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The present study investigates the fabrication of hierarchical 3D nanostructures with multi-component metal oxides in the presence of highly-porous graphene and characterized for its applications in high-performance supercapacitors. A hierarchical flowers like 3D nanostructure of Co 3 O 4 @MnO 2 on nitrogen-doped graphene oxide (NGO) hybrid composite was synthesized by thermal reduction process at 650 °C in the presence of ammonia and urea. The synthesized Co 3 O 4 @MnO 2 /NGO hybrid composites were studied via Raman, XRD, X-ray XPS, FE-SEM, FE-SEM with EDX, FE-TEM and BET analyses. The electrochemical analysis of Co 3 O 4 @MnO 2 /NGO hybrid composite electrode was investigated using cyclic voltammetry, chronopotentiometry and electrochemical impedance measurements. The hybrid composite electrode showed significant specific capacitance results of up to 347 F/g at 0.5 A/g and a corresponding energy density of 34.83 Wh kg −1 with better rate performance and excellent long-term cycling stability were achieved for 10,000 cycles. The obtained electrochemical results paved a way to utilize Co 3 O 4 @MnO 2 /NGO composite electrode as a promising electrode material in high performance supercapacitors.

Deep learning has helped achieve breakthroughs in a variety of applications; however, the lack of data from faulty states hinders the development of effective and robust diagnostic strategies using deep learning models. This work introduces a transfer learning framework for the autonomous detection, isolation, and quantification of delamination in laminated composites based on scarce low-frequency structural vibration data. Limited response data from an electromechanically coupled simulation model and from experimental testing of laminated composite coupons were encoded into high-resolution time-frequency images using SynchroExtracting Transforms (SETs). The simulated and experimental data were processed through different layers of pretrained deep learning models based on AlexNet, GoogleNet, SqueezeNet, ResNet-18, and VGG-16 to extract low- and high-level autonomous features. The support vector machine (SVM) machine learning algorithm was employed to assess how the identified autonomous features were able to assist in the detection, isolation, and quantification of delamination in laminated composites. The results obtained using these autonomous features were also compared with those obtained using handcrafted statistical features. The obtained results are encouraging and provide a new direction that will allow us to progress in the autonomous damage assessment of laminated composites despite being limited to using raw scarce structural vibration data.

In this study, nickel hydroxide nanoparticles (NPs) decorated with nitrogen doped multiwalled carbon nanotubes (N-MWCNT) hybrid composite was synthesized by thermal reduction process in the presence of cetyl ammonium bromide (CTAB) and urea. The as-synthesized Ni(OH) 2 @N-MWCNT hybrid composite was characterized by FTIR, Raman, XRD, BET, BJH and FE-TEM analyses. These prepared porous carbon hybrid composite materials possessed high specific surface area and sheet like morphology useful for active electrode materials. The maximum specific capacitance of Ni(OH) 2 @N-MWCNT hybrid nanocomposite in the two electrode system showed 350 Fg −1 at 0.5 A/g,energy density ~43.75 Wkg −1 and corresponds to power density 1500 W kg −1 with excellent capacity retention after 5000 cycles. The results suggest that the prepared two-dimensional hybrid composite is a promising electrode material for electrochemical energy storage applications.

Delamination is one of the detrimental defects in laminated composite materials that often arose due to manufacturing defects or in-service loadings (e.g., low/high velocity impacts). Most of the contemporary research efforts are dedicated to high-frequency guided wave and mode shape-based methods for the assessment (i.e., detection, quantification, localization) of delamination. This paper presents a deep learning framework for structural vibration-based assessment of delamination in smart composite laminates. A number of small-sized (4.5% of total area) inner and edge delaminations are simulated using an electromechanically coupled model of the piezo-bonded laminated composite. Healthy and delaminated structures are stimulated with random loads and the corresponding transient responses are transformed into spectrograms using optimal values of window size, overlapping rate, window type, and fast Fourier transform (FFT) resolution. A convolutional neural network (CNN) is designed to automatically extract discriminative features from the vibration-based spectrograms and use those to distinguish the intact and delaminated cases of the smart composite laminate. The proposed architecture of the convolutional neural network showed a training accuracy of 99.9%, validation accuracy of 97.1%, and test accuracy of 94.5% on an unseen data set. The testing confusion chart of the pre-trained convolutional neural network revealed interesting results regarding the severity and detectability for the in-plane and through the thickness scenarios of delamination.

Here, we developed a new approach to synthesize NiCo 2 S 4 thin films for supercapacitor application using the successive ionic layer adsorption and reaction (SILAR) method on Ni mesh with different molar ratios of Ni and Co precursors. The five different NiCo 2 S 4 electrodes affect the electrochemical performance of the supercapacitor. The NiCo 2 S 4 thin films demonstrate superior supercapacitance performance with a significantly higher specific capacitance of 1427 F g −1 at a scan rate of 20 mV s −1 . These results indicate that ternary NiCo 2 S 4 thin films are more effective electrodes compared to binary metal oxides and metal sulfides.

