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894 results on '"Sinha, Jyoti"'

3. 2-Steps Approach for Both Rotor and Bearing Faults Identification in Rotating Machines Using Measured Vibration Responses

Author

Almutairi, Khalid M., Sinha, Jyoti K., IFToMM, Series Editor, Ceccarelli, Marco, Advisory Editor, Corves, Burkhard, Advisory Editor, Glazunov, Victor, Advisory Editor, Hernández, Alfonso, Advisory Editor, Huang, Tian, Advisory Editor, Jauregui Correa, Juan Carlos, Advisory Editor, Takeda, Yukio, Advisory Editor, Agrawal, Sunil K., Advisory Editor, Ball, Andrew D., editor, Ouyang, Huajiang, editor, Sinha, Jyoti K., editor, and Wang, Zuolu, editor

Published
2024
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4. Damage Identification for Orthotropic Steel Bridge Girder Based on Wavelet Packet Permutation Entropy

Author

Zhou, Yutao, Zhuang, Yizhou, Sinha, Jyoti, IFToMM, Series Editor, Ceccarelli, Marco, Advisory Editor, Corves, Burkhard, Advisory Editor, Glazunov, Victor, Advisory Editor, Hernández, Alfonso, Advisory Editor, Huang, Tian, Advisory Editor, Jauregui Correa, Juan Carlos, Advisory Editor, Takeda, Yukio, Advisory Editor, Agrawal, Sunil K., Advisory Editor, Ball, Andrew D., editor, Ouyang, Huajiang, editor, Sinha, Jyoti K., editor, and Wang, Zuolu, editor

Published
2024
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5. Integrated Risk and Stakeholder Management in Industrial Maintenance Projects: The Bowtie Methodology

Author

Masarira, Mufaro, Papadopoulou, Kassandra A., Rahbarimanesh, Amir, Sinha, Jyoti K., IFToMM, Series Editor, Ceccarelli, Marco, Advisory Editor, Corves, Burkhard, Advisory Editor, Glazunov, Victor, Advisory Editor, Hernández, Alfonso, Advisory Editor, Huang, Tian, Advisory Editor, Jauregui Correa, Juan Carlos, Advisory Editor, Takeda, Yukio, Advisory Editor, Agrawal, Sunil K., Advisory Editor, Ball, Andrew D., editor, Ouyang, Huajiang, editor, Sinha, Jyoti K., editor, and Wang, Zuolu, editor

Published
2024
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6. Degradation Detection and RUL Prediction of Rolling Bearings Based on Gini Index and Particle Filter

Author

Wen, Haobin, Zhang, Long, Sinha, Jyoti K., IFToMM, Series Editor, Ceccarelli, Marco, Advisory Editor, Corves, Burkhard, Advisory Editor, Glazunov, Victor, Advisory Editor, Hernández, Alfonso, Advisory Editor, Huang, Tian, Advisory Editor, Jauregui Correa, Juan Carlos, Advisory Editor, Takeda, Yukio, Advisory Editor, Agrawal, Sunil K., Advisory Editor, Ball, Andrew D., editor, Ouyang, Huajiang, editor, Sinha, Jyoti K., editor, and Wang, Zuolu, editor

Published
2024
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7. Generic Smart Rotor Fault Diagnosis Model with Normalised Vibration Parameters

Author

Espinoza-Sepulveda, Natalia, Sinha, Jyoti, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Ivanov, Vitalii, Series Editor, Haddar, Mohamed, Series Editor, Cavas-Martínez, Francisco, Editorial Board Member, di Mare, Francesca, Editorial Board Member, Kwon, Young W., Editorial Board Member, Trojanowska, Justyna, Editorial Board Member, Xu, Jinyang, Editorial Board Member, Kumar, Uday, editor, Karim, Ramin, editor, Galar, Diego, editor, and Kour, Ravdeep, editor

Published
2024
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8. Rotor and Bearing Fault Classification of Rotating Machinery Using Extracted Features from Experimental Vibration Data and Machine Learning Approach

