Search Results (1,015)

To address the limited spatial localization accuracy of partial discharge (PD) in high-voltage cable terminations and the difficulty in accurately determining the trigger time in traditional ultrasonic detection, this paper proposes an electro-acoustic synergistic localization technology based on a high-frequency current transformer (HFCT) and a Sagnac optical fiber interferometer. A high-sensitivity Sagnac acoustic sensor based on a 3D-printed photosensitive resin mandrel was developed. Through structural design and 0–50 kHz amplitude–frequency testing, the sensor exhibits a dominant resonant response at 33.2 kHz. This narrow-band, high-sensitivity characteristic effectively enhances the perception capability for weak PD ultrasonic signals. An electro-acoustic synergistic detection system was constructed, in which the high-frequency PD current signal captured by the HFCT was used as the electrical time reference, and a dual-channel Sagnac sensor array was used to extract the arrival times of ultrasonic waves. In a 12 kV laboratory cable-termination PD experiment, the proposed system identified the representative built-in air-gap PD source with an absolute localization error of 5 mm under the tested laboratory configuration. This value should be interpreted as the localization result for the tested representative defect, rather than as a generally validated accuracy specification of the system. This study provides a proof-of-concept laboratory demonstration of an electro-acoustic localization strategy that combines the fast electrical response of HFCT detection with the electromagnetic-interference immunity and acoustic sensitivity of Sagnac fiber-optic sensing. Full article

The reliable evaluation of the interfacial bonding quality of steel structures strengthened with carbon fiber-reinforced polymer (CFRP) is crucial to ensuring the long-term service safety of the structures. Focusing on the active and passive detection methods based on piezoelectric sensing, this paper takes numerical simulation as the core research method to provide theoretical verification and mechanism explanation for subsequent key experiments, thus supporting the accurate detection of interfacial damage in CFRP–steel plate joints. A 3D piezoelectric–structural coupling finite element model and a 2D ultrasonic guided wave propagation finite element model were established via COMSOL Multiphysics 6.2 to systematically simulate the electromechanical response characteristics of three piezoelectric sensors (PMN-PT, PZT and PVDF). The research focused on analyzing the potential output and voltage–load response of the three sensors, and simultaneously explored the propagation laws and energy evolution mechanisms of ultrasonic waves in the presence of different debonding damages and groove defects in CFRP plates. The simulation results show that the PMN-PT sensor exhibits the optimal detection performance, with its peak potential output reaching 2.66 times that of the PZT sensor and 4.69 times that of the PVDF sensor, with a load sensitivity of 484.3 mV/kN. In the ultrasonic active detection of interfacial debonding damage, the first-wave amplitude has a significant positive correlation with the debonding length, and this characteristic is attributed to the strong reflection effect and energy accumulation caused by the acoustic impedance mismatch at the CFRP–air interface. For the internal groove defects in CFRP plates, the simulation clarifies that the increase in groove length leads to energy trapping in the plate, while the increase in groove depth intensifies ultrasonic wave energy reflection. The numerical simulation results were compared and verified with data from companion experiments conducted by the authors’ team, showing a high degree of consistency, which confirms the accuracy and reliability of the established finite element models. Meanwhile, the physical essence of damage detection is elucidated from the perspective of wave theory, providing a solid numerical analysis foundation and theoretical support for the intelligent monitoring of interfacial damage in CFRP–steel structures. Full article

This paper describes the development of an automated solid waste sorting system that integrates advanced computer vision pipelines with a robotic manipulator for real-time classification and actuation. The system consists of a Deep Neural Network (DNN) and a YOLOv8-based perception module. Thedeveloped model is capable of accurately detecting and classifying objects with confidence scores exceeding 0.71, and the overall system attained a sorting accuracy of approximately 81.8% across multiple test batches. From an integration perspective, the coordination among the Intel RealSense camera, Raspberry Pi 5, Arduino Uno, ultrasonic sensors, relay-switching circuit, and SCORBOT-ER 4U robotic arm demonstrated reliable communication and execution, enabling accurate pick-and-place operations. Overall, the results confirm that the proposed system provides a functional and scalable proof of concept for automated waste segregation in controlled environments. The study highlights that while current performance is sufficient for low-speed applications, further improvements in dataset diversity, perception robustness, mechanical gripping, and feedback control are necessary to achieve higher accuracy, reliability, and industrial applicability. Full article

