Search Results (71)

Microseismic source localization is essential for the early warning of disasters in deep rock mass engineering. Traditional time difference methods require a dense sensor network, which is often impractical in large-scale scenarios with low-density sensor placement. Three-component microseismic sensors offer a promising alternative by utilizing multi-axis sensing, but their application depends on accurate sensor attitude estimation—a challenge due to installation deviations, integration errors, magnetic interference, and ambiguity in P-wave polarization direction. This study proposes an attitude calculation and source localization method based on P-wave polarization analysis. For attitude estimation, a unit vector from the sensor to the event is used as a reference; the P-wave polarization direction is extracted via covariance matrix analysis, and a novel “direction–vector–rotation–matrix cross-optimization” method resolves polarization–vector ambiguity. Multi-event data fusion enhances stability and robustness. For source localization, a “1 three-component + 1 single-component” sensor scheme is introduced, combining distance, azimuth, and distance difference constraints to achieve accurate positioning while substantially reducing hardware and energy costs. Field validation at the Yebatan Hydropower Station shows an average reference vector conversion error of 7.72° and an average localization deviation of 10.72 m compared with a conventional high-precision method, meeting engineering early-warning requirements. The proposed approach provides a cost-effective, efficient technical solution for large-scale microseismic monitoring with low sensor density, supporting sustainable infrastructure development through improved disaster risk management. Full article

In Global Navigation Satellite System (GNSS)-denied environments, reconstructing three dimensional trajectories using only an Inertial Measurement Unit faces challenges such as heading drift, stride error accumulation, and gait recognition uncertainty. This paper proposes a path estimation method with a nine-axis inertial sensor that continuously and accurately estimates an agent’s path without external support. The method detects stationary states and halts updates to suppress error propagation. During motion, gait modes including flat walking, stair ascent, and stair descent are classified using vertical acceleration with dynamic thresholds. Vertical displacement is estimated by combining gait pattern and posture angle during stair traversal, while planar displacement is updated through adaptive stride length adjustment based on gait cycle and movement magnitude. Heading is derived from the attitude matrix aligned with magnetic north, enabling projection of displacements onto a unified frame. Experiments show planar errors below three percent for one-hundred-meter paths and vertical errors under two percent in stair environments up to ten stories, with stable heading maintained. Overall, the method achieves reliable gait recognition and continuous three-dimensional trajectory reconstruction with low computational cost, using only a single inertial sensor and no additional devices. Full article

High-precision sensor co-registration is a critical prerequisite for achieving high-resolution imaging in Optically Pumped Magnetometer–Magnetoencephalography (OPM-MEG) systems. The conventional magnetic dipole fitting method, essentially a multipole expansion approximation of a finite-size coil, exhibits accuracy that strongly depends on spatial geometric factors such as coil–sensor distance, dipole orientation, and the projection angle of the sensor’s sensitive axis. Moreover, the approximation error increases significantly when sensors are placed either too close to the coils or at an unfavorable angular coupling. To address this issue, we propose a sensor localization and orientation method that combines magnetic dipole-equivalent modeling with a rigid coil structure (RCS). The RCS provides stable geometric constraints and eliminates uncertainties introduced by scalp-attached coils. In addition, three objective functions (the standard Frobenius norm, a weighted Frobenius norm and the structural similarity index (SSIM)) are formulated to mitigate the imbalance caused by near-field strong signals and to improve stability under noise and error propagation. Simulation results demonstrate that both under ideal conditions and with assembly perturbations, the weighted Frobenius norm and SSIM methods consistently achieve position errors below 1 mm and orientation errors below 1°, which effectively suppress large outlier deviations and achieve better performance than the standard Frobenius norm. The results confirm the effectiveness of the proposed method in achieving both high accuracy and robustness. Beyond clarifying the primary factors influencing magnetic dipole approximation errors, this study provides a geometry-constrained and optimization-based framework, offering a feasible pathway toward the practical implementation of high-precision, multi-channel OPM-MEG systems. Full article

