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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

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%.

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.

Recently, in-vitro studies of magnetic nanoparticle (MNP) hyperthermia have attracted significant attention because of the severity of this cancer therapy for in-vivo culture. Accurate temperature evaluation is one of the key challenges of MNP hyperthermia. Hence, numerical studies play a crucial role in evaluating the thermal behavior of ferrofluids. As a result, the optimum therapeutic conditions can be achieved. The presented research work aims to develop a comprehensive numerical model that directly correlates the MNP hyperthermia parameters to the thermal response of the in-vitro model using optimization through linear response theory (LRT). For that purpose, the ferrofluid solution is evaluated based on various parameters, and the temperature distribution of the system is estimated in space and time. Consequently, the optimum conditions for the ferrofluid preparation are estimated based on experimental and mathematical findings. The reliability of the presented model is evaluated via the correlation analysis between magnetic and calorimetric methods for the specific loss power (SLP) and intrinsic loss power (ILP) calculations. Besides, the presented numerical model is verified with our experimental setup. In summary, the proposed model offers a novel approach to investigate the thermal diffusion of a non-adiabatic ferrofluid sample intended for MNP hyperthermia in cancer treatment.

Recently, energy harvesting from human motion has attracted substantial research into its ability to replace conventional batteries for smart electronics. Human motion exhibits excellent potential to provide sustainable and clean energy for powering low-powered electronics, such as portable instruments and wearable devices. This review article reports on the piezoelectric, electromagnetic, and triboelectric energy harvesting technologies that can effectively scavenge biomechanical energy from human motion such as, walking, stretching, and human limb movement, as well as from small displacements (e.g., heartbeat, respiration, and muscle movement) inside the human body. Furthermore, various recent designs and configurations of human motion energy harvesters are presented according to their working mechanisms, device compositions, and performances. In order to provide insight into future research prospects, the paper also discusses the limitations, issues, and challenges of piezoelectric, electromagnetic, and triboelectric energy harvesting technologies for the development of smart electronics.

Thermal power plants (TPPs) are critical to supplying energy to society, and ensuring their safe and efficient operation is a top priority. To minimize maintenance shutdowns and costs, modern TPPs have adopted advanced fault detection and diagnosis (FDD) techniques. These FDD approaches can be divided into three main categories: model-based, data-driven-based, and statistical-based methods. Despite the practical limitations of model-based methods, a multitude of data-driven and statistical techniques have been developed to monitor key equipment in TPPs. The main contribution of this paper is a systematic review of advanced FDD methods that addresses a literature gap by providing a comprehensive comparison and analysis of these techniques. The review discusses the most relevant FDD strategies, including model-based, data-driven, and statistical-based approaches, and their applications in enhancing the efficiency and reliability of TPPs. Our review highlights the novel and innovative aspects of these techniques and emphasizes their significance in sustainable energy development and the long-term viability of thermal power generation. This review further explores the recent advancements in intelligent FDD techniques for boilers and turbines in TPPs. It also discusses real-world applications, and analyzes the limitations and challenges of current approaches. The paper highlights the need for further research and development in this field, and outlines potential future directions to improve the safety, efficiency, and reliability of intelligent TPPs. Overall, this review provides valuable insights into the current state-of-the-art in FDD techniques for TPPs, and serves as a guide for future research and development.

Physics-Informed Neural Networks (PINNs) integrate physics principles with machine learning, offering innovative solutions for complex modeling challenges. Laminated composites, characterized by their anisotropic behavior, multi-layered structures, and intricate interlayer interactions, pose significant challenges for traditional computational methods. PINNs address these issues by embedding governing physical laws directly into neural network architectures, enabling efficient and accurate modeling. This review provides a comprehensive overview of PINNs applied to laminated composites, highlighting advanced methodologies such as hybrid PINNs, k-space PINNs, Theory-Constrained PINNs, optimal PINNs, and disjointed PINNs. Key applications, including structural health monitoring (SHM), structural analysis, stress-strain and failure analysis, and multi-scale modeling, are explored to illustrate how PINNs optimize material configurations and enhance structural reliability. Additionally, this review examines the challenges associated with deploying PINNs and identifies future directions to further advance their capabilities. By bridging the gap between classical physics-based models and data-driven techniques, this review advances the understanding of PINN methodologies for laminated composites and underscores their transformative role in addressing modeling complexities and solving real-world problems.

With increasing customer demand, industry 4.0 gained a lot of interest, which is based on smart factories. In smart factories, robotic components are vulnerable to failure due to various industrial operations such as assembly, manufacturing, and product handling. Timely fault detection and diagnosis (FDD) is important to keep the industrial operation smooth. Previously, only the unloaded-based FDD algorithms were considered for the industrial robotic system. In the industrial environment, the robot is working under various working conditions such as speeds, loads, and motions. Hence, to reduce the domain discrepancy between the lab scale and the real working environment, we conducted experimentations under various working conditions. For that purpose, an extensive experimental setup is prepared to perform a series of various experiments mimicking the real environmental condition. In addition, in previous research work, various machine learning (ML) and deep learning (DL) approaches were proposed for robotic arm component fault detection. However, various issues are related to the DL and ML approaches. The ML models are problem-specific, and complex in computations. The DL model needs a huge amount of data. The DL model is composed of various layers that have not been thoroughly explored; as a result, the fault detection model lacks a comprehensive explanation. To overcome these issues, the transfer learning (TL) model is considered with the diverse experimental scenarios. The main contribution is to increase the generalization capabilities of the robotic PHM in the context of previously available research work. For that purpose, the VGG16 model is used because of its autonomous feature extractions for fault classification. The data are collected under a variety of different operating conditions such as loadings, speeds, and motion patterns. The 1D signal is converted to a 2D signal (scalogram) to perform the TL model. The proposed approach shows effective fault detection performance and has the capabilities of generalization under variable working conditions.

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.

Prognostics and health management (PHM) has developed into a crucial discipline because of its never-ending pursuit of safety, effectiveness, and dependability. The aircraft Landing gear (LG) is one of the most significant components during takeoff and landing. Consequently, the PHM of LG is essential for the aircraft to operate safely and reliably. This paper provides an in-depth exploration of the developments, difficulties, and prospects in PHM for aircraft LG. The study begins by providing an overview of the LG parts and related faults, emphasizing their importance for the flight safety. The insights of PHM are presented based on various artificial intelligence (AI) techniques. Various approaches are discussed for fault detection and isolation (FDI) and remaining useful life (RUL). These efforts help to improve the maintenance and decision-making (MDM) process, which improves the overall effectiveness of PHM. With the aim of giving researchers a useful resource, this review addresses to fill the research gaps based on the available literature so far. It lays the foundations for future advancements by highlighting the challenges in this field.