Papers by Shweta Dabetwar
Lecture notes in civil engineering, Jun 16, 2022
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Journal of Building Engineering
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Journal of Building Engineering
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Journal of Building Engineering
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Health Monitoring of Structural and Biological Systems XVI
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ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg, 2022
The improvements in wind energy infrastructure have been a constant process throughout many decad... more The improvements in wind energy infrastructure have been a constant process throughout many decades. There are new advancements in technology that can further contribute towards the Prognostics and Health Management (PHM) in this industry. These advancements are driven by the need to fully explore the impact of uncertainty, quality and quantity of data, physics-based machine learning (PBML), and digital twin (DT). All these aspects need to be taken into consideration to perform an effective PHM of wind energy infrastructure. To address these aspects, four research questions were formulated. What is the role of uncertainty in machine learning (ML) in diagnostics and prognostics? What is the role of data augmentation and quality of data for ML? What is the role of PBML? What is the role of the DT in diagnostics and prognostics? The methodology used was Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA). A total of 143 records, from the last five years, were ana...
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ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg
Wind turbines suffer from mass imbalance due to manufacturing, installation, and severe climatic ... more Wind turbines suffer from mass imbalance due to manufacturing, installation, and severe climatic conditions. Condition monitoring systems are essential to reduce costs in the wind energy sector. Many attempts were made to improve the detection of faults at an early stage to plan predictive maintenance strategies, but effective methods have not yet been developed. Artificial intelligence has a huge potential in the wind turbine industry. However, several shortcomings related to the datasets still need to be overcome. Thus, the research question developed for this paper was “Can data augmentation and fusion techniques enhance the mass imbalance diagnostics methods applied to wind turbines using deep learning algorithms?” The specific aims developed were: (i) to perform sensitivity analysis on classification based on how many samples/sample frequencies are required for stabilized results; (ii) to classify the imbalance levels using Gramian angular summation field and Gramian angular di...
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This work attempts to answer the following research question: can fault imbalance diagnostics in ... more This work attempts to answer the following research question: can fault imbalance diagnostics in wind turbine rotors using electrical generator signals be improved with deep learning methods? For this purpose, a framework using TurbSim/FAST/Simulink was developed to simulate electric signals generated from a 1.5 MW WT for different wind inflow scenarios and blade imbalances parameters. The simulations were used to train, validate and test a deep learning algorithm, and the fault classification metrics were<br> obtained. It is possible to detect amplitude and frequency modulation from the current spectrum due to the imbalance, which spread the harmonics components in sidebands around its nominal frequency.
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ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg
The improvements in wind energy infrastructure have been a constant process throughout many decad... more The improvements in wind energy infrastructure have been a constant process throughout many decades. There are new advancements in technology that can further contribute towards the Prognostics and Health Management (PHM) in this industry. These advancements are driven by the need to fully explore the impact of uncertainty, quality and quantity of data, physics-based machine learning (PBML), and digital twin (DT). All these aspects need to be taken into consideration to perform an effective PHM of wind energy infrastructure. To address these aspects, four research questions were formulated. What is the role of uncertainty in machine learning (ML) in diagnostics and prognostics? What is the role of data augmentation and quality of data for ML? What is the role of PBML? What is the role of the DT in diagnostics and prognostics? The methodology used was Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA). A total of 143 records, from the last five years, were ana...
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Volume 13: Safety Engineering, Risk, and Reliability Analysis
Composite materials have tremendous and ever-increasing applications in complex engineering syste... more Composite materials have tremendous and ever-increasing applications in complex engineering systems; thus, it is important to develop non-destructive and efficient condition monitoring methods to improve damage prediction, thereby avoiding catastrophic failures and reducing standby time. Nondestructive condition monitoring techniques when combined with machine learning applications can contribute towards the stated improvements. Thus, the research question taken into consideration for this paper is “Can machine learning techniques provide efficient damage classification of composite materials to improve condition monitoring using features extracted from acousto-ultrasonic measurements?” In order to answer this question, acoustic-ultrasonic signals in Carbon Fiber Reinforced Polymer (CFRP) composites for distinct damage levels were taken from NASA Ames prognostics data repository. Statistical condition indicators of the signals were used as features to train and test four traditional...
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Composite materials have enormous applications in various fields. Thus, it is important to have a... more Composite materials have enormous applications in various fields. Thus, it is important to have an efficient damage detection method to avoid catastrophic failures. Due to the existence of multiple damage modes and the availability of data in different formats, it is important to employ efficient techniques to consider all the types of damage. Deep neural networks were seen to exhibit the ability to address similar complex problems. The research question in this work is ‘Can data fusion improve damage classification using the convolutional neural network?’ The specific aims developed were to 1) assess the performance of image encoding algorithms, 2) classify the damage using data from separate experimental coupons, and 3) classify the damage using mixed data from multiple experimental coupons. Two different experimental measurements were taken from NASA Ames Prognostic Repository for Carbon Fiber Reinforced polymer. To use data fusion, the piezoelectric signals were converted into i...
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Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems
Composite materials can be modified according to the requirements of applications, and hence, the... more Composite materials can be modified according to the requirements of applications, and hence, their applications are increasing significantly with time. Due to the complex nature of the aging of composites, it is equally challenging to establish structural health monitoring techniques. One of the most applied non-destructive techniques for this class of materials is using Lamb waves to quantify the damage. Another important advancement in damage detection is the application of deep neural networks. The data-driven methods have proven to be most efficient for damage detection in composites. For both of these advanced methods, the burning question always has been the requirement of data and quality of data. In this paper, these measurements were used to create a framework based on a deep neural network for efficient fault diagnostics. The research question developed for this paper was as follows: Can data fusion techniques used along with data augmentation improve the damage diagnosti...
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ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg
Composite materials have a myriad of applications in complex engineering systems, and multiple st... more Composite materials have a myriad of applications in complex engineering systems, and multiple structural health monitoring (SHM) strategies have been developed. However, these methods are challenging due to signal attenuation and excessive noise interference in composite materials. Signal processing can capture a small difference between the input–output signals associated with the severity of the damage in composites. Thus, the research question is “can signal processing techniques reduce the required number of features and assess the randomness of fatigue damage classification in composite materials using machine learning (ML) algorithms?” To answer this question, piezo-electric signals for carbon fiber reinforced polymer (CFRP) test specimens were taken from NASA Ames prognostics data repository. A framework based on a comparative analysis of signals was developed. For the first specific aim, the effectiveness of features based on statistical condition indicators of the sensor s...
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International Journal of Fatigue
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Volume 6A: Energy, 2016
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Papers by Shweta Dabetwar