Early and accurate detection of distributed bearing faults is essential to prevent equipment failures and reduce downtime in industrial environments. This study explores the optimal selection of input signal sources for high-accuracy distributed fault classification, employing wavelet transform and machine learning algorithms. The primary contribution of this work is to demonstrate that robust distributed bearing fault diagnosis can be achieved through optimal sensor fusion and wavelet-based feature engineering, without the need for deep learning or high-dimensional inputs. This approach provides interpretable, computationally efficient, and generalizable fault classification, setting it apart from most existing studies that rely on larger models or more extensive data. All experiments were conducted in a controlled laboratory environment across multiple loads and speeds. A comprehensive dataset, including three-axis vibration, stray magnetic flux, and two-phase current signals, was used to diagnose six distinct bearing fault conditions. The wavelet transform is applied to extract frequency-domain features, capturing intricate fault signatures. To identify the most effective input signal combinations, we systematically evaluated Random Forest, XGBoost, and Support Vector Machine (SVM) models. The analysis reveals that specific signal pairs significantly enhance classification accuracy. Notably, combining vibration signals with stray magnetic flux consistently achieved the highest performance across models, with Random Forest reaching perfect test accuracy (100%) and SVM showing robust results. These findings underscore the importance of optimal source selection and wavelet-transformed features for improving machine learning model performance in bearing fault classification tasks. While the results are promising, validation in real-world industrial settings is needed to fully assess the method’s practical reliability and impact on predictive maintenance systems. Full article

A concept towards current measurement in low and medium voltage power distribution networks is presented. The concentric magnetic field around the current-carrying conductor should be measured using a nitrogen-vacancy quantum magnetic field sensor. A bottleneck in current measurement systems is the readout electronics, which are usually based on optically detected magnetic resonance (ODMR). The idea is to have a hardware that tracks up to four resonances simultaneously for the detection of the three-axis magnetic field components and the temperature. Normally, expensive scientific instruments are used for the measurement setup. In this work, we present an electronic device that is based on a Zynq 7010 FPGA (Red Pitaya) with an add-on board, which has been developed to control the excitation laser, the generation of the microwaves, and interfacing the photodiode, and which provides additional fast digital outputs. The T1 measurement was chosen to demonstrate the ability to read out the spin of the system. Full article

Touch is a crucial sense for advanced organisms, particularly humans, as it provides essential information about the shape, size, and texture of contacting objects. In robotics and automation, the integration of tactile sensors has become increasingly relevant, enabling devices to properly interact with their environment. This study aimed to develop a biomimetic, skin-inspired tactile sensor device capable of sensing applied force, characterizing it in three dimensions, and determining the point of application. The device was designed as a 4 × 4 matrix of tunneling magnetoresistive sensors, which provide a higher sensitivity in comparison to the ones based on the Hall effect, the current standard in tactile sensors. These detect magnetic field changes along a single axis, wire-bonded to a PCB and encapsulated in epoxy. This sensing array detects the magnetic field from an overlayed magnetorheological elastomer composed of Ecoflex and 5 µm neodymium–iron–boron ferromagnetic particles. Structural integrity tests showed that the device could withstand forces above 100 N, with an epoxy coverage of 0.12 mL per sensor chip. A 3D movement stage equipped with an indenting tip and force sensor was used to collect device data, which was then used to train neural network models to predict the contact location and 3D magnitude of the applied force. The magnitude-sensing model was trained on 31,260 data points, being able to accurately characterize force with a mean absolute error ranging between 0.07 and 0.17 N. The spatial sensitivity model was trained on 171,008 points and achieved a mean absolute error of 0.26 mm when predicting the location of applied force within a sensitive area of 25.5 mm × 25.5 mm using sensors spaced 4.5 mm apart. For points outside the testing range, the mean absolute error was 0.63 mm. Full article

