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Edwin Sudhagar P

Bio: Edwin Sudhagar P is an academic researcher from VIT University. The author has contributed to research in topics: Bearing (mechanical) & Composite material. The author has an hindex of 4, co-authored 4 publications receiving 34 citations.

Papers
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Journal ArticleDOI
TL;DR: The results showed that the simplicity of the DA and the superior performance of the ANFIS contributes to detecting bearing fault effectively and accurately in the diagnosis of industrial machines.

23 citations

Journal ArticleDOI
TL;DR: This work demonstrates a novel approach to develop a dynamic model for vibration response of spherical roller bearings using dimensional analysis with Buckingham’s pi theorem (BPT) by considering significant geometric, operating and thermal parameters of the system.

18 citations

Journal ArticleDOI
TL;DR: This study aims to address dimensional analysis; a new and imperative way to model the dynamic behavior of rolling-element bearings and their real-time performance in a rotor-bearing system.
Abstract: The marvelous uniqueness of vibration responses of faulty roller bearings can be simply observed through its vibration signature. Therefore, vibration analysis has been claimed as an effective tool...

15 citations

Journal ArticleDOI
TL;DR: In this paper , the transverse shear modulus of the glass-reinforced polymer honeycomb core (GFRP-HC) and glass-based polymer corrugated bioinspired model (CBIM) core were evaluated.
Abstract: Sandwich panels with cellular material cores are widely used in the aerospace, automotive, and marine industries. Although honeycombs with hexagonal cells are the most frequent shapes for cores, new possibilities in lightweight structures have lately emerged. The present work evaluates the transverse shear modulus of the glass-reinforced polymer honeycomb core (GFRP - HC) and glass-reinforced polymer corrugated bioinspired model (GFRP - CBIM) core. The experimental results from the alternative dynamic technique agreed well with the numerical simulation. In the GFRP sandwich beam with corrugated HC and CBIM's core, numerical free vibration analysis was performed. The CBIM core has a higher natural frequency than the HC core, according to the numerical data. Further, numerical analysis has been performed on the bioinspired cores by varying the side length and edge radius. Modified bioinspired model - 03 core design (MBIM03) appears to be a good option for regular honeycombs used in sandwich composite panels for industrial applications that demand low weight, high rigidity, and a large amount of energy-absorbing capacity.

5 citations


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Book ChapterDOI
01 Jan 1997
TL;DR: The boundary layer equations for plane, incompressible, and steady flow are described in this paper, where the boundary layer equation for plane incompressibility is defined in terms of boundary layers.
Abstract: The boundary layer equations for plane, incompressible, and steady flow are $$\matrix{ {u{{\partial u} \over {\partial x}} + v{{\partial u} \over {\partial y}} = - {1 \over \varrho }{{\partial p} \over {\partial x}} + v{{{\partial ^2}u} \over {\partial {y^2}}},} \cr {0 = {{\partial p} \over {\partial y}},} \cr {{{\partial u} \over {\partial x}} + {{\partial v} \over {\partial y}} = 0.} \cr }$$

2,598 citations

Journal ArticleDOI
TL;DR: In this article , a detailed review of data mining techniques for structural health monitoring (SHM) applications is presented, where a brief background, models, functions, and classification of DM techniques are presented.

46 citations

Journal ArticleDOI
TL;DR: The machine learning approach will be integrated with the proposed intelligent digital twin for the classification of the bearing anomaly and crack sizes and the average accuracy for the bearing fault pattern recognition and crack size identification will be 99.5% and 99.6%.
Abstract: In this study, the application of an intelligent digital twin integrated with machine learning for bearing anomaly detection and crack size identification will be observed. The intelligent digital twin has two main sections: signal approximation and intelligent signal estimation. The mathematical vibration bearing signal approximation is integrated with machine learning-based signal approximation to approximate the bearing vibration signal in normal conditions. After that, the combination of the Kalman filter, high-order variable structure technique, and adaptive neural-fuzzy technique is integrated with the proposed signal approximation technique to design an intelligent digital twin. Next, the residual signals will be generated using the proposed intelligent digital twin and the original RAW signals. The machine learning approach will be integrated with the proposed intelligent digital twin for the classification of the bearing anomaly and crack sizes. The Case Western Reserve University bearing dataset is used to test the impact of the proposed scheme. Regarding the experimental results, the average accuracy for the bearing fault pattern recognition and crack size identification will be, respectively, 99.5% and 99.6%.

26 citations

Journal ArticleDOI
TL;DR: The results showed that the simplicity of the DA and the superior performance of the ANFIS contributes to detecting bearing fault effectively and accurately in the diagnosis of industrial machines.

23 citations

Journal ArticleDOI
TL;DR: An input feature mappings-based deep residual network (ResNet) based signal-to-IFMs method is proposed to conduct detailed and comprehensive fault diagnosis on REB with complicated bearing dataset and yields average testing accuracies of 99.7%, 99.8%, and 99.81%, which outperforms other methods.
Abstract: Most rolling element bearing (REB) fault diagnosis algorithms are evaluated on the Case Western Reserve University (CWRU) bearing dataset for its popularity and simplicity. However, the diagnosis accuracy on CWRU bearing dataset is overly saturated; it is nearly up to 100%. In this study, an input feature mappings (IFMs)-based deep residual network (ResNet) is proposed to conduct detailed and comprehensive fault diagnosis on REB with complicated bearing dataset. Firstly, a new data preprocessing method named as a signal-to-IFMs method is proposed to automatically extract features from raw signals without predefined parameters. Then, a deep ResNet is used as the fault classifier to learn the discriminative features from IFMs and identify the faults of REB. Finally, the proposed model is evaluated on the artificial, real, and mixed damages of the Paderborn university bearing dataset. The proposed method yields the average testing accuracies of 99.7%, 99.7%, and 99.81% in artificial, real, and mixed bearing damages, which outperforms other methods.

22 citations