Recent advancements in sensor technology have made in-situ crack assessment of structures feasible. To investigate the correlation between the ultrasonic amplitude and metal fatigue life, an aluminum compact tension (C(T)) specimen was fabricated to simulate fatigue damage in thin plate structures. An experimental investigation of fatigue crack propagation was performed, wherein the specimen experienced cyclic uniaxial tensile loading at constant amplitude. The crack propagation behavior was analyzed, and the relationship between crack length and the associated loading cycles was determined. Additionally, the evolution of the ultrasonic signal during crack propagation was investigated, and the quantitative dependence of the ultrasonic characteristic parameter on crack length was revealed. Finally, a model correlating ultrasonic characteristic parameters with loading cycles was developed, enabling fatigue life evaluation. The proposed method demonstrates significant potential for evaluating the fatigue life of thin plate structures. Full article

Acoustoelasticity provides the physical sensing principle for ultrasonic stress measurement. However, most existing formulations are restricted to isotropic media, simple stress conditions, and Cartesian coordinate systems, which limits their applicability in practical sensing scenarios involving curved and anisotropic structures. In this work, a general tensorial formulation of acoustoelasticity is developed based on the theory of incremental deformation. The proposed governing equations describe the motion of incremental displacement with explicit dependence on initial stress or strain, and are applicable to materials with arbitrary symmetry and general initial stress states. Owing to its coordinate-independent tensorial nature, the formulation can be expressed in any curvilinear coordinate system. To facilitate practical ultrasonic sensing applications, the general equations are further expanded in a cylindrical coordinate system for orthotropic materials. This enables the analysis of elastic wave propagation in curved structures such as pipelines, pressure vessels, and boreholes. The formulation establishes a direct relationship between initial stress and effective elastic properties, which determine wave velocities measurable by ultrasonic sensors, such as time-of-flight and phase velocity. The proposed approach provides a rigorous theoretical foundation for ultrasonic stress sensing and nondestructive testing, particularly for curved and anisotropic structures, and supports improved accuracy in sensor-based stress evaluation. Full article

The ultrasonic anti-icing and de-icing system applies an electrical field to the ship’s surface to weaken the adhesion between the ice layer and the steel plates, while using ultrasonic vibration to generate longitudinal shear forces that break the remaining adhesion, thereby achieving effective de-icing. This study employs the control variable method to examine how different vibration frequencies and configurations of ultrasonic vibrators (“de-icing formations”) impact the adhesion characteristics of the ice layer covering the hull steel plates. Due to the lack of existing experimental instruments for measuring the adhesion force of ship icing, we designed an intelligent device to test the adhesion force between the ship and the ice layer. This device incorporates high-precision sensors and an advanced data acquisition system, enabling real-time measurement and recording of adhesion force data between icing and the hull steel plates. Using the newly developed JUST flat plate adhesion force testing system, this study evaluates how various distribution strategies of ultrasonic vibrators influence the ice adhesion force. Furthermore, the experiment investigates the de-icing efficiency of ultrasonic vibrators with the same number of but different “de-icing formations” and vibration frequencies under identical conditions of ice thickness, time, and excitation current, and provides a detailed analysis of the variation in ice adhesion force. These results clarify the mechanism by which the ultrasonic system manages the adhesion force of ship icing. This research not only introduces new ideas and methods for ship anti-icing and de-icing technology but also offers a scientific basis for enhancing navigation safety and operational efficiency in icy conditions. Full article

This review paper presents a comprehensive and system-oriented analysis of advanced non-destructive testing (NDT) technologies for metal additive manufacturing (AM), including X-ray computed tomography (XCT), ultrasonic testing (UT), infrared thermography, acoustic emission (AE), and electromagnetic techniques. While the existing literature often focuses on the physical principles of individual NDT methods, this work addresses a critical knowledge gap by analyzing NDT as a digitally integrated “quality intelligence layer” rather than a standalone post-process inspection tool. The primary motivation is to bridge the disconnect between raw inspection data and cyber–physical production systems. Particular focus is given to NDT data analytics and digitalization, where machine learning (ML) and digital twin (DT) integration are discussed as fundamental enablers of intelligent manufacturing. The review systematically examines image and signal processing pipelines required for quantitative defect characterization, highlighting challenges related to voxel resolution, signal-to-noise ratio, anisotropic microstructures, and operator dependency. It further analyzes supervised learning, deep learning, and multi-sensor data fusion approaches for automated defect classification and predictive quality assessment. Furthermore, the role of digital twins in coupling in situ monitoring data, ex situ NDT results, and physics-based models is discussed as a transformative pathway toward closed-loop process control and evidence-based certification. By synthesizing NDT science with digital manufacturing architectures, this review contributes a unique framework for transitioning from traditional inspection-centric quality control to a predictive, adaptive, and digital twin-enabled quality assurance paradigm. The work concludes by identifying key research gaps in data standardization and computational scalability, providing a strategic roadmap for the future of smart AM production. Full article