Accurate current sensing in rectangular conductors is challenged by mechanical deformations, including eccentricity (X/Y-axis shifts) and inclination (Z-axis tilt), which distort magnetic field distributions and induce measurement errors. To address this, we propose a bio-inspired error compensation strategy integrating an elliptically configured Hall sensor array with a hybrid Grey Wolf Optimizer (GWO)-enhanced backpropagation neural network. The eccentric displacement and tilt angle of the conductor are quantified via a three-dimensional magnetic field reconstruction and current inversion modeling. A dual-stage optimization framework is implemented: first, establishing a BP neural network for real-time conductor state estimations, and second, leveraging the GWO’s swarm intelligence to refine network weights and thresholds, thereby avoiding local optima and enhancing the robustness against asymmetric field patterns. The experimental validation under extreme mechanical deformations (X/Y-eccentricity: ±8 mm; Z-tilt: ±15°) demonstrates the strategy’s efficacy, achieving a 65.07%, 45.74%, and 76.15% error suppression for X-, Y-, and Z-axis deviations. The elliptical configuration reduces the installation footprint by 72.4% compared with conventional circular sensor arrays while maintaining a robust suppression of eccentricity- and tilt-induced errors, proving critical for space-constrained applications, such as electric vehicle powertrains and miniaturized industrial inverters. This work bridges bio-inspired algorithms and adaptive sensing hardware, offering a systematic solution to mechanical deformation-induced errors in high-density power systems. Full article

Magnetic sensing technology is crucial for non-contact distance and position measurement. Due to the nonlinear characteristics of the magnetic fields from permanent magnets, conventional magnetic sensors struggle with accurate distance and position determination. To address this, we propose a distance/position sensor that employs a customized back propagation (BP) neural network. By detecting the magnetic field variations induced by a permanent magnet, the proposed sensor can effectively model the nonlinear mapping between magnetic field strength and distance, thereby enabling precise distance and position measurement. Experimental results demonstrate that the BP neural network approach, when employing a single magnetic sensor, exhibits a measurement error in the range of −0.0268 mm to 0.0362 mm over a distance of 0–70 mm, which is significantly lower than traditional methods based on the magnetic dipole model and the Levenberg–Marquardt (LM) algorithm. Increasing the number of sensors to three reduces the error further to −0.0107 mm to 0.0093 mm. Furthermore, when employing four magnetic sensors for position measurement within a 60 mm × 60 mm planar area, the positioning errors along the x-axis and y-axis are confined to the ranges of −0.6168 mm to 1.1312 mm and −0.6001 mm to 0.5133 mm, respectively. Full article

A three-dimensional active maglev (magnetic levitation) actuating system based on force imbalance is proposed. By combining the principle of force imbalance control with the control algorithm, the stable levitation and controllable levitating motion of the magnetic ball can be realized. The four electromagnetic actuating structures are used to stabilize the force of the controlled object, and the dual-hall sensor group and hardware differential method are used to improve control stability and accuracy. By combining the fine adjustment of the active maglev actuating system with the coarse adjustment of the mechanical arm, the three-dimensional levitation motion of the magnetic ball in curved pipes is realized. Experimental results show that the proposed control algorithm solves problems such as the increase of deviation between the controlled object and the steady-state operating point and the rapid deterioration of tracking performance in the model-based control method. In the vertical direction, the overshoot is within 0.418%, regardless of axis motion or non-axis motion. In the horizontal direction, the offset limits left and right of the axis are 4.590 mm and 3.536 mm, respectively. The fluctuation of vertical and horizontal motion is within the allowable range of ±0.2 mm. This can be applied to the non-destructive quality detection of the inner walls and the internal dredging of long and thin pipes in examinations and industrial fields. Full article

