Search Results (385)

This paper introduces a framework to perform operational modal analysis (OMA) for structural health monitoring (SHM) by presenting the development and validation of a low-power, solar-powered wireless sensor network (WSN) tailored for bridge structures. The system integrates accelerometers and temperature sensors for dynamic structural assessment, all interconnected through the energy-efficient message queuing telemetry transport (MQTT) messaging protocol. Moreover, it delves into the details of sensor selection, calibration, and the design considerations necessary to address the unique challenges associated with bridge structures. Special attention is given to the solar-powered aspect, allowing for extended deployment periods without the need for frequent maintenance or battery replacements. To validate the proposed system, a comprehensive field deployment was conducted on an actual bridge structure. The collected data were transmitted through MQTT messages and analyzed by means of OMA. Comparative studies with traditional wired systems underscore the advantages of the solar-powered wireless solution in terms of sustainability, scalability, and ease of deployment. Results from the validation phase demonstrate the system’s capability to provide accurate and real-time data needed to assess the health state of the monitored asset. This paper concludes with insights into the practical implications of adopting such a solar-powered WSN, emphasizing its potential to revolutionize bridge health monitoring by offering a cost-effective and energy-efficient solution for long-term infrastructure resilience. Full article

Anomalies frequently occur during the operation of spacecraft in orbit, and studying anomaly detection methods is crucial to ensure the normal operation of spacecraft. Due to the complexity of spacecraft structures, telemetry data possess characteristics such as high dimensionality, complexity, and large scale. Existing methods frequently ignore or fail to explicitly extract the correlation between variables, and due to the lack of prior knowledge, it is difficult to obtain the initial relationship of variables. To address these issues, this paper proposes a new method, namely spatio-temporal graph learning reconstruction (STGLR), for spacecraft anomaly detection. STGLR employs a dynamic graph learning module to infer the initial relationships among telemetry variables. It then constructs a spatio-temporal feature extraction module to capture complex spatio-temporal dependencies among variables, leveraging a graph sample and aggregation network to learn embedded features and incorporating an attention mechanism to adaptively select salient features. Finally, a reconstruction module is used to learn the latent representations of features, capturing the normal patterns in telemetry data and achieving anomaly detection. To validate the effectiveness of the proposed method, experiments were conducted on two public spacecraft datasets, and the results demonstrate that the performance of the STGLR method surpasses existing anomaly detection methods, with an average F1 score exceeding 0.97. Full article

Fires and fire hazards in industrial environments pose a significant risk to safety, infrastructure, and the operational community. The need for real-time monitoring systems capable of detecting fires early and transmitting alerts promptly is crucial. This paper presents a fire monitoring system utilizing lightweight communication protocols, a multi-hop wireless network, and anomaly detection techniques. The system leverages Message Queue Telemetry Transport (MQTT) for efficient message exchange, the ESP-NOW for low-latency and reliable multi-hop wireless communications, and a radio environment map for optimal node placement, eliminating packet loss and ensuring robust data transmission. The proposed system addresses the limitations of traditional fire monitoring systems, providing flexibility, scalability, and robustness in detecting fire. Data collected by ESP32-CAM sensors, which are equipped with pre-trained YOLOv5-based fire detection modules, are processed and transmitted to a central monitoring server. Experimental results demonstrate a 100% success rate in fire detection transmissions, a significant reduction in latency to 150ms, and zero packet loss under REM-guided configuration. These findings validate the system’s suitability for real-time monitoring in high-risk industrial settings. Future work will focus on enhancing the anomaly detection model for greater accuracy, expanding scalability through additional communication protocols, like LoRaWAN, and incorporating adaptive algorithms for real-time network optimization. Full article

Mud pulse telemetry (MPT) systems are widely recognized for their effectiveness and are most commonly used to transmit downhole data to the surface in real time. These data facilitate the drilling process and make it more cost-efficient. In MPT, the mud channel presents a challenging communication environment, primarily due to various sources of noises, with pump noise being the most dominant. In this paper, a noise cancellation method based on discrete Fourier transform (DFT) is proposed for demodulation under a low signal-to-noise ratio, eliminating the pump noise generated by two pumps with a single sensor during drilling. The method employs DFT to estimate the noise spectrum, subtracts noises from the received signal, and performs an inverse transformation to reconstruct the original signal estimation. The effectiveness of the proposed method is evaluated through a simulation analysis and field experiments. The simulation results indicate that the major components of multiple pump noises could be successfully eliminated. The field experiment results demonstrate that the demodulation of the received data achieves advanced data rate communication and a low bit error rate (BER) in a 3000 m drilling system. Full article

