Monitoring System For Detection Research Articles

Algorithms and Resources for the Monitoring of Very-Low-Frequency Signal Deviations Due to Solar Activity Using a Web-Based Software-Defined Radio-Distributed Network

This work presents the development and testing of an experimental web-based SDR (software-defined radio) monitoring system for indirect solar activity detection, which has the ability to estimate and potentially predict various events in space and on earth, including solar flares, coronal mass ejections, and geomagnetic storms. The proposed system can be used to investigate the effect of solar activity on the propagation of very-low-frequency (VLF) signals. The advantages and benefits of the given approach are as follows: increasing measurement accuracy and eventual solar activity identification by combining measurements from multiple spatially distributed SDRs. The verification process involves carrying out several experiments comparing data from the GOES satellite system and the Dunksin SuperSID system with information received by the SDR monitoring system. Then, utilizing Pearson correlation coefficients, the measured data from the SDRs, along with those from the GOES satellite system and the Dunsing monitoring station, are investigated. At the time of a solar flare, the correlation value is above 90% for most of the stations used. Combining the signal-to-noise ratio via summation also shows an improvement in the results, with a correlation above 98%.

Driver Distraction Detection and Alert System Using Convolutional Neural Networks

Abstract: The Driver Distraction and Alert System Using Convolutional Neural Networks provide a real-time machine learning and webcam-based monitoring system for driver distraction detection. The system uses a sophisticated Convolutional Neural Network (CNN) architecture trained on the State Form of Distracted Driver Detection dataset, which is made possible by utilizing the OpenCV, TensorFlow, and Pandas packages. With improvements like dropout layers and Xavier weight initialization, the model efficiently classifies ten driver attention states through preprocessing stages like image scaling and normalization. The results of the experiment show how well the suggested method works to identify distracted driving behaviors; a revised model on the Kaggle competition platform achieved a competitive public score of 2.67118

Online boxplot derived outlier detection

AbstractOutlier detection is a widely used technique for identifying anomalous or exceptional events across various contexts. It has proven to be valuable in applications like fault detection, fraud detection, and real-time monitoring systems. Detecting outliers in real time is crucial in several industries, such as financial fraud detection and quality control in manufacturing processes. In the context of big data, the amount of data generated is enormous, and traditional batch mode methods are not practical since the entire dataset is not available. The limited computational resources further compound this issue. Boxplot is a widely used batch mode algorithm for outlier detection that involves several derivations. However, the lack of an incremental closed form for statistical calculations during boxplot construction poses considerable challenges for its application within the realm of big data. We propose an incremental/online version of the boxplot algorithm to address these challenges. Our proposed algorithm is based on an approximation approach that involves numerical integration of the histogram and calculation of the cumulative distribution function. This approach is independent of the dataset’s distribution, making it effective for all types of distributions, whether skewed or not. To assess the efficacy of the proposed algorithm, we conducted tests using simulated datasets featuring varying degrees of skewness. Additionally, we applied the algorithm to a real-world dataset concerning software fault detection, which posed a considerable challenge. The experimental results underscored the robust performance of our proposed algorithm, highlighting its efficacy comparable to batch mode methods that access the entire dataset. Our online boxplot method, leveraging dataset distribution to define whiskers, consistently achieved exceptional outlier detection results. Notably, our algorithm demonstrated computational efficiency, maintaining constant memory usage with minimal hyperparameter tuning.

An ultrafast and robust structural damage identification framework enabled by an optimized extreme learning machine

Artificial intelligence (AI) has recently been implemented in structural health monitoring (SHM) systems for damage detection and identification. However, existing AI methods often involve a high number of parameters, resulting in the laborious workload during model training and implementation. In this study, we develop a lightweight damage identification framework embedded with an optimized extreme learning machine (ELM) using a significantly reduced number of parameters from frequency features of the structural vibrational signals. Coupled with the ensemble empirical mode decomposition (EEMD), the frequency features were abstracted from the structural vibrational signals in the data pre-processing step. Then, a metaheuristic algorithm (chaos game optimization, CGO) was chosen to optimize the model weight of the ELM to guarantee damage identification accuracy. Once the model is trained, the structural damages can be precisely identified despite the noise-contaminated data. We validated the efficiency and accuracy of the proposed framework with a public database and compare its performance with several common metaheuristic algorithms. With the proven capability, the proposed framework shows a promising future as a handy and reliable AI-assisted digital tool for robust development in next-generation structural monitoring systems.

