Fault Identification of Multi-level Gear Defects Using Adaptive Noise Control and a Genetic Algorithm

Nguyen, Cong Dai; Prosvirin, Alexander E.; Kim, Jong‐Myon

doi:10.1007/978-3-030-68449-5_32

Cited by 2 publications

(3 citation statements)

References 14 publications

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“…KNN performed fault classification based on the selected features (reduced dimensionality) to identify the gear defect types for validating the accuracy of the constructed model (Fw1). The details of Fw1 can be found in [56]. (2) LADT + GA + KNN (Fw2): To validate the improved denoising technique, the LADT module was used instead of the ANR-GRS module in the Fw1 to construct the Fw2.…”

Section: Dcna-based Identification Performance Analysismentioning

confidence: 99%

Gearbox Fault Identification Framework Based on Novel Localized Adaptive Denoising Technique, Wavelet-Based Vibration Imaging, and Deep Convolutional Neural Network

2021

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This paper proposes an accurate and stable gearbox fault diagnosis scheme that combines a localized adaptive denoising technique with a wavelet-based vibration imaging approach and a deep convolution neural network model. Vibration signatures of a gearbox contain important fault-related information. However, this useful fault-related information is often overwhelmed by random interference noises. Furthermore, the varying speed of gearboxes makes it difficult to distinguish the fault-related frequencies from the interference noises. To obtain a noise-free signal for extraction of fault-related information under variable speed conditions, first, a new localized adaptive denoising technique (LADT) is applied to the vibration signal. The new localized adaptive denoising technique results in optimized vibration sub-bands with negligible background noise. To obtain fault-related information, the wavelet-based vibration imaging approach (WVI) is applied to the denoised vibration signal. The wavelet-based vibration imaging approach decomposes the vibration signal into different time–frequency scales, these scales are reflected by a two-dimensional image called a scalogram. The scalograms obtained from the wavelet-based vibration imaging approach are provided as an input to the deep convolutional neural network architecture (DCNA) for extraction of discriminant features and classification of multi-degree tooth faults (MDTFs) in a gearbox under variable speed conditions. The proposed scheme outperforms the already existing state-of-the-art gearbox fault diagnosis methods with the highest classification accuracy of 100%.

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Section: Dcna-based Identification Performance Analysismentioning

confidence: 99%

Gearbox Fault Identification Framework Based on Novel Localized Adaptive Denoising Technique, Wavelet-Based Vibration Imaging, and Deep Convolutional Neural Network

2021

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“…The crack at the root of the tooth can reduce the time-varying meshing stiffness of the gear, thereby accelerating the tooth fracture process [4,5]. Research shows that the linear tooth surface scratch is one of the most common initial defects of gears [6,7]. For large equipment gears in the eld of wind power and ship, the working conditions of high speed and heavy load aggravate the wear rate of gears and magnifying the initial defects on the gear surface.…”

Section: Introductionmentioning

confidence: 99%

“…Nguyen et al [7] proposed a new gearbox fault sensitive identi cation system, which classi ed the types of faulty gear defects by analyzing the vibration characteristics of the gearbox. Yang et al [11] studied the in uence of tooth root crack defects on meshing stiffness and dynamic response, and proposed an improved calculation model for meshing stiffness of cracked gears.…”

Section: Introductionmentioning

confidence: 99%

Study on Wear Characteristics Evolution of Helical Gear With Linear Initial Defect

Zhu

Yuan

Guo

et al. 2021

Preprint

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The initial defects have greatly affected the gear transmission under harsh working conditions in the fields of wind power and ships. The influence of linear initial defects on the evolution of wear characteristics of helical gears was studied. The laser marking device was used to process the linear initial defect along the tooth width direction, and the gear without initial defect was used for comparison. It can be concluded that the linear initial defect changed the meshing state of the gear tooth, and greatly shortened the normal wear life of the gear, the normal wear life of the gear is shortened by about 45%, and the wear rate in the stable wear stage is increased by about 56%, a great deal of pitting corrosion and plastic flow on the tooth surface occurred in the pitch circle position of the defective gear. In addition, the lubrication condition deteriorated in the later period caused by lubricating oil pollution and the hard particles falling off the gearbox bearings entered the meshing surface and the emerged crack, which further accelerated the wear process of gear.

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Fault Identification of Multi-level Gear Defects Using Adaptive Noise Control and a Genetic Algorithm

Cited by 2 publications

References 14 publications

Gearbox Fault Identification Framework Based on Novel Localized Adaptive Denoising Technique, Wavelet-Based Vibration Imaging, and Deep Convolutional Neural Network

Gearbox Fault Identification Framework Based on Novel Localized Adaptive Denoising Technique, Wavelet-Based Vibration Imaging, and Deep Convolutional Neural Network

Study on Wear Characteristics Evolution of Helical Gear With Linear Initial Defect

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