Smart Monitoring Research Articles

Smart Monitoring Method for Land-Based Sources of Marine Outfalls Based on an Improved YOLOv8 Model

Land-based sources of marine outfalls are a major source of marine pollution. The monitoring of land-based sources of marine outfalls is an important means for marine environmental protection and governance. Traditional on-site manual monitoring methods are inefficient, expensive, and constrained by geographic conditions. Satellite remote sensing spectral analysis methods can only identify pollutant plumes and are affected by discharge timing and cloud/fog interference. Therefore, we propose a smart monitoring method for land-based sources of marine outfalls based on an improved YOLOv8 model, using unmanned aerial vehicles (UAVs). This method can accurately identify and classify marine outfalls, offering high practical application value. Inspired by the sparse sampling method in compressed sensing, we incorporated a multi-scale dilated attention mechanism into the model and integrated dynamic snake convolutions into the C2f module. This approach enhanced the model’s detection capability for occluded and complex-feature targets while constraining the increase in computational load. Additionally, we proposed a new loss calculation method by combining Inner-IoU (Intersection over Union) and MPDIoU (IoU with Minimum Points Distance), which further improved the model’s regression speed and its ability to predict multi-scale targets. The final experimental results show that the improved model achieved an mAP50 (mean Average Precision at 50) of 87.0%, representing a 3.4% increase from the original model, effectively enabling the smart monitoring of land-based marine discharge outlets.

Industry 5.0 for Society 5.0 a new Transformation

Industry 5.0 represents a transformative approach within Society 5.0, leveraging advanced technologies like AI, IoT, Robotics, and Blockchain to enhance productivity, automation, and decision-making across various sectors. This abstract examines how privacy, security, and ethical issues are addressed by modern technologies to create a societal structure that is morally and humanely sound. IoT improves smart city infrastructure and environmental monitoring, while AI enables tailored education and predictive healthcare. Blockchain guarantees safe and transparent data transfers, while robotics helps with accurate healthcare operations. Collectively, these technologies support Society 5.0's sustainability, inclusivity, and resilience. They also hold out hope for a time when technological breakthroughs will preserve strict ethical standards and cybersecurity safeguards while prioritizing the needs of people and society.

Machine learning techniques for the smart faults detection and diagnosis of centrifugal compressor

In this work, we have conducted a comparative study among several machine learning techniques with the aim of selecting the best one for classifying faults affecting the compressor system to enable smart monitoring. This study encompasses various machine learning techniques, including Support Vector Machine, k-nearest neighbor, Decision Tree, Naive Bayes, AdaBoost, and Bag ensembles. To determine the optimal classification technique, we applied three distinct criteria: the confusion matrix, error histogram, and mean square error through cross-validation. Based on these criteria, the results indicate a tie for the top position between two classification models: Decision Tree and Bag ensemble. To solidify our choice of a single model, we employed the new AutoML technique to automatically identify the most suitable machine learning classification model for our case study. We evaluated this approach using process data obtained from an operational industrial centrifugal compressor. Consequently, the results presented in this work affirm that Decision Tree is the superior technique for classifying faults in the 3MCL compressor.

The Development of Machine Learning Models for Radial Compressor Monitoring With Instability Detection

Abstract Modern radial compressors are designed to operate with high performance and a wide range of applications while minimizing their environmental impact. It is necessary to study the machine near the stability limit and gain a comprehensive understanding of the fluid dynamic mechanisms that trigger the instability in the system to extend the operating range at high efficiency. Machine learning aids in developing pattern identification models for detecting compressor instability. In a prior study, a two-stage radial compressor for refrigerant gas underwent extensive simulation, capturing unsteady RANS conditions and generating a substantial dataset of pressure signals from multiple probes. Selected signals, coupled with detailed computational fluid dynamics (CFD) post-processing, revealed fluid dynamic structures near surge conditions. This study utilizes all pressure signals to assess the stage's operational state (stable, transient, or unstable/surge). An additional goal is to develop an algorithm predicting flow patterns in different stage sections with minimal pressure signals at the rotor inlet. This is a preliminary step toward the development of a smart monitoring and diagnostic model for the compressor installed in a plant. The developed model consists of three submodels, trained with CFD results obtained from unsteady Reynolds averaged Navier–Stokes (URANS) simulations. The three submodels are a regression submodule, a classification submodule, and a forecasting algorithm. The regression submodule predicts diffuser static pressure fields, the classification submodule categorizes whether the compressor is in a critical condition or not, and the forecasting algorithm predicts the inducer pressure signals in the future impeller rotations according to the actual operation history. These sub-modules work together to forecast whether the compressor, according to the operation strategy, is going into instability conditions.