Research on fault detection and remote monitoring system of variable speed constant frequency wind turbine based on Internet of Things

In order to study the operating characteristics of variable speed constant frequency wind turbine under different working conditions and the monitoring system of wind turbine. In this paper, the simulation model of each component system of wind turbine is established by MATLAB/Simulink module, and the influence law of different wind speed and ground fault types on the output power of wind turbine is studied. The active power of wind turbines under different short-circuit fault types is compared. At the same time, in order to realize real-time monitoring of wind turbine speed and output power, an online monitoring system for wind turbine operation based on industrial Internet of Things is proposed, and the composition and operation characteristics of this remote monitoring system are given. The practical application shows that the on-line monitoring system can accurately and remotely monitor the running status of the wind turbine and avoid the unstable running of the wind turbine. The research conclusions of this paper can provide reference for the design and construction of wind turbines and the operation of connecting to the power grid.

AI-Powered Disease Detection and Growth Monitoring System Using IoT for Improved Tomato Yield

Abstract: This project introduces an advanced AI- powered disease detection and growth monitoring system designed specifically for enhancing tomato yield. With Internet of Things (IoT) technology, the system integrates Raspberry Pi as the central processing unit with temperature and humidity sensors for environmental monitoring. AI algorithms are used to predict tomato ripening stages and detect diseases, to ensure proactive management of plant health. If a disease is identified, the system provides the solutions to control its impact, thereby enhancing yield and quality. The captured data is analysed and results are displayed on a user-friendly webpage accessible to farmers, enabling informed decision-making and improved agricultural practices. This innovative approach combines technology with agricultural expertise, offering a solution to the challenges faced by tomato farmers and contributing to sustainable tomato production. Keywords: Image Recognition, Disease Detection

Centralized Monitoring System for Street Light and Fault Detection

Centralized Monitoring System for Street Light and Fault Detection

In-Home, Smart Sleep Monitoring System for Cardiorespiratory Estimation and Sleep Apnea Detection: Proof of Concept

In-Home, Smart Sleep Monitoring System for Cardiorespiratory Estimation and Sleep Apnea Detection: Proof of Concept

Centrifugal Pump Fault Detection with Convolutional Neural Network Transfer Learning.

The centrifugal pump is the workhorse of many industrial and domestic applications, such as water supply, wastewater treatment and heating. While modern pumps are reliable, their unexpected failures may jeopardise safety or lead to significant financial losses. Consequently, there is a strong demand for early fault diagnosis, detection and predictive monitoring systems. Most prior work on machine learning-based centrifugal pump fault detection is based on either synthetic data, simulations or data from test rigs in controlled laboratory conditions. In this research, we attempted to detect centrifugal pump faults using data collected from real operational pumps deployed in various places in collaboration with a specialist pump engineering company. The detection was done by the binary classification of visual features of DQ/Concordia patterns with residual networks. Besides using a real dataset, this study employed transfer learning from the image detection domain to systematically solve a real-life problem in the engineering domain. By feeding DQ image data into a popular and high-performance residual network (e.g., ResNet-34), the proposed approach achieved up to 85.51% classification accuracy.

SHM Implementation on a RPV Airplane Model Based on Machine Learning for Impact Detection

AbstractIn this study, an on-working structural health monitoring system for impact detection on remote piloted vehicle (RPV) airplane is proposed. The approach is based on the propagation of Lamb waves in metallic structures on which Pb[ZrxTi1−x]O3 (PZT) sensors are bonded for receiving vibrational signals due to impact events. The proposed method can be used to detect impacts in aerospace structures, i.e. skin fuselage and/or wing panels. After the detection, machine learning (ML) algorithms (polynomial regression and neural networks) are applied for processing the acquired ultrasounds waves in order to characterise the impacts, in terms of time of flight (ToF) and relative location. Several test cases are studied: the ML models are tested both without external noise (in laboratory) and introducing external RC engine vibration (on-working conditions). Furthermore, this work presents the implementation of a mini-equipment for acquisition and data processing based on Raspberry Pi. A good agreement between laboratory and in-flight results is achieved, in terms of distance between the actual and calculated impact location.