Author

Al Mutairi, Khalid M., Sinha, Jyoti K., Wen, Haobin, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Ivanov, Vitalii, Series Editor, Haddar, Mohamed, Series Editor, Cavas-Martínez, Francisco, Editorial Board Member, di Mare, Francesca, Editorial Board Member, Kwon, Young W., Editorial Board Member, Trojanowska, Justyna, Editorial Board Member, Xu, Jinyang, Editorial Board Member, Kumar, Uday, editor, Karim, Ramin, editor, Galar, Diego, editor, and Kour, Ravdeep, editor

Published
2024
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9. Remaining Useful Life Estimation for Anti-friction Bearing Prognosis Based on Envelope Spectrum and Variational Autoencoder

Author

Wen, Haobin, Zhang, Long, Sinha, Jyoti K., Almutairi, Khalid, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Ivanov, Vitalii, Series Editor, Haddar, Mohamed, Series Editor, Cavas-Martínez, Francisco, Editorial Board Member, di Mare, Francesca, Editorial Board Member, Kwon, Young W., Editorial Board Member, Trojanowska, Justyna, Editorial Board Member, Xu, Jinyang, Editorial Board Member, Kumar, Uday, editor, Karim, Ramin, editor, Galar, Diego, editor, and Kour, Ravdeep, editor

Published
2024
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10. Remaining Useful Life Prediction for Anti-friction Bearings Based on Envelope Spectrum and Extended Kalman Filter

Author

Wen, Haobin, Zhang, Long, Sinha, Jyoti K., Ceccarelli, Marco, Series Editor, Agrawal, Sunil K., Advisory Editor, Corves, Burkhard, Advisory Editor, Glazunov, Victor, Advisory Editor, Hernández, Alfonso, Advisory Editor, Huang, Tian, Advisory Editor, Jauregui Correa, Juan Carlos, Advisory Editor, Takeda, Yukio, Advisory Editor, Chu, Fulei, editor, and Qin, Zhaoye, editor

Published
2024
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19. Smart Online Monitoring of Industrial Pipeline Defects

Author

Sinha, Jyoti K., Papadopoulou, Kassandra A., Cavas-Martínez, Francisco, Series Editor, Chaari, Fakher, Series Editor, di Mare, Francesca, Series Editor, Gherardini, Francesco, Series Editor, Haddar, Mohamed, Series Editor, Ivanov, Vitalii, Series Editor, Kwon, Young W., Series Editor, Trojanowska, Justyna, Series Editor, Karim, Ramin, editor, Ahmadi, Alireza, editor, Soleimanmeigouni, Iman, editor, Kour, Ravdeep, editor, and Rao, Raj, editor

Published
2022
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20. Design for Vibration-Based Fault Diagnosis Model by Integrating AI and IIoT

Author

Espinoza-Sepulveda, Natalia F., Sinha, Jyoti K., Cavas-Martínez, Francisco, Series Editor, Chaari, Fakher, Series Editor, di Mare, Francesca, Series Editor, Gherardini, Francesco, Series Editor, Haddar, Mohamed, Series Editor, Ivanov, Vitalii, Series Editor, Kwon, Young W., Series Editor, Trojanowska, Justyna, Series Editor, Karim, Ramin, editor, Ahmadi, Alireza, editor, Soleimanmeigouni, Iman, editor, Kour, Ravdeep, editor, and Rao, Raj, editor

Published
2022
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21. Theoretical Validation of Experimental Rotor Fault Detection Model Previously Developed

Author

Espinoza Sepulveda, Natalia F., Sinha, Jyoti K., Ceccarelli, Marco, Series Editor, Agrawal, Sunil K., Advisory Editor, Corves, Burkhard, Advisory Editor, Glazunov, Victor, Advisory Editor, Hernández, Alfonso, Advisory Editor, Huang, Tian, Advisory Editor, Jauregui Correa, Juan Carlos, Advisory Editor, Takeda, Yukio, Advisory Editor, Zhen, Dong, editor, Wang, Dong, editor, Wang, Tianyang, editor, Wang, Hongjun, editor, Huang, Baoshan, editor, Sinha, Jyoti K., editor, and Ball, Andrew David, editor

