Surface Contact Fatigue Failure Assessment in Spur Gears Using Lubricant Film Thickness and Vibration Signal Analysis

Amarnath, M.; Sujatha, C.

doi:10.1080/10402004.2014.971993

Cited by 19 publications

(5 citation statements)

References 26 publications

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“…RCF is a complex process influenced by many factors, such as the experimental conditions and coating properties. In addition, other variables, such as temperature information (Chen et al, 2017), the vibration signal (Amarnath and Sujatha, 2015;Zhang et al, 2012) and the acoustic emission signal (Zhang et al, 2012;Zhong-yu et al, 2019; Figure 5 Workflow of the case study of contact fatigue life prediction Guo-lu et al, 2013) reflect the process of fatigue degradation. In this work, a data set containing 95 RCFT values of AT40 ceramic coating fatigue from the literature is created (Runbo et al, 2019).…”

Section: Data Set Description From the Fatigue Experimentsmentioning

confidence: 99%

Prediction of contact fatigue life of AT40 ceramic coating based on neural network

Zhou

Xing

Wang

et al. 2020

ACMM

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Purpose With the development of deep learning-based analytical techniques, increased research has focused on fatigue data analysis methods based on deep learning, which are gaining in popularity. However, the application of deep neural networks in the material science domain is mainly inhibited by data availability. In this paper, to overcome the difficulty of multifactor fatigue life prediction with small data sets, Design/methodology/approach A multiple neural network ensemble (MNNE) is used, and an MNNE with a general and flexible explicit function is developed to accurately quantify the complicated relationships hidden in multivariable data sets. Moreover, a variational autoencoder-based data generator is trained with small sample sets to expand the size of the training data set. A comparative study involving the proposed method and traditional models is performed. In addition, a filtering rule based on the R2 score is proposed and applied in the training process of the MNNE, and this approach has a beneficial effect on the prediction accuracy and generalization ability. Findings A comparative study involving the proposed method and traditional models is performed. The comparative experiment confirms that the use of hybrid data can improve the accuracy and generalization ability of the deep neural network and that the MNNE outperforms support vector machines, multilayer perceptron and deep neural network models based on the goodness of fit and robustness in the small sample case. Practical implications The experimental results imply that the proposed algorithm is a sophisticated and promising multivariate method for predicting the contact fatigue life of a coating when data availability is limited. Originality/value A data generated model based on variational autoencoder was used to make up lack of data. An MNNE method was proposed to apply in the small data case of fatigue life prediction.

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Section: Data Set Description From the Fatigue Experimentsmentioning

confidence: 99%

Prediction of contact fatigue life of AT40 ceramic coating based on neural network

Zhou

Xing

Wang

et al. 2020

ACMM

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“…Therefore, the Cmor3-3, Cmor5-5, and Cmor7-7 wavelets could be used for the extraction of the nonlinear parameter in ultrasonic signals. The reason for these three wavelet functions having obvious advantages is that these are single-frequency complex sinusoidal functions under a Gaussian envelope, which have a better local focus in both the time and frequency domains, and its waveforms are similar to the analyzed signal [24,25].…”

Section: Factors Influencing Dwfpmentioning

confidence: 99%

Preliminary Research on the Nonlinear Ultrasonic Detection of the Porosity of Porous Material Based on Dynamic Wavelet Fingerprint Technology

Wang

Xie

et al. 2019

Sensors

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Porosity is an important characteristic of porous material, which affects mechanical and material properties. In order to solve the problem that the large distribution range of pore size of porous materials leads to the large detection errors of porosity, the non-linear ultrasonic testing technique is applied. A graphite composite was used as the experimental object in the study. As the accuracy of porosity is directly related with feature extraction, the dynamic wavelet fingerprint (DWFP) technology was utilized to extract the feature parameter of the ultrasonic signals. The effects of the wavelet function, scale factor, and white slice ratio on the extraction of the nonlinear feature are discussed. The SEM photos were conducted using gray value to identify the aperture. The relationship between pore diameter and detection accuracy was studied. Its results show that the DWFP technology could identify the second harmonic component well, and the extracted nonlinear feature could be used for the quantitative trait of porosity. The larger the proportion of the small diameter holes and the smaller the aperture distribution range was, the smaller the error was. This preliminary research aimed to improve the nondestructive testing accuracy of porosity and it is beneficial to the application of porous material in the manufacturing field.

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“…Related theories and experiments show that the gear is prone to pitting and gluing failure when the lubricating film is too thin. The value of oil film thickness on the gear tooth surface reflects the ability of gear to resist pitting and gluing failure [5]. It can be seen that the study of oil film thickness on the gear tooth surface has great theoretical research and engineering application value [6,7].…”

Section: Introductionmentioning

confidence: 99%

The research of oil film thickness on the gear tooth surface: A review

Sun¹,

Wang²,

Shu-shan³

2020

IJM

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Increasingly demanding performance standards and operating requirements are driving the growing interest on the influence of gear oil film on reliability under the conditions of high speed and heavy load. The fatigue life of gears can be increased by optimizing the oil film thickness on the gear tooth surface. The oil film thickness can reflect the ability of the gear to resist pitting and gluing failure. Many theories and experiments are devoted to the study of the oil film thickness. The improvement of gear manufacturing accuracy also makes the influence of tooth surface roughness on oil film thickness not negligible. In order to compile and categorize key investigations in an expansive field with substantial recent research, this work reviews oil film thickness with focus on numerical calculation methods, influence of gear surface roughness on oil film thickness and fatigue life analysis based on oil film thickness.

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Surface Contact Fatigue Failure Assessment in Spur Gears Using Lubricant Film Thickness and Vibration Signal Analysis

Cited by 19 publications

References 26 publications

Prediction of contact fatigue life of AT40 ceramic coating based on neural network

Prediction of contact fatigue life of AT40 ceramic coating based on neural network

Preliminary Research on the Nonlinear Ultrasonic Detection of the Porosity of Porous Material Based on Dynamic Wavelet Fingerprint Technology

The research of oil film thickness on the gear tooth surface: A review

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