The Use of Products with a Monitoring System for Remote Bee Detection in Beekeeping in Czechia

The use of modern technology is becoming part of both industry and agriculture. These technologies can also be used in beekeeping, where they can help to monitor the operation of the hive remotely. Beekeepers can remotely monitor the weight of their hives, their temperature, humidity, and other parameters. The aim of this paper is to map the beekeepers in the use of products with monitoring system for remote bee detection in beekeeping in Czechia. To map the issue, qualitative research using semi-structured interviews was conducted with beekeepers, manufacturers/providers of smart devices in beekeeping, and other entities involved in beekeeping. The findings showed that the interest of manufacturers and sellers to offer these smart devices is significant, but the interest of beekeepers is rather less, due to e.g., the purchase price, weaker IT knowledge, traditional beekeeping practices, higher age of beekeepers and the joy of being personally with bees. The novelty of the paper is not to look at the provision of ICT in beekeeping from a technical perspective, but from the perspective of users (beekeepers) and manufacturers of these technologies. Through interviews with beekeepers as well as others in the apiculture sphere, a comprehensive view of the issue is developed. Moreover, this is the first piece of research on this area in Czechia.

IoT based weather monitoring system for temperature, humidity, rainfall and seismic detection in real time

This paper presents the design and implementation of an IoT-based weather monitoring system enhanced with seismic detection capabilities, employing Node MCU microcontroller and the Blynk mobile application platform. The system incorporates various sensors including a rain sensor, Light light-dependent resistor (LDR), a DHT11 sensor for temperature and humidity measurement, and an accelerometer (MPU6050) for earthquake detection. The hardware components are interconnected with the Node MCU microcontroller, facilitating real-time data acquisition from the sensors. Programming of the Node MCU is conducted using the Arduino IDE, enabling data transmission to the Blynk app via Wi-Fi connectivity. The Blynk app serves as the user interface, allowing seamless visualization and monitoring of weather parameters and seismic activity on smartphones. Key functionalities of the system include periodic data collection from sensors, transmission of sensor data to the Blynk server, and updating of graphical representations in the Blynk app interface. The accelerometer sensor is utilized to detect changes in acceleration, indicative of seismic disturbances. Threshold values are defined to trigger alerts within the Blynk app in the event of potential seismic activity. The implementation undergoes rigorous testing to ensure accuracy, reliability, and responsiveness under diverse environmental conditions. Considerations such as network stability, power efficiency, and data security are addressed to enhance system performance and longevity. The system demonstrates promising capabilities for real-time weather monitoring and seismic event detection, offering valuable insights for disaster preparedness and environmental analysis. IoT-based weather monitoring system integrated with seismic detection provides an effective solution for comprehensive environmental monitoring, leveraging the power of IoT technology and mobile applications for enhanced data accessibility and analysis. Further advancements and optimizations can be explored to extend the functionality and scalability of the system for broader applications in environmental science and disaster management.

An intelligent fault detection and diagnosis monitoring system for reactor operational resilience: Unknown fault detection

Multiple advanced reactor designs envision deployment scenarios that feature reactor operations with significantly reduced operating staff compared to present reactors, or even feature fully autonomous operations. Since many of these new reactor designs, like small modular reactors (SMRs) and microreactors, also include extended fuel cycles which limit inspection intervals, load-following capabilities, and reduced access to critical components, safe and reliable semi- or fully autonomous operations must be enabled through an on-line monitoring (OLM) system that effectively detects and diagnoses any faults in the reactor. This paper presents the development of the Fault Detection Module of the previously proposed intelligent OLM system: the Fault Detection and Diagnosis Monitoring System (FDDMS). The Fault Detection Module monitors various plant parameters from 86 sensors throughout the nuclear power plant and accurately defines operations as nominal or abnormal. The methodology leverages the advantages of unsupervised learning and semi-supervised optimization data-driven techniques to successfully detect any fault in the system in real-time without fault information a priori. This allows the Fault Detection Module to identify any unknown fault that may affect the system. When integrated with the rest of the FDDMS architecture, the Fault Detection Module provides power transient dependent fault detection by using a separate model for steady state, ramping up in power, and ramping down in power operations. This makes the FDDMS especially applicable for load-following reactor systems. The Fault Detection Module architecture features a dimensionality reduction algorithm followed by an anomaly detection method. Three dimensionality reduction techniques: 1) Principal Component Analysis (PCA), 2) Deep Neural Network Autoencoder (DNNAE), and 3) Convolutional Neural Network Autoencoder (CNNAE); and three anomaly detection methods: 1) One-Class Support Vector Machine (OC-SVM), 2) Clustering (DBSCAN), and 3) Reconstruction Error (RecErr) thresholding were compared to find the best model for each power transient dataset. Precision rates, recall rates, F1-scores, and accuracies were used to compare model performance. The best models produced total accuracies of 94.50%, 93.63%, and 91.76% for the steady state, ramp up, and ramp down datasets, respectively, or 99.19%, 98.97%, and 97.09% when omitting 1%-severe fault cases. Specifically, the best performing models for the steady state, ramp up, and ramp down cases were the CNNAE-RecErr, DNNAE-DBSCAN, and CNNAE-RecErr, respectively. Recall rates were calculated for various fault types at multiple severity levels. The results show that the Fault Detection Module can identify any type of fault in the system with a delay of a few seconds on average. Additionally, robustness against noisy sensor data was tested, with models maintaining 100% accuracy at various levels of noise, and an illustrative real-time application of the methodology is provided.