This paper presents a low-cost Internet-of-Things (IoT) telemetry and alerting system for monitoring and protecting sea turtle nests. The proposed platform integrates temperature, humidity, vibration, ultrasonic proximity, and ambient light sensors into an autonomous sensing node based on the ESP8266 microcontroller. Measurements are transmitted wirelessly to a cloud backend for real-time visualization and rule-based alert generation. The system is designed to support continuous nest-level monitoring and rapid response to environmental and anthropogenic threats such as overheating, artificial light exposure during hatching, and physical disturbance. In contrast to approaches that require extensive historical datasets or machine-learning models, the proposed solution employs transparent threshold-based rules that provide reliable operation without training data. The platform emphasizes low cost, ease of deployment, and scalability, making it suitable for large-scale conservation deployments across multiple nesting sites. It provides conservation practitioners with actionable situational awareness that complements existing field-based monitoring and protection strategies. Full article

Odor source localization (OSL) using mobile robots in indoor ventilated environments remains challenging due to turbulent dispersion, uneven concentration distribution, and weak robustness in conventional algorithms. This paper proposes an efficient OSL strategy for wheeled mobile robots by integrating time-varying smoke plume modeling, particle filtering (PF), and information entropy. A multi-sensor fusion perception system is developed, including an LDS-02 LiDAR, ultrasonic anemometer, and PMS5003 particle sensor. The proposed method employs a plume model to characterize odor particle propagation, uses particle filtering to estimate the posterior distribution of the source location, and introduces information entropy to quantify perceptual uncertainty and optimize robot path planning. Comparative simulations and real-world experiments are conducted in a 5 m × 3 m indoor ventilated environment against the traditional gradient–bionic hybrid algorithm. Results demonstrate that the proposed algorithm significantly reduces the average search time and improves the localization success rate. The long-distance localization success rate exceeds 90%, and the positioning error is controlled within 0.5 m. The proposed strategy provides a reliable and practical solution for OSL in indoor ventilation environments. Full article

We report on a novel approach for the monitoring of tangential laser turning with ultrashort laser pulses. By using an ultra-sonic sensor consisting of a membrane-free optical microphone, the current state of the ablation process can be analyzed, potentially enabling a real-time automated regulation. With its high sensitivity, bandwidth, and sampling rate, it is an ideal tool for process monitoring. The material ablation caused by focused femtosecond laser pulses produces distinct sound waves, which can be detected by the optical microphone. The diameter reduction of a rotating cylindrical workpiece during the laser turning process with ultrashort laser pulses results in a variation in the acoustic emissions. From this, properties like the state of the machining progress can be inferred. Full article

This paper presents a new type of ultrasonic gyroscopic sensor based on a solid-state standing-wave vibrator, which is promising for shock-resistant applications. A theoretical model of the proposed design, which is a layered structure, and the numerical simulation of its frequency response using the developed software are presented. A test sample of the novel sensing element was made and experimental studies of its frequency response were conducted. The results showed a high correlation between the resonant frequencies both for the real sample research and numerical modeling; thus, the validity of the theoretical model was confirmed. The laboratory investigation of the developed sensing element on a test bench under rotating conditions was carried out and a shift in the standing-wave amplitude proportional to the angular velocity of rotation was revealed; thus, an informative signal for this type of gyroscopic sensor was found. It is shown that the amplitude of the output signal of the new sensor on standing waves compares favorably with the signal levels reported for similar traveling-wave solutions in previous studies. The optimization strategies for the new sensor’s design and operating mode to increase signal to noise ratio are also identified. Thus, the potential of using the developed solid-state standing-wave vibrator as a shock-resistant ultrasonic gyroscopic sensor is supported. Full article

Reliable detection of internal defects in pressure vessel structures remains essential for structural safety and condition-based maintenance. This study presents a low-complexity structural health monitoring framework based on fiber Bragg grating (FBG) sensing and multiresolution wavelet analysis for void detection in curved pressure vessel structures under guided wave excitation. Guided waves are introduced using piezoelectric actuators, while the FBG sensors capture the resulting strain-induced wavelength variations. Due to the limited bandwidth of the optical interrogator, the recorded signals represent the strain envelope response associated with guided wave interaction rather than the resolved ultrasonic carrier waveform. To characterize defect-induced changes, the acquired signals are analyzed using continuous wavelet transform (CWT) for time–frequency interpretation, and discrete wavelet transform (DWT) and wavelet packet transform (WPT) for energy-based multiresolution feature extraction. Experimental results show that void defects lead to consistent redistribution of wavelet-domain energy and increased non-stationarity in the measured strain responses. These trends are further supported by finite-element simulations, which reproduce similar energy redistribution patterns between intact and damaged cases. The proposed framework provides a physically interpretable and computationally efficient approach for defect detection using low-bandwidth FBG sensing, without reliance on high-speed acquisition or data-intensive learning models. The results demonstrate the feasibility of using energy-based multiresolution analysis of FBG strain signals for practical and scalable structural health monitoring of pressure vessel systems. Full article