Autonomous Intelligent Monitoring of Photovoltaic Systems: An In‐Depth Multidisciplinary Review

ABSTRACTThis study presents a comprehensive multidisciplinary review of autonomous monitoring and analysis of large‐scale photovoltaic (PV) power plants using enabling technologies, namely artificial intelligence (AI), machine learning (ML), deep learning (DL), internet of things (IoT), unmanned aerial vehicle (UAV), and big data analytics (BDA), aiming to automate the entire condition monitoring procedures of PV systems. Autonomous monitoring and analysis is a novel concept for integrating various techniques, devices, systems, and platforms to further enhance the accuracy of PV monitoring, thereby improving the performance, reliability, and service life of PV systems. This review article covers current trends, recent research paths and developments, and future perspectives of autonomous monitoring and analysis for PV power plants. Additionally, this study identifies the main barriers and research routes for the autonomous and smart condition monitoring of PV systems, to address the current and future challenges of enabling the PV terawatt (TW) transition. The holistic review of the literature shows that the field of autonomous monitoring and analysis of PV plants is rapidly growing and is capable to significantly improve the efficiency and reliability of PV systems. It can also have significant benefits for PV plant operators and maintenance staff, such as reducing the downtime and the need for human operators in maintenance tasks, as well as increasing the generated energy.

Smart Agriculture Crop Management Warehouse

In a developing country like India, agriculture is one the most important sectors in terms of financial gain and providing healthy foods to survive. Agriculture is the primary occupation in our country for ages and most of the population depend on it. Now-a-days farmers are facing huge losses due to some storage requirements, while storing agricultural products. A Farmer should have knowledge about the condition of their agricultural crops. Farmers are keeping their agricultural crops in warehouses to increase the lifetime of food grains. Food plays a significant vital role in life, once it involves food security that is littered with each food loss and food wastages. Reports suggests that almost fifty proportion of the food made world-wide doesn't even reach the individuals as they're wasted throughout harvest, transportation, or storage. One in all the point that threats to the farmers is that the loss throughout storage of crops in granaries and warehouses. If crop losses are reduced, it'll mechanically increase the number of food accessibility. To solve this problem one solution is that this project includes smart warehouse management and monitoring farming field, which includes temperature maintenance, humidity maintenance, fire alarms, stock measurement, Pest control. To assist and help the farmers who protects the crops by storing in warehouse, a fully automated enabled observation system is planned to be deployed in remote areas wherever the accessibility is incredibly minimum for farmers with smart storage facilities to scale back food losses and increase food safety. Keywords: Agricultural crops monitoring and controlling, fully automated technology, Sensors, Warehouse management system.

Smart Monitoring and Control System for Crude Oil Pipeline Vandalism

Crude oil pipeline vandalism, theft, and unauthorized interference present a significant challenge to the oil industry's security, profitability, and sustainability. Despite initial measures such as security patrols, traditional methods have proven inadequate, particularly in difficult terrains and aquatic regions. These methods often fail to promptly detect and prevent vandalism and theft, which results in significant economic, environmental, and safety risks. This study developed and implemented a smart monitoring and control system to address these challenges by improving the detection of oil pipeline vandalism, including incidents in aquatic environments. The Prototyping methodology was adopted, allowing for continuous testing and improvements throughout the development process. The system was built using C#.NET under the .NET Framework, with Microsoft SQL Server as the database, incorporating an encryption layer for database security. To enhance its effectiveness, an oil theft detection algorithm was integrated with a web-based interface to facilitate real-time monitoring, visualization, and reporting. Testing scenarios demonstrated that the system successfully detected threats and ensured seamless data flow from sensors to the central system. This solution marks a significant advancement in the security and sustainability of oil infrastructure, contributing to environmental protection and the safety of stakeholders

A Comprehensive Review of Sensor-Based Smart Building Monitoring and Data Gathering Techniques

In an era where buildings are increasingly becoming multifaceted entities, the paradigm of smart buildings has witnessed significant evolution. This advancement integrates sophisticated communication technologies, the Internet of Things (IoT), artificial intelligence (AI), and data analytics. Intending to design an effective smart building monitoring system, this research paper explores and compares various solutions for measuring building parameters by identifying a broad spectrum of review articles considering building occupant behavior, sensor deployment, and implementation complexity. The objective of our paper is to compile diverse information on various sensors used for monitoring building conditions and provide a comprehensive overview of data structuring and processing, all within a single article. Additionally, this paper addresses the challenges of combining data from decentralized systems and the need for managerial tools to optimize user experiences. The findings contribute to the advancement of smart building management, offering valuable insights for improving building performance and user experience as well as evaluating future research directions in this field. This review is designed to serve as an introduction for anyone venturing into the field of building monitoring.