A Hybrid AI Framework for Sensor-Based Personal Health Monitoring towards Precision Health

Non-communicable diseases are on the rise globally, resulting in accelerated efforts to develop personal health monitoring systems for early detection, prediction, and prevention of diseases. This is part of the vision of precision health, an emerging paradigm that focuses on preventing disease before it strikes by encouraging people to actively monitor and work towards improving their health. A key facilitator of this is the use of wearable sensors that can collect and measure physiological data.Although many sensor-based health monitoring systems have been proposed, interoperability of health data and processes, prediction of future health states, and uncertainty management remain open challenges. This research aims to alleviate these challenges through the development of a reusable framework integrating both data-driven and knowledge-driven AI within a hybrid AI architecture.

IoT-Machine Learning-Based Structural Health Monitoring System for Detection of Cracks in Bridges

The deteriorating condition of infrastructure, particularly bridges, poses significant challenges to public safety and necessitates the development of advanced monitoring systems for early detection of structural defects. This paper proposes a novel approach by integrating Internet of Things (IoT) technology with machine learning (ML) algorithms for real-time structural health monitoring, focusing on the detection of cracks in bridges. The proposed system employs a network of IoT sensors strategically deployed on the bridge structure to collect diverse data related to environmental conditions, strain, and vibrations. These sensors provide a continuous stream of data, creating a comprehensive dataset for analysis. Machine learning algorithms, specifically designed for anomaly detection, are applied to this dataset to identify patterns indicative of potential structural issues. This system is designed and implemented using ML and IoT. The excitation results shows that the designed system accuracy and efficiency of crack detection is improved and further enhance overall structural resilience. Furthermore, the integration of a real-time alerting mechanism allows for immediate notification of detected anomalies.

Development and Evaluation of a MQ-5 Sensor-Based Condition Monitoring System for In-Situ Pipeline Leak Detection

The condition monitoring system for an in-situ pipeline is an innovative concept that uses MQ-5 sensors to detect fuel leaks in pipelines and relay concentration data to a receiver station. The essence of the monitoring process is to ensure the safety and security of engineering properties and lives. The project addresses the crucial requirement for rapid detection of fuel leaks to avoid environmental problems, economic losses, and safety risks connected with pipeline circulation. The goal is to create a leak detection monitoring system. The project required the design and execution of four transmitter stations, each responsible for detecting fuel leaks at various places along a small-scale pipeline and transmitting concentration data to a central receiver station. The system's response time, a critical performance parameter for this project, was measured by timing how long it took for an alert to arrive at the receiving station after the transmitter detected a leak. This time was measured at three distances between the transmitter and receiver stations: 1m, 2m, and 3m. Multiple measurements were taken at each distance, and the average response time was computed. The results showed that as the distance between the stations increased, so did the reaction time. The average response time at 1m was 3.37 seconds, whereas, at 2m and 3m, it was 3.856 seconds and 4.198 seconds, respectively. A t-test inference was performed to check that there was a distinct difference between the response times for each distance, and a significant difference was detected. The built system effectively demonstrated its ability to detect fuel leaks, and the observed response times offered useful information about the system's performance at various distances. This technology demonstrates the potential to improve pipeline transportation safety and efficiency by enabling early identification of fuel leaks.