Published
2021
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22. A Hybrid Framework for a Reliability Improvement of a Rolling Stock Door Subsystem

Author

Appoh, Frederick, Yunusa-Kaltungo, Akilu, Sinha, Jyoti Kumar, Kidd, Moray, Ceccarelli, Marco, Series Editor, Agrawal, Sunil K., Advisory Editor, Corves, Burkhard, Advisory Editor, Glazunov, Victor, Advisory Editor, Hernández, Alfonso, Advisory Editor, Huang, Tian, Advisory Editor, Jauregui Correa, Juan Carlos, Advisory Editor, Takeda, Yukio, Advisory Editor, Zhen, Dong, editor, Wang, Dong, editor, Wang, Tianyang, editor, Wang, Hongjun, editor, Huang, Baoshan, editor, Sinha, Jyoti K., editor, and Ball, Andrew David, editor

Published
2021
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23. Substrate induced composition change during Ge2Sb2Te5 atomic layer deposition and study of initial reactions on SiO2 surface.

Author

Sinha, Jyoti, Nyns, Laura, and Delabie, Annelies

Subjects
ATOMIC layer deposition, SUBSTRATES (Materials science), PHASE change materials, FLUORESCENCE spectroscopy, X-ray reflection
Abstract

Ge2Sb2Te5 (GST) is a well-known phase change material used in nonvolatile memory devices, photonics, and nonvolatile displays. This study investigates the impact of precursor sequence during atomic layer deposition (ALD) of GST from GeCl2.C4H8O2, SbCl3, and Te[(CH3)3Si]2 on Si/SiO2 substrates, focusing on growth per cycle, morphology, and composition along with initial precursor reactions on the substrate. We found that while thick layers approach stoichiometric Ge2Sb2Te5, a Ge-rich interfacial layer is initially formed, regardless of the binary ALD sequence used to start the deposition (GeTe vs Sb2Te3). Starting the ALD process with the GeTe-Sb2Te3 sequence results in a higher Ge content near the GST/SiO2 interface compared to the Sb2Te3-GeTe sequence. To understand these phenomena, we examine the initial precursor reactions on the SiO2 substrate by total reflection x-ray fluorescence spectrometry. The analysis reveals that SbCl3 exhibits lower reactivity with the SiO2 substrate than GeCl2.C4H8O2, and not all Ge adspecies react with the Te[(CH3)3Si]2 precursors. Additionally, the impact of the initial SiO2 surface on deposition extends over several ALD cycles, as the SiO2 surface only gradually gets covered by GST due to island growth. These processes contribute to the formation of a Ge-rich GST layer at the interface, irrespective of the precursor pulse sequence. These insights from the study may contribute to optimizing the initial deposition stages of GST and other ternary ALD processes to enable better composition control and enhanced device performance. [ABSTRACT FROM AUTHOR]

Published
2024
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24. From Envelope Spectra to Bearing Remaining Useful Life: An Intelligent Vibration-Based Prediction Model with Quantified Uncertainty.

Author

Wen, Haobin, Zhang, Long, and Sinha, Jyoti K.