Information-Centric IoT-Based Smart Home Control and Monitoring System

Information-Centric IoT-Based Smart Home Control and Monitoring System

Iontophoresis and electroporation-assisted microneedles: advancements and therapeutic potentials in transdermal drug delivery.

Transdermal drug delivery (TDD) using electrically assisted microneedle (MN) systems has emerged as a promising alternative to traditional drug administration routes. This review explores recent advancements in this technology across various therapeutic applications. Integrating iontophoresis (IP) and electroporation (EP) with MN technology has shown significant potential in improving treatment outcomes for various conditions. Studies demonstrate their effectiveness in enhancing vaccine and DNA delivery, improving diabetes management, and increasing efficacy in dermatological applications. The technology has also exhibited promise in delivering nonsteroidal anti-inflammatory drugs (NSAIDs), treating multiple sclerosis, and advancing obesity and cancer therapy. These systems offer improved drug permeation, targeted delivery, and enhanced therapeutic effects. While challenges remain, including safety concerns and technological limitations, ongoing research focuses on optimizing these systems for broader clinical applications. The future of electrically assisted MN technologies in TDD appears promising, with potential advancements in personalized medicine, smart monitoring systems, and expanded therapeutic applications.

A smart, multi-configuration, and low-cost system for water turbidity monitoring

Measuring the amount of suspended sediment in rivers is important for a better understanding of phenomena like soil erosion or floods. Since such phenomena are not spatially homogeneous, a distributed measurement approach is required to obtain fine-grained and realistic models. We designed and implemented a low-cost, flexible, and smart suspended sediment concentration monitor. The system only uses components that are easy to find and assemble. This, together with the reduced costs, enables the use of multiple instances for distributed monitoring. The system can operate according to different configurations, fixed or mobile, and using a variety of connection technologies, to possibly cover the various necessities that arise from the diverse installation sites. Each measuring unit automatically retrieves its operational parameters, easing the configuration of a possibly large distributed measurement system.

A hierarchical multistage holistic model for acoustic emission source monitoring in composites

Abstract This paper introduces a multistage smart structural health monitoring (SHM) model for carbon-fiber composites, with a focus on multiple types of acoustic emission (AE) source localization and classification. The SHM model uses time-frequency data from various AE events (such as tool drops, impact, and artificial debonding) across different zones of a composite structure. The SHM strategy demonstrates a robust smart monitoring of composites with high accuracy. Further, a hypothesis testing has been carried out that supports the superiority of a 2-stage identification process, revealing statistically significant higher accuracy and confidence intervals across all zones and AE source types. This research establishes a novel framework for solving a hierarchical multistage holistic damage source identification problem, offering robustness in identifying various damage scenarios and quantifying associated prediction uncertainties.

The next-generation of metaverse embodiment interaction devices: A self-powered sensing smart monitoring system

Wearable electronic devices collect user data to provide personalized experiences and real time interactions for the metaverse, thereby driving its development. This paper introduces an innovative self-powered sensing smart monitoring system (SSSMS) designed to harvest human low-frequency inertial energy and achieve gait recognition and fall monitoring (GR-FM) capabilities. The SSSMS enhances energy harvesting efficiency through Halbach electromagnetic enhancement mechanisms, achieving self-sustainability. Additionally, the conical roller triboelectric nanosensor (CR-TENS) accurately captures various human gaits, transmitting these states in the form of electrical signals to a computer via OpenBCI in real time. These signals are integrated with a deep learning-driven diagnostic module to achieve GR-FM. The experimental results demonstrate that under optimal external excitation, the output power is 2.27 mW, with a power density of 32 W/m3. After 100,000 cycles, both the EMG and CR-TENS modules still maintain good performance, sufficiently providing stable and continuous charging for the battery and offering a stable signal for signal processing. Both the EMG and CR-TENS modules exhibit robust feature capture capabilities, effectively sensing different excitation frequencies, amplitudes, and gait patterns. The SSSMS based on the GRU deep learning model achieves recognition accuracies of 96.13 %, 96.60 %, and 92.22 % for frequency, amplitude, and gait, respectively. Deployment experiments demonstrate high accuracy and sensitivity in frequency, amplitude, gait recognition, and fall monitoring, highlighting the advantageous design of EMG and CR-TENS for perceiving subtle motion state changes. The SSSMS demonstrates outstanding capabilities in self-sustainability and real time user embodiment information capture, showcasing its potential as a next-generation interaction device for the metaverse.