Research on electric field characteristics under different length interface air gap defects in cable terminals of high-speed train

It is vital that accurately and effectively detecting the insulation status of cable terminal to ensure the safe and stable operation of high-speed trains. Accordingly, a novel monitoring system for the internal defect detection of cable terminal was proposed based on electric field strength. Firstly, partial discharge and electric field strength along the surface of the air gap defects cable terminal umbrella skirt were measured and analyzed. The development of internal defects in cable terminals can be divided into three stages: S1, S2, and S3. Then, the interquartile range, variance, average value and dispersion coefficient of electric field strength have been proposed to characterize the development state of air gap defects in cable terminal. The results shows that the internal defects of cable terminals develop from 20%l to 100%l, the slope of the interquartile range of the distribution of magnitudes of the electric field intensity increases from 0.126 to 0.671, and the absolute value of variance and offset increase by about 81.3% and 80.66%, respectively. Therefore, it is feasible and effective to detect the development state of air gap defects inside the cable terminal by measuring and analyzing the electric field strength characteristics along the surface of the cable terminal umbrella skirt.

Development of a 3D tracking system for multiple marmosets under free-moving conditions

Assessment of social interactions and behavioral changes in nonhuman primates is useful for understanding brain function changes during life events and pathogenesis of neurological diseases. The common marmoset (Callithrix jacchus), which lives in a nuclear family like humans, is a useful model, but longitudinal automated behavioral observation of multiple animals has not been achieved. Here, we developed a Full Monitoring and Animal Identification (FulMAI) system for longitudinal detection of three-dimensional (3D) trajectories of each individual in multiple marmosets under free-moving conditions by combining video tracking, Light Detection and Ranging, and deep learning. Using this system, identification of each animal was more than 97% accurate. Location preferences and inter-individual distance could be calculated, and deep learning could detect grooming behavior. The FulMAI system allows us to analyze the natural behavior of individuals in a family over their lifetime and understand how behavior changes due to life events together with other data.

Design and implementation of an automated baby monitoring cradle

AbstractEver increasing global birth rate, increased working women ratio and innovative technologies motivate the engineering society to introduce novel ideas for the baby caring. Considering this, we present hardware implementation of an automatic cradle for smart and remote monitoring. In contrast to previous implementations, we present the design insights such as swing angle, required force and torque, arc length and speed of the cradle for the first time in the literature. Furthermore, to make the cradle more comfortable and smooth swing, we introduce a novel Slider Crank Mechanism to control the cradle swing for the first time in the literature. This provides flexibility, and improved energy efficiency, while maintaining controlled and predictable reciprocating motion. The proposed solution is equipped with a number of technological features, that is, cry detection based swing, collision avoidance, WiFi based remote video monitoring, wet detection and baby health monitoring system.

A Structural Health Monitoring System for Bond Line Flaws Detection on a Full-Scale Wingbox Section Demonstrator

In recent years, there has been a significant increase in the use of structural health monitoring (SHM) technologies as systems for monitoring the integrity of aircraft’s structures. The use of compact and embeddable sensor networks, like the ones based on fibre optics (FO), is particularly attractive from the perspective of releasing an integrated structural system with intrinsic sensing capacity. Usually, an SHM system architecture is completed by a dedicated algorithm that processes the data gathered from the sensors to elaborate on the level of damage currently suffered by the structure, with the further possibility of providing information to the relevant specialists involved with its supervision. One of the main SHM applications that is attracting major interest is related to the inspection and detection of anomalies in bonded joints, which is extremely relevant in many composite realizations. Aeronautical regulations allow the use of bonded joints on an aircraft’s primary structure but require the implementation of a means to ensure their absolute safety, such as the introduction of further mechanical links aimed at stopping the propagation of a possible flaw or the availability of Non-Destructive Inspection (NDI) systems to prove the absence of relevant damaged areas. Generally, the main typical defects occurring during the manufacturing of bonded joints include adhesive curing, kissing bonds, poor porosity, and poor surface preparation. The current NDI systems more widely used and available to detect defects are still inaccurate due to the lack of standard procedures for the creation of representative defects in a controlled manner, which would allow for the development of reliable methodologies and tools able to ensure the safety of a bonded joint, as required by safety regulations. This paper shows the results relative to the implementation of an SHM system developed by the Italian Aerospace Research Centre (CIRA) aimed at monitoring the bonding lines between spar caps and panels of a typical composite wingbox section and detecting faults in location and length. The work was performed during typical ground static tests by using a fibre optical sensing network embedded within relevant adhesive paste layers during the manufacturing process of the structure. In the reported investigation, the SHM system assumed the function of an NDI system tool. The results show that the developed SHM system has good reliability for the detection of both the position and size of damage areas that were artificially inserted within the test article during the bonding phase, showing its potential as a candidate to be used as a tool to verify the conditions of a bonded joint, as required by aviation authorities’ regulations.