Gear Defect Modeling of a Multiple‐Stage Gear Train

Sommer, Andrew; Meagher, Jim; Wu, Xi

doi:10.1155/2011/754257

Cited by 4 publications

(4 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As shown in Fig. 4 (a and b) [36], the gear is a part with cut teeth, each of which meshes with another toothed part to transmit torque to adjust the speed, torque, and direction of a power source. The Most gear faults occur in the tooth surface, such as a pitted tooth, or in the breakage of the gear tooth such as a cracked, chipped, or missing tooth, as shown in Fig.…”

Section: Gear Defectsmentioning

confidence: 99%

A New Deep Stacked Architecture for Multi-Fault Machinery Identification With Imbalanced Samples

et al. 2021

View full text Add to dashboard Cite

Effective intelligent fault diagnosis of rotating machinery using its vibrational signals has a considerable influence on certain analysis factors such as the reliability, performance, and productivity of a variety of modern manufacturing machines. Traditional intelligent approaches lack generalization schemes and add the burden of extracting features from data-driven cases. On the other hand, the Deep Learning (DL) studies have reported capabilities higher than the expectations of the researchers' objectives. In this context, this paper proposes a new deep architecture based on Stacked Variant Autoencoders for multi-fault machinery identification with imbalanced samples. The proposed model starts with a Variational Autoencoder (VAE) for facilitating data augmentation of small and imbalanced data samples using Gaussian distribution. After the preparation of suitable samples based on quality and size, the preprocessed vibration signals obtained are injected into the deep framework. The proposed deep architecture contains two subsequent unsupervised Sparse Autoencoders (SAE) with a penalty term that helps in acquiring more abstract and essential features as well as avoiding redundancy. The output of the second SAE is integrated on a supervised Logistic Regression (LR) with 10 classes. This is utilized for the proposed classifier training to achieve accurate fault identification. Experimental results show the efficiency of the proposed model which achieved an accuracy of 93.2%. In addition, for extensive comparative analysis issue, the Generative Adversarial Network (GAN) and triNetwork Generative Adversarial Network (tnGAN) were both implemented on the vibrational signal data, where the proposed method reported better results in terms of training and testing time and overall accuracy.

show abstract

Section: Gear Defectsmentioning

confidence: 99%

A New Deep Stacked Architecture for Multi-Fault Machinery Identification With Imbalanced Samples

et al. 2021

View full text Add to dashboard Cite

show abstract

“…In the study the authors obtained very similar results for analyzed models, which can be applied to analyze a rattle noises that occur in an automotive transmission. Other papers [9,10] treat about a multibody dynamic model of a spur and planetary gears developed in the MSC ADAMS software. In the study [9], authors present a model of the crank-slider mechanism.…”

Section: Introductionmentioning

confidence: 99%

“…Other papers [9,10] treat about a multibody dynamic model of a spur and planetary gears developed in the MSC ADAMS software. In the study [9], authors present a model of the crank-slider mechanism. Vibration signals behavior in case of a geometric defects like a chipped tooth and eccentric tooth was presented in the paper.…”

Section: Introductionmentioning

confidence: 99%

Model of the Gears Based on Multibody System and Its Validation by Application of Non-Contact Methods

et al. 2013

View full text Add to dashboard Cite

In the paper an application of laser measurements for validation of the gears dynamic model have been presented. The model of the gears was compared with an experimental data in case of diagnostics of manufacturing failures. The comparison was done on the basis of vibration signals generated by the model and the real object. In the experiment correct work as well as incorrect one of the gears was studied. Purpose of the paper is to compare the model with the experimental data; to this end, the Multibody model of the gears was build in MSC ADMS software. The model consists of two gears and two shafts, with both gears and shafts treated as rigid bodies, but contact surfaces between them are exible. To conclude, in the study comparison of acceleration signals estimates behavior due to other gears faults in the experiment and in the model has been presented. For optimal model validation, laser measurements on the shaft in the experiment were conducted.

show abstract

“…The simulation results for other technical states (broken tooth) in this paper were presented. The other model of a spur gears was presented in (Sommer et al 2011). In this study the multi-body kinematic model of a slider-crank mechanism was presented, the model was developed by a specialized software.…”

Section: Introductionmentioning

confidence: 99%