Combining Artificial Neural Networks and Mathematical Models for Unbalance Estimation in a Rotating System under the Nonlinear Journal Bearing Approach

Rotating systems are essential components and play a critical role in many industrial sectors. Unbalance is a very common and serious fault that can cause machine downtime, unplanned maintenance, and potential damage to vital rotating machines. Accurately estimating unbalance in rotor–bearing systems is crucial for ensuring the reliable and efficient operation of machinery. This research paper presents a novel approach utilizing artificial neural networks (ANNs) to estimate the unbalance masses in a multidisk system based on simulation data from a nonlinear rotor–bearing system. Additionally, this study explores the effect of various operating parameters on oil film stability and vibration response through a combination of bifurcation diagrams, spectrum cascades, Poincare maps, and orbit and FFT plots. This study demonstrates the effectiveness of ANNs for unbalance estimation in a fast and accurate way and discusses the potential of ANNs in smart online condition monitoring systems.

Self-assembled conductive coating and bifacial microstructures Collaboratively Adjusting the sensing performance and high reliability of the flexible pressure sensors

Flexible pressure sensors are being studied intensively for applications such as smart wearables and health monitoring. While ensuring the excellent sensing performance, the reliability (durability and environmental resistance) of the sensors are also key indicators that determine whether they can be used in practice. In this research, a strategy to adjust the sensing performance and high reliability of the flexible pressure sensors by self-assembled conductive coating and bifacial microstructures (BM) is proposed. Polydimethylsiloxane/carbon nanotube films with BM are first modified to reduce the surface contact angle. Then poly(dimethyl diallyl ammonium chloride) (PDAC) and hydroxylated carbon nanotubes/carboxylated cellulose nanofibers/glycerol (CNT-OH/CNF-C/GL) are successively assembled on the BM surface to construct conductive coatings (PCCG). The sensor based on PCCG and BM (the PCCG-BM sensor) shows a high sensitivity (9.97 kPa−1), a wide detection range (0–330 kPa), and a fast response time (59 ms). The PCCG-BM sensor has excellent repeatability, and it still outputs stable current change signals after 240,000 pressure cycles. What’s more, the PCCG-BM sensor is treated with extreme environments such as bending, stretching, washing, and high pressure, and the sensor still maintains excellent performances. This preparation strategy provides a new perspective for the fabrication of high-performance and high-reliability flexible sensors.

An Invisible Dermal Nanotattoo-Based Smart Wearable Sensor for eDiagnostics of Jaundice.

Despite substantial progress in the diagnosis of jaundice/hyperbilirubinemia as the most common disease and cause of hospitalization of newborns, on the eve of Industry/Healthcare 5.0, the development of accurate and reliable wearable diagnostic sensors for noninvasive smart monitoring of bilirubin (BIL) is still in high demand. Aiming to fabricate a smart wearable sensor for early diagnosis of neonatal jaundice and its therapeutic monitoring, we here report a fluorescent dermal nanotattoo that further coupled with an IoT-integrated wearable optoelectronic reader for minimally invasive, continuous, and real-time monitoring of BIL in interstitial fluid. Selective recovery of quenched fluorescence of the dermal tattoo sensor, composed of biocompatible dissolving/hydrogel microneedles loaded with fluorescent carbon quantum dots, upon blue light exposure used for jaundice phototherapy was utilized for highly selective BIL sensing. The fascinating features of our developed smart wearable tattoo sensor and its successful results with high correlation with blood BIL results make it a highly promising sensor for easy, minimally invasive, reliable, and smart eDiagnostics and continuous therapeutic eMonitoring of jaundice and other BIL-induced diseases at the point of care. We envision that the developed nanotattoo sensing bioplatform will inspire the development of future smart tattoo sensors in various diagnostic and monitoring scenarios.