Subjects
REMAINING useful life, PREDICTION models, RESONANCE, COMPARATIVE studies, ACQUISITION of data, BEARINGS (Machinery)
Abstract

Bearings are pivotal components of rotating machines where any defects could propagate and trigger systematic failures. Once faults are detected, accurately predicting remaining useful life (RUL) is essential for optimizing predictive maintenance. Although data-driven methods demonstrate promising performance in direct RUL prediction, their robustness and practicability need further improvement regarding physical interpretation and uncertainty quantification. This work leverages variational neural networks to model bearing degradation behind envelope spectra. A convolutional variational autoencoder for regression (CVAER) is developed to probabilistically predict RUL distributions with confidence measures. Enhanced average envelope spectra (AES) are used as network input for its physical robustness in bearing condition assessment and fault detection. The use of the envelope spectrum ensures that it contains only bearing-related information by removing other rotor-related frequencies, hence it improves the RUL prediction. Unlike traditional variational autoencoders, the probabilistic regressor and latent generator are formulated to quantify uncertainty in RUL estimates and learn meaningful latent representations conditioned on specific RUL. Experimental validations are conducted on vibration data collected using multiple accelerometers whose natural frequencies cover bearing resonance ranges to ensure fault detection reliability. Beyond conventional bearing diagnosis, envelope spectra are extended for statistical RUL prediction integrating physical knowledge of actual defect conditions. Comparative and ablation studies are conducted against benchmark models to demonstrate their effectiveness. [ABSTRACT FROM AUTHOR]

Published
2024
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31. Damage Identification in Steel Girder Based on Vibration Responses of Different Sinusoidal Excitations and Wavelet Packet Permutation Entropy.

Author

Zhou, Yutao, Zhuang, Yizhou, and Sinha, Jyoti K.

Subjects
PLATE girders, BOX beams, STEEL girders, WHITE noise, ENTROPY
Abstract

Damage identification, both in terms of size and location, in bridges is important for timely maintenance and to avoid any catastrophic failure. An earlier experimental study showed that damage in a steel box girder orthotropic plate can be successfully detected using the measured vibration acceleration data. In this study, the wavelet packet decomposition (WPD) method is used to analyze the measured vibration acceleration responses and then the estimation of the permutation entropy (PE) on the re-constructed signals. A damage index is then defined based on the permutation entropy difference (PED) between the damaged and the healthy conditions to detect the location and size of the damage. The method is further validated through the finite element (FE) model of a steel box girder and the computed vibration acceleration responses when subjected to the sinusoidal excitations at different frequencies. In addition, the robustness of the methodology under different white noise interference conditions is also verified. The results show that the proposed methodology can effectively identify the location of human-made damage and accurately estimate the degree of damage under different frequencies of sinusoidal excitation. The method has shown a strong anti-noise property. [ABSTRACT FROM AUTHOR]

Published
2024
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32. Fault Detection of Rotating Machines Using poly -Coherent Composite Spectrum of Measured Vibration Responses with Machine Learning.

Author

Almutairi, Khalid, Sinha, Jyoti K., and Wen, Haobin

Subjects
ARTIFICIAL neural networks, MACHINE learning, ROTOR vibration, VIBRATION (Mechanics), VIBRATION measurements
Abstract

This study presents an efficient vibration-based fault detection method for rotating machines utilising the poly-coherent composite spectrum (pCCS) and machine learning techniques. pCCS combines vibration measurements from multiple bearing locations into a single spectrum, retaining amplitude and phase information while reducing background noise. The use of pCCS significantly reduces the number of extracted parameters in the frequency domain compared to using individual spectra at each measurement location. This reduction in parameters is crucial, especially for large industrial rotating machines, as processing and analysing extensive datasets demand significant computational resources, increasing the time and cost of fault detection. An artificial neural network (ANN)-based machine learning model is then employed for fault detection using these reduced extracted parameters. The methodology is developed and validated on an experimental rotating machine at three different speeds: below the first critical speed, between the first and second critical speeds, and above the second critical speed. This range of speeds represents the diverse dynamic conditions commonly encountered in industrial settings. This study examines both healthy machine conditions and various simulated fault conditions, including misalignment, rotor-to-stator rub, shaft cracks, and bearing faults. By combining the pCCS technique with machine learning, this study enhances the reliability, efficiency, and practical applicability of fault detection in rotating machines under varying dynamic conditions and different machine conditions. [ABSTRACT FROM AUTHOR]

Published
2024
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