MnO 2 /NiCo 2 O 4 @N-MWCNT hybrid nanocomposite was synthesized by the hydrothermal route using ammonia and urea as catalysts. The structural, morphological and compositional properties of the hybrid composites were analyzed using XRD, SEM, HR-TEM SEM-EDAX, XPS, FTIR, and Raman measurements. The electrochemical properties of the prepared hybrid composite were studied by cyclic voltammetry analysis. The outcome of the electrochemical studies revealed a specific capacitance of ~543 Fg −1 at 0.5 A g −1 current density in the KOH (6 M) electrolyte, with a stability of ~88% up to 5000 cycles. The obtained results clearly demonstrated the significance of the nanostructured MnO 2 /NiCo 2 O 4 @N-MWCNT hybrid composite in supercapacitor applications. Hydrothermal prepared well-defined nanocrystalline MnO 2 @NiCo 2 O 4 /N-doped multiwalled carbon nanotubes (N-MWCNTs) composite was successfully employed in supercapacitor application. Highlights Controlled synthesis of MnO 2 @NiCo 2 O 4 /N-MWCNT hybrid composite by hydrothermal process is reported. MnO 2 @NiCo 2 O 4 /N-MWCNT hybrid composite showed specific capacitance ~543 Fg −1 at 0.5 A.g −1 . Nanocrystalline morphology of the composite material enhanced the electrochemical properties. The hybrid composite shows the excellent capacitance retention ~88% up to 5000 cycles.

In this study, a novel nanohybrid composite containing nitrogen-doped multiwalled carbon nanotubes/carboxymethylcellulose (N-MWCNT/CMC) was synthesized for supercapacitor applications. The synthesized composite materials were subjected to an ultrasonication-mediated solvothermal hydrothermal reaction. The synthesized nanohybrid composite electrode material was characterized using analytical methods to confirm its structure and morphology. The electrochemical properties of the composite electrode were investigated using cyclic voltammetry (CV), galvanic charge–discharge, and electrochemical impedance spectroscopy (EIS) using a 3 M KOH electrolyte. The fabricated composite material exhibited unique electrochemical properties by delivering a maximum specific capacitance of approximately 274 F g −1 at a current density of 2 A g −1 . The composite electrode displayed high cycling stability of 96% after 4000 cycles at 2 A g −1 , indicating that it is favorable for supercapacitor applications.

In prognostics and health management (PHM), the majority of fault detection and diagnosis is performed by adopting segregated methodology, where electrical faults are detected using motor current signature analysis (MCSA), while mechanical faults are detected using vibration, acoustic emission, or ferrography analysis. This leads to more complicated methods for overall fault detection and diagnosis. Additionally, the involvement of several types of data makes system management difficult, thus increasing computational cost in real-time. Aiming to resolve that, this work proposes the use of the embedded electrical current signals of the control unit (MCSA) as an approach to detect and diagnose mechanical faults. The proposed fault detection and diagnosis method use the discrete wavelet transform (DWT) to analyze the electric motor current signals in the time-frequency domain. The technique decomposes current signals into wavelets, and extracts distinguishing features to perform machine learning (ML) based classification. To achieve an acceptable level of classification accuracy for ML-based classifiers, this work extends to presenting a methodology to extract, select, and infuse several types of features from the decomposed wavelets of the original current signals, based on wavelet characteristics and statistical analysis. The mechanical faults under study are related to the rotate vector (RV) reducer mechanically coupled to electric motors of the industrial robot Hyundai Robot YS080 developed by Hyundai Robotics Co. The proposed approach was implemented in real-time and showed satisfying results in fault detection and diagnosis for the RV reducer, with a classification accuracy of 96.7%.

Abstract Recently, prognostic and health management (PHM) has become a prominent field in modern industry. The rotate vector (RV) reducer is one of the widely used mechanical components in industrial systems, specifically in robots. The RV reducer is known for its unique characteristics of small size, efficient speed transmission, and high torsion. The RV reducer is prone to several kinds of faults, due to its continuous operation in an industrial robot. To keep the operation smooth and steady, timely PHM of the RV reducer has become essential. Previously, the RV reducer fault was diagnosed via various techniques, such as ferrography analysis, vibration analysis, and acoustic emission analysis. However, these conventional techniques have various issues. To resolve those issues, we introduce a novel approach to use the embedded electrical current system for the fault detection of the RV reducer. However, this is quite complicated to investigate mechanical fault using an electrical current signature, since the RV reducer is not an integral part of the electric motor, and finding a fault pattern in faulty components needs thorough examination. We therefore focus on the application of machine learning (ML) for fault classifications. We present an approach for feature extraction, feature selection, and feature reduction using the information obtained from the motor current signature analysis to create an ML-based fault classification system with distinguishable prominent features. Finally, the authenticity of the presented approach is justified via the improved values of evaluating parameters, such as accuracy, specificity, and sensitivity, for ML classifiers. Graphical Abstract Graphical Abstract

The mobility applications of laminated composites are constantly expanding due to their improved mechanical properties and superior strength-to-weight ratio. Such advancements directly contribute to a significant reduction in energy consumption in mobile applications. However, the orthotropic nature of these materials results in complex failure modes that require advanced damage detection techniques to prevent catastrophic failures. Therefore, various non-destructive evaluation techniques for structural health monitoring (SHM) of laminated composites are constantly being developed. Moreover, due to the latest advancements in intelligent computational methods, such as machine learning and deep learning, more reliable inspections can be performed. This review discusses current advances in SHM of composite laminates for safety–critical mobility applications such as aerospace, automobile, and marine. A comprehensive overview of the steps involved in SHM of mobility composite structures, such as sensing systems and intelligent computational methods, is presented. Additionally, the review discusses the procedure for developing these intelligent computational methods. The article also describes various public-domain datasets that readers can utilize to create novel, intelligent computational methods. Finally, potential research directions are highlighted that will enable researchers and practitioners to develop more accurate and efficient damage monitoring systems for mobility composite structures.