Clinical Validation of a Prototype Smart Non-Invasive Pregnancy Glucose Monitor

The aim of this study is to evaluate the effectiveness of a smart non-invasive blood glucose monitor prototype during pregnancy through clinical validation. The monitor utilizes near-infrared spectroscopy combined with AI big data analysis of photoelectric volumetric pulse wave data to achieve non-invasive monitoring of blood glucose in women during pregnancy. The research team developed a monitor that employs a sensing chip, effectively overcoming the problems of weak signals and individual differences in non-invasive blood glucose monitoring. The user experience is enhanced by visualizing the test results on the accompanying cell phone APP (application) of the smart non-invasive pregnancy blood glucose monitor. Clinical validation revealed that the non-invasive monitoring data for pregnant women aged 20~30 years significantly differed from those obtained via traditional blood glucose measurement methods, whereas no significant difference ( P<0.05) was observed for pregnant women aged 31~42 years. The study concluded that further calibration of the monitor and an expansion of the sample size are necessary to enhance consistency with invasive glucose monitoring results.

Smart Monitoring of Microgrid-Integrated Renewable-Energy-Powered Electric Vehicle Charging Stations Using Synchrophasor Technology

With the growing concern over climate change and energy security, the Government of India expedited enhancing the share of renewable energy (RE) derived from solar, wind and biomass sources within the energy blend. In this paper, a techno-economic and environmental analysis of a microgrid-integrated electric vehicle charging stations fueled by renewable energy is proposed for a typical area in the State of Karnataka, South India. The power transaction with the grid and the sell-back price to the national grid were investigated. Carbon emissions were also assessed, and 128,406 CO2 kg/Yr can be saved in the grid-connected mode. Also, in this work, different scenarios such as injecting active power, reactive power, and active and reactive power, and injecting active and absorbing reactive power to the grid are comprehensively assessed. Out of four types, type 3 (inject real and reactive power) provides significant reduction in power losses by up to 80.99%. The synchrophasor-technology-based monitoring method is adopted in order to enhance the microgrid system’s overall performance. The execution times for different cases with distributed generators (DGs) and electric vehicle charging stations (EVCSs) for conventional systems and micro-phasor measurement units (µPMU) were observed to be 19.07 s and 5.64 s, respectively, which is well accepted in the case of online monitoring.

Revealing the Impact of Composition on Oil Monitoring Performance of Ni-Ti-Cu Coated Optical Fiber

Shape Memory Alloy-coated optical fiber provides an economical, Smart, and real-time monitoring technological solution for Industry 4.0. In this work, the compositional influence of Ni-Ti-Cu coating on optical signal actuation has been studied. Morphological studies showed improvement in the grain size, surface roughness, and a better shape memory effect. Thermal characteristics revealed an increase in the phase transformation temperature of NiTiCu with higher copper content. Experimental analysis of optical fiber sensors in Mineral Oil resulted in a gradual shift in the actuation temperature range. A sensitivity of ∼52 mV/°C was recorded in the actuation window, showcasing the improved response time for copper-rich composition. A comparison of coated-uncoated optical fiber performance in oil has also been presented, and delamination studies were conducted by continuously exposing the samples to hot oil. The work can be referred for requirement-based composition selection for real-time and smart monitoring capability in the Oil industries.

Advancements and Best Practices in Hard Disk Drives (HDDs) Repair: A Comprehensive Review of Diagnostic Techniques, Repair Methods, and Preventive Strategies

Hard Disk Drives (HDDs) remain a crucial component of data storage systems despite the advent of Solid State Drives (SSDs). However, the mechanical and electronic nature of HDDs makes them susceptible to various failures over time. This article provides a comprehensive examination of HDD repair, focusing on the anatomy and function of these devices, common causes of failure, diagnostic techniques, and effective repair methods. Mechanical failures, such as head crashes and spindle motor issues, electronic failures like PCB damage, and logical failures including file system corruption and bad sectors are explored in detail. Diagnostic approaches utilizing visual inspection, diagnostic software tools, hardware-based diagnostics, and SMART monitoring are discussed to aid in identifying issues accurately. Repair methods covered include data recovery techniques, mechanical repairs, electronic component replacement, and firmware fixes. Real-world case studies illustrate practical challenges and solutions in HDD repair, highlighting the complexities involved. Additionally, best practices and safety measures for handling and maintaining HDDs are provided to prevent future failures and ensure data integrity. This article aims to equip data recovery professionals, IT technicians, and tech enthusiasts with the knowledge and skills necessary to diagnose and repair HDD issues effectively. By integrating technical insights and practical approaches, it serves as a valuable resource for enhancing the reliability and longevity of HDDs in various applications.