In biomedical research, telemetry is used to take automated physiological measurements wirelessly from animals, as it reduces their stress and allows recordings for large data collection over long periods. The ability to transmit high-throughput data from an in-body device (e.g., implantable systems, endoscopic capsules) to external devices can also be achieved by radiofrequency (RF), a standard wireless communication procedure. However, wireless in-body RF devices do not exceed a transmission speed of 2 Mbit/s, as signal absorption increases dramatically with tissue thickness and at higher frequencies. This paper presents the design of an optical wireless communication system (OWCS) for neural probes with an optical transmitter, sending out physiological data through an optical signal that is detected by an optical receiver. The optical receiver position is controlled by a tracking system of the small animal position, based on a cage with a piezoelectric floor. To validate the concept, an OWCS based on a wavelength of 850 nm for a data transfer of 5 Mbit/s, with an optical power of 55 mW, was demonstrated for a tissue thickness of approximately 10 mm, measured in an optical tissue phantom. Full article

Currently, most oil and gas wells are drilled by continuously transmitting downhole measured information (directional and geological information) in real-time to the surface to monitor and steer the well along a pre-defined path. The intelligent drill pipe method can transmit data over longer distances and at a higher rate than other methods, such as mud pulse telemetry, acoustic telemetry, and electromagnetic telemetry. Nevertheless, it is expensive and requires boosters along the drill string. In the available literature, academic research rarely addresses the data transmission process in boreholes using intelligent drill pipes. Furthermore, there is a need for an effective and validated model to study various controllable parameters to enhance the efficiency of the intelligent drill pipe telemetry without the need to develop several physical lab or field prototypes. This paper presents the development of a model based on MATLAB Simulink to simulate the process of data transmission in boreholes utilizing intelligent drill pipes. Laboratory experimental prototype measurements have been used to test the model’s effectiveness. A good correlation is found between the measured lab data and the model’s predictions for the signals transmitted contactless through intelligent drill pipes with a correlation coefficient (R²) above 0.9. This model can enhance data transmission efficiency via intelligent drill pipes, study different concepts, and eliminate the need to develop several unnecessarily expensive and time-consuming physical lab prototypes. Full article

This paper presents the design and development of a proof of concept (PoC) open-source data logger system for wireless data acquisition via Wi-Fi aimed at bench testing and fault detection in combustion and electric engines. The system integrates multiple sensors, including accelerometers, microphones, thermocouples, and gas sensors, to monitor critical parameters, such as vibration, sound, temperature, and CO₂ levels. These measurements are crucial for detecting anomalies in engine performance, such as ignition and combustion faults. For combustion engines, temperature sensors detect operational anomalies, including diesel engines operating beyond the normal range of 80 °C to 95 °C and gasoline engines between 90 °C and 110 °C. These readings help identify failures in cooling systems, thermostat valves, or potential coolant leaks. Acoustic sensors identify abnormal noises indicative of issues such as belt misalignment, valve knocking, timing irregularities, or loose parts. Vibration sensors detect displacement issues caused by engine mount failures, cracks in the engine block, or defects in pistons and valves. These sensors can work synergistically with acoustic sensors to enhance fault detection. Additionally, CO₂ and organic compound sensors monitor fuel combustion efficiency and detect failures in the exhaust system. For electric motors, temperature sensors help identify anomalies, such as overloads, bearing problems, or excessive shaft load. Acoustic sensors diagnose coil issues, phase imbalances, bearing defects, and faults in chain or belt systems. Vibration sensors detect shaft and bearing problems, inadequate motor mounting, or overload conditions. The collected data are processed and analyzed to improve engine performance, contributing to reduced greenhouse gas (GHG) emissions and enhanced energy efficiency. This PoC system leverages open-source technology to provide a cost-effective and versatile solution for both research and practical applications. Initial laboratory tests validate its feasibility for real-time data acquisition and highlight its potential for creating datasets to support advanced diagnostic algorithms. Future work will focus on enhancing telemetry capabilities, improving Wi-Fi and cloud integration, and developing machine learning-based diagnostic methodologies for combustion and electric engines. Full article

To achieve low-cost and on-demand launches of microsatellites, the authors have been researching and developing a micro hybrid rocket since 2014. In 2018, a ballistic launch experiment was performed using the developed hybrid rocket, where it reached an altitude of about 6.2 km. The rocket engine had a 3D-printed solid fuel grain made of acrylonitrile butadiene styrene (ABS) resin in combination with a nitrous oxide oxidizer. The fuel grain port had a star-fractal swirl geometry in order to increase the surface area of the port, to promote the laminar–turbulent transition by increasing the friction resistance, and to give a swirling velocity component to the oxidizer flow. This overcame the hybrid rocket’s drawback of a low fuel regression rate; i.e., it achieved a higher fuel gas generation rate compared with a classical port geometry. In 2021, the hybrid rocket engine was scaled up, and its total impulse was increased to over 50 kNs; it reached an altitude of 15 km. In addition to the engine, other components were also improved, such as through the incorporation of lightweight structures, low-shock separation devices, a high-reliability telemetry device, and a data logger, while keeping costs low. The rocket was launched and reached an altitude of about 10.1 km, which broke the previous Japanese altitude record of 8.3 km for hybrid rockets. This presentation will report on the developed components from the viewpoint of system design and the results of the ballistic launch experiments. Full article