Underwater natural gas pipelines constitute critical infrastructure for energy transportation. Any damage or leakage in these pipelines poses serious security risks, directly threatening marine and lake ecosystems, and potentially causing operational issues and economic losses in the energy supply chain. However, current methods for detecting deterioration and regularly inspecting these submerged pipelines remain limited, as they rely heavily on divers, which is both costly and inefficient. Due to these challenges, the use of unmanned underwater vehicles (UUVs) becomes crucial in this field, offering a more effective and reliable solution for pipeline monitoring and maintenance. In this study, we conducted an underwater pipeline tracking and damage detection experiment using a remote-controlled unmanned underwater vehicle (UUV) with autonomous features. The primary objective of this research is to demonstrate that UUV systems provide a more cost-effective, efficient, and practical alternative to traditional, more expensive methods for inspecting submerged natural gas pipelines. The experimental method included vehicle (UUV) setup, pre-test calibration, pipeline tracking mechanism, 3D navigation control, damage detection, data processing, and analysis. During the tracking of the underwater pipeline, damages were identified, and their locations were determined. The navigation information of the underwater vehicle, including orientation in the x, y, and z axes (roll, pitch, yaw) from a gyroscope integrated with a magnetic compass, speed and position information in three axes from an accelerometer, and the distance to the water surface from a pressure sensor, was integrated into the vehicle. Pre-tests determined the necessary pulse width modulation values for the vehicle’s thrusters, enabling autonomous operation by providing these values as input to the thruster motors. In this study, 3D movement was achieved by activating the vehicle’s vertical thruster to maintain a specific depth and applying equal force to the right and left thrusters for forward movement, while differential force was used to induce deviation angles. In pool experiments, the unmanned underwater vehicle autonomously tracked the pipeline as intended, identifying damages on the pipeline using images captured by the vehicle’s camera. The images for damage assessment were processed using a convolutional neural network (CNN) algorithm, a deep learning method. The position of the damage relative to the vehicle was estimated from the pixel dimensions of the identified damage. The location of the damage relative to its starting point was obtained by combining these two positional pieces of information from the vehicle’s navigation system. The damages in the underwater pipeline were successfully detected using the CNN algorithm. The training accuracy and validation accuracy of the CNN algorithm in detecting underwater pipeline damages were 94.4% and 92.87%, respectively. The autonomous underwater vehicle also followed the designated underwater pipeline route with high precision. The experiments showed that the underwater vehicle followed the pipeline path with an error of 0.072 m on the x-axis and 0.037 m on the y-axis. Object recognition and the automation of the unmanned underwater vehicle were implemented in the Python environment. Full article

The inertial measurement method of pipelines utilizes a Micro-Electro-Mechanical Systems Inertial Measurement Unit (MIMU) to get the three-dimensional trajectory of underground pipelines. The initial attitude is significant for the inertial measurement method of pipelines. The traditional method to obtain the initial attitude uses three-axis magnetometers to measure the Earth’s magnetic field. However, the magnetic field in urban underground pipelines is intricate, which leads to the initial attitude being inaccurate. To overcome this challenge, a novel multi-position initial alignment method based on data backtracking for a single-axis FOG and a three-axis Micro-Electro-Mechanical Inertial Measurement Unit (MIMU) is proposed. Firstly, the configuration of the sensors is determined. Secondly, according to the three-point support structure of the pipeline measuring instrument, a three-position alignment scheme is designed. Additionally, an initial alignment algorithm using the data backtracking method is developed. In this algorithm, a rough initial alignment is conducted by the data from single-axis FOG, and a fine initial alignment is conducted by the data from FOG/MIMU. Finally, an experiment was conducted to validate this method. The experiment results indicate that the pitch and roll angle errors are less than 0.05°, and the azimuth angle errors are less than 0.2°. This improved the precision of the 3-D trajectory of underground pipelines. Full article