Acoustic emission (AE) monitoring in metal additive manufacturing (AM) requires compact sensors capable of high-frequency operation, broad bandwidth, and high sensitivity. However, increasing structural stiffness to achieve high resonance frequencies typically reduces electromechanical sensitivity. This work presents a finite element study of a coupled-resonator capacitive micromachined ultrasonic transducer (CMUT) designed to address this trade-off. The proposed architecture integrates three mechanically coupled silicon membranes with a stepped capacitive cavity that increases capacitance while preserving structural stiffness, enabling enhanced sensitivity without compromising high-frequency operation. COMSOL Multiphysics simulations were used to evaluate modal characteristics and frequency response under DC pre-stressed conditions. Modal coupling produced closely spaced resonances that broadened the effective bandwidth, while the stepped cavity significantly increased voltage output through improved electromechanical coupling. Compared to a single-resonator flat-cavity design, the coupled stepped-cavity configuration demonstrated nearly a threefold enhancement in output voltage while maintaining operation near 100 kHz. Additionally, adjusting the central resonator length enabled controlled frequency tuning for scalable array implementation. These results establish a proof of concept for a high-frequency, high-sensitivity micro-electro-mechanical systems (MEMS) CMUT architecture suitable for distributed AE monitoring in advanced manufacturing environments. Full article

In the world of electronics, sensors are more than just components—they are the eyes, ears, and touchpoints of modern technology. From self-driving cars that rely on LiDAR and ultrasonic sensors to navigate complex environments, smart watches that detect your every move and heartbeat, to advanced brain chip implants that can sense your thoughts and translate them into physical moves with the assistance of exoskeletons, sensors bridge the gap between the physical world and digital systems. The rapid arrival of advanced Artificial Intelligence (AI) and Large Language Models (LLMs) has transformed almost every part of technology, especially data processing. However, the development of sensors remains a vitally important topic. Sensors form the foundation of innovation in electronics; novel sensors provide reliable data across a broad range of application areas and are a foundation for intelligent systems. Notably, knowing the capabilities and limitations of each sensor type is crucial for selecting the right sensor for a specific application, troubleshooting issues, and optimizing system performance. This book, entitled “Advanced Sensors for Real-Time Monitoring Applications II”, demonstrates developments of real sensors for a range of applications, including descriptions of fundamental principles of operation, concepts, theory, and practical validation of the results, as well as a review of current state-of-the-art and future directions. Full article

Despite its significant water-saving potential, the adoption of alternate wetting and drying (AWD) irrigation remains limited due to infrastructure constraints and intensive manual monitoring requirements. An automated precision irrigation system was developed and tested at the Bangladesh Rice Research Institute research farm in Gazipur, Bangladesh. The system combined ultrasonic water-level sensors, capacitive soil moisture sensors, an Arduino-based microcontroller, a GSM communication module, and solar-powered automatic control gates. Field performance was evaluated following a Randomized Complete Block Design (RCBD) under four irrigation treatments: IRRISAT, IRRI35, IRRI25, and continuous flooding (CF). The first three irrigation treatments were operated using scheduled daily decision windows, in which irrigation actions were automatically triggered based on predefined schedules and sensor threshold values. In IRRISAT, irrigation started when soil moisture dropped slightly below saturation and stopped at a ponding depth of 5 cm, while IRRI35 and IRRI25 were triggered at volumetric soil water contents of 35% and 25%, respectively, with the same upper cutoff of 5 cm ponding depth; CF served as the control. The IRRI35 treatment achieved a high grain yield (7.76 t ha⁻¹) while reducing water use by 28% and energy consumption by 37% compared to CF. Water use efficiency was considerably higher under IRRI35 (9.4 kg ha⁻¹ mm⁻¹) than under CF (6.7 kg ha⁻¹ mm⁻¹). The automated system proved to be reliable and precise in scheduled irrigation control, significantly reducing water use and labor requirements. The findings suggest that large-scale adoption of the system under real-world cultivation conditions could reduce irrigation energy needs and contribute to sustainable water governance in rice production. Full article