An ecological understanding of threatened species provides the basis for their protection and recovery. This information must be used to analyze threats in order to propose conservation strategies for target species. River management projects, such as the construction of dikes, revetments, and dredging, are often undertaken to prevent flooding, and these activities affect fish communities and population dynamics. The critically endangered Pseudobagrus brevicorpus is highly vulnerable, but the causes of its decline are poorly understood. In this study, we assess the movements and habitat selection of P. brevicorpus to better understand its ecological characteristics and analyse the causes of its decline. We used radio telemetry to track the movements of the species and compared the effects of river-maintenance projects with data from a long-term study of the distribution of this endangered species. Total movements and home ranges were quite limited, with an average total distance traveled of 107.58 ± 66.01 m over an approximately 8-week monitoring period. The average MCP (minimum convex polygon) was 341.91 ± 776.35 m², the KDE (kernel density estimation) 50 was 76.01 ± 30.98 m², and the KDE 95 was 144.41 ± 58.86 m². The species is nocturnal, and during the day, individuals primarily hide among rocks and aquatic roots. The movement and habitat selection of P. brevicorpus indicated that the species could be directly or indirectly affected by river management. Acute population declines have been anticipated due to a lack of avoidance during management, and post-management habitat loss appears to have contributed to long-term population declines. Therefore, a strategic approach that considers ecological consequences is urgently needed to prevent the extinction of this species. Full article

Water pollution presents one of the biggest challenges in the world today, as the degradation of water quality of rivers in many instances is increasing so fast and poses a big danger to all forms of life, eventually causing many aquatic species and other species that depend on them to be endangered. Hence, with the development of Internet of Things (IoT) and Wireless Sensor Networks (WSNs), there arises a need to monitor river waters for a timely response in protecting the rivers, which is the aim of this paper. With respect to this project, we searched a little bit for some existing IoT technologies and other related work. In this paper, we propose a practical low-cost solution based on Long Range (LoRa) technology to obtain real-time observations of, with certain sensors, such water parameters as temperature, pH, conductivity and turbidity. Data gathered at a sensor node are transmitted via LoRa modulation to a gateway for processing and local storage on a Message Queuing Telemetry Transport (MQTT) server, visualization on a Node-RED interface, or transmission to the cloud. The prototype system created is employed in the actual field and demonstrates that the water quality monitoring in the river can be carried out effectively within a small scale of the area of roughly 20 km² depending on the location of the study site. Full article

Objectives: This study aimed to investigate the onset time to habitual psychogenic non-epileptic seizures (PNES) in adults referred to Guy’s and St Thomas’ Neurophysiology Department for home video telemetry (HVT) with a clinical question of PNES. The primary objective was to determine the optimal time window for HVT recording for patients with suspected PNES to try to improve the allocation of clinical resources. The secondary objective was to explore any potential association between time to habitual PN ES onset and demographic indexes and other clinical, neuro-radiological and semiological findings. Methods: We performed a retrospective analysis of our XLTEK database between 2019 and 2020. A multifactorial analysis of PNES semiologic subtypes, patient demographics, psychiatric comorbidities and neuroimaging was conducted to explore their impact on time to PNES within an HVT study. People who had at least one typical PNES during their recording were included. The exclusion criteria included people who had the test performed without video recording. The total number of participants was 37. The data were extracted from our local XLTEK database. Statistical analyses using Mann–Whitney U and Fischer exact tests were carried out. Results: The mean time to first habitual PNES onset was seven hours, with a mean recording duration of 46 h. The most commonly occurring event type was blank spells (12, 32%), with the least common presentation being déjà vu (1, 3%). There was a significant association between time to PNES onset and male sex (p = 0.04). There was a significant association between time to PNES onset and abnormal MRI findings (p = 0.02). Particular PNES semiologic subtypes were not significantly linked with PNES onset time. Conclusions: Our study highlights that on average, patients with PNES will rapidly have their first habitual event within an HVT study (mean time to event onset of seven hours), consistent with the current literature. This raises the question of whether HVT study duration could be reduced to release study resources and aid departmental efficiencies. We also observe the novel finding that men presented significantly earlier with their habitual PNES event than women, and that abnormal imaging findings were also significantly associated with an earlier time to event onset, although the reason for this association is yet to be determined. Full article

Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) products are essential data sources for global and regional climate change research. Currently, several versions of the MODIS LST product have been released, yet the performance differences and uncertainties they introduce in land surface studies remain insufficiently addressed. To bridge this gap, this study focuses on four distinct versions of the LST product: MxD11A1 Collection 5 (C5), Collection 6 (C6), Collection 6.1 (C6.1), and MxD21A1 Collection 6.1 (MxD21). The spatial resolution of all product generations is 1 km, and the temporal resolution is 0.5 days. This study provides a comprehensive analysis of the errors arising from different generations of these products in various land surface process studies. The error assessment includes cross-comparisons between product versions and evaluations of the absolute errors generated. Absolute errors in evaluation data were collected from 13 surface sites within the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) project during the period 2013–2018. Cross-validation results show that the largest difference between C5 and C6.1 occurs over bare land, with an RMSE of approximately 1.45 K, while there is no significant change between C6 and C6.1. MOD21 shows considerable variation compared to C6.1 at night across different land cover types, with RMSE over cropland exceeding 2 K. The temperature difference between MOD21 and C6.1 is more pronounced at night (2.01 K) than during the day (0.30 K). Validation results based on temperature indicate that C5 has greater uncertainty compared to C6, especially over bare land, where errors are 2.06 K and 1.06 K, respectively. Furthermore, MxD21 demonstrates significant day–night performance discrepancies, with an average bias of 0.10 K at night, while daytime errors over bare land can reach 2 K, potentially influenced by atmospheric conditions. Based on the research in this paper, it is possible to clarify the performance of different versions of MODIS products, reflecting the appropriateness of their past applications; on the other hand, it is recommended to prioritize the use of the MxD11A1 C6 and C6.1 products for monitoring and applications in bare soil areas to ensure higher accuracy. Furthermore, for day and night monitoring, it may be beneficial to alternate between the MxD11A1 and MxD21A1 products to fully leverage their respective advantages and enhance overall monitoring effectiveness. Full article

Aiming at classification and recognition of aero-engines, two telemetry Fourier transform infrared (FT-IR) spectrometers are utilized to measure the infrared spectrum of the areo-engine hot jet, meanwhile a spectrum dataset of six types of areo-engines is established. In this paper, a convolutional autoencoder (CAE) is designed for spectral feature extraction and classification, which is composed of coding network, decoding network, and classification network. The encoder network consists of convolutional layers and maximum pooling layers, the decoder network consists of up-sampling layers and deconvolution layers, and the classification network consists of a flattened layer and a dense layer. In the experiment, data for the spectral dataset were randomly sampled at a ratio of 8:1:1 to produce the training set, validation set, and prediction set, and the performance measures were accuracy, precision, recall, confusion matrix, F1 score, ROC curve, and AUC value. The experimental result of CAE reached 96% accuracy and the prediction running time was 1.57 s. Compared with the classical PCA feature extraction and SVM, XGBoost, AdaBoost, and Random Forest classifier algorithms, as well as AE, CSAE, and CVAE deep learning classification methods, the CAE network can achieve higher accuracy and efficiency and can complete the spectral classification task. Full article

As information technology progresses, the need for digital transformation within the industrial sector has become increasingly apparent, and digital twin technology has emerged as a significant trend in manufacturing. Digital twins synchronize physical and digital environments, overcoming spatial and temporal limitations to create various added values that are unattainable in reality. This paper presents a model that integrates digital twin technology with production and operational technologies at manufacturing sites, enabling remote, centrally controlled manufacturing services that transcend physical constraints. Specifically, by utilizing Message Queuing Telemetry Transport (MQTT) for real-time synchronization, this approach ensures efficient and timely data transfer between physical and digital environments. While traditional approaches often encounter challenges due to high investment costs and design complexities, this paper proposes a cost-effective and practical solution that reflects actual factory conditions. Full article

This paper focuses on the novel and unique training provision of acoustics relevant for noise, vibration, and harshness (NVH), focused on the ISO-362 standard highlighting important design aspects for electric vehicles. A case study of the practical implementation of off-site vehicle testing supporting an acoustics module is described, detailing a time-constrained test for automotive pass-by-noise and tyre-radiated noise with speed. Industrial test standards are discussed, with education as a primary motivation. The connections between low-cost, accessible equipment and future electric vehicle acoustics are made. The paper contains a full equipment breakdown to demonstrate the ability to link digital data transfer, analogue-to-digital communication, telemetry, and acquisition skills. The benchmark results of novel pass-by-noise and tyre testing are framed around discussion points for assessments. Inexpensive Arduino Uno boards provide data acquisition with class 1 sound pressure meters, XBee radios provide telemetry to a vehicle, and a vehicle datalogger provides GPS position with CANBUS data. Data acquisition is triggered through the implementation of light gate sensors on the test track, with the whole test lasting 90 minutes. Full article