enrich the feature information extraction ability. At the same time, the network performance is improved to get higher fault classification accuracy higher.

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“…It is suitable for modeling signals having sharp peaks such as current sinusoidal signals (Wang and Wong, 2002). A random deterministic series is predicted accurately based on its infinite past (Sharma et al, 2019). It simply means that a time series Z ( n ) could be represented by an infinite linear combination of all its preceding points, that is, a linear combination of preceding signal values along with white noise ( ϵ ) constitutes a linear autoregressive model y ( t ) (McCormick et al, 1998):Here, a ( i ) is the model coefficients of the AR model of i th order.…”

Section: Autoregressive (Ar) Modelingmentioning

confidence: 99%

Residual signal–based condition monitoring of planetary gearbox using electrical signature analysis

Sharma

Tiwari

2023

Journal of Vibration and Control

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Electrical signature–based technique, due to its non-intrusive nature, is very useful for condition monitoring of rotary machines. Major challenge is the dominance of line frequency in the current signature. The present study focused on decreasing this dominance of the line frequency by obtaining the residual signals through autoregressive modeling. The residual signals are used further to extract the health-related features. The recently developed weighted multi-scale fluctuation-based dispersion entropy features are extracted as health indicators. The extracted health indicators are used for classification of different types of planetary gearbox faults. The results reflect that the proposed methodology has the potential for diagnosing different types of planetary gearbox faults with acceptable accuracy values.

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Diagnosis of gear tooth fault in a bevel gearbox using discrete wavelet transform and autoregressive modeling

Cited by 15 publications

References 26 publications

Development of Defect Detection Models for Standard and Modified Gear Tooth Surfaces based on FFT/DWT

Development of Defect Detection Models for Standard and Modified Gear Tooth Surfaces based on FFT/DWT

Convolutional neural network intelligent fault diagnosis method for rotating machinery based on discriminant correlation analysis multi-domain feature fusion strategy

Residual signal–based condition monitoring of planetary gearbox using electrical signature analysis

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