High-precision magnetometers play a crucial role in ocean exploration, geophysical prospecting, and military and security applications. Installing them on human-occupied vehicle (HOV) platforms can greatly enhance ocean exploration capabilities and efficiency. However, most existing magnetometers suffer from low sensitivity and excessively large size. This study presents a high-sensitivity, miniaturized magnetometer based on cesium optically pumped probes. The designed magnetometer utilizes a three-probe design to eliminate the detection dead zone of the cesium optically pumped probe and enable three-dimensional magnetic detection. The proposed magnetometer uses a flux gate probe to detect the three-axis magnetic field and ensure that the probe does not enter the dead zone. The three probes can automatically switch by measuring the geomagnetic elements and real-time attitude of the HOV platform. This article primarily introduces the cesium three-probe optically pump, flux gate sensor, and automatic switching scheme design of the above-mentioned magnetometer. Moreover, it is proven through testing that the core indicators, such as the accuracy and sensitivity of the cesium three-probe optically pumped and flux gate sensor, reach international standards. Finally, the effectiveness of the automatic switching scheme proposed in this study is demonstrated through drone-mounted experiments. Full article

This study introduces a robust model-based framework designed for the verification and validation (V&V) of Attitude Determination and Control Systems (ADCSs) in nanosatellites, focusing on magnetic actuation while still being applicable to larger spacecraft platforms. By employing Model-in-the-Loop (MIL), Software-in-the-Loop (SIL), Processor-in-the-Loop (PIL), and Hardware-in-the-Loop (HIL) methodologies, this framework enables a thorough and systematic approach to testing and validation. The framework facilitates the assessment of long-term maneuvers, addressing challenges such as initial small-attitude errors and restricted 3D movements. Two specific maneuvers are evaluated: detumbling and nadir pointing, utilizing quaternions and a comprehensive suite of sensors, including six sun sensors, a three-axis magnetometer, a three-axis gyroscope, GPS, and three magnetorquers. The methodologies—MIL, SIL, PIL, and HIL—integrate the behaviors of digital sensors, analog signals, and astrodynamic perturbations. Based on an optimized SIL environment, Monte Carlo simulations were performed to optimize control gains for nadir pointing, achieving a mean pointing accuracy of 11.69° (MIL) and 18.22° (PIL), and an angular velocity norm of 0.0022 rad/s for detumbling. The HIL environment demonstrated a mean pointing accuracy of 9.96° and an angular velocity norm of 0.0024 rad/s. This comprehensive framework significantly advances the design and verification processes for nanosatellite ADCSs, enhancing the reliability and performance of nanosatellite missions. Full article

In order to realize the measurement of three-axis acceleration, pressure, and magnetic field, monolithic integrated three-axis acceleration/pressure/magnetic field sensors are proposed in this paper. The proposed sensors were constructed with an acceleration sensor consisting of four L-shaped double beams, two masses, middle double-beams, and twelve piezoresistors, a pressure sensor made of a square silicon membrane, and four piezoresistors, as well as a magnetic field sensor composed of five Hall elements. COMSOL software and TCAD-Atlas software were used to simulate characteristics of integrated sensors, and analyze the working principles of the sensors in measuring acceleration, pressure, and magnetic field. The integrated sensors were fabricated by using micro-electro-mechanical systems (MEMS) technology and packaged by using inner lead bonding technology. When applying a working voltage of 5 V at room temperature, it is possible for the proposed sensors to achieve the acceleration sensitivities of 3.58 mV/g, 2.68 mV/g, and 9.45 mV/g along the x-axis, y-axis, and z-axis (through an amplifying circuit), and the sensitivities towards pressure and magnetic field are 0.28 mV/kPa and 22.44 mV/T, respectively. It is shown that the proposed sensors can measure three-axis acceleration, pressure, and magnetic field. Full article

This article presents a magnetic tracking system using on-chip anisotropic magnetoresistive (AMR) sensors. The system consists of four air-core coils sequentially generating four dc magnetic fields. The implemented localization algorithm is quadrilateration, and the accuracy of the system is dependent on the accuracy of the sensors and the simulated field maps. The performance of the system was evaluated using an in-house magnetic field camera (MFC), and the results showed that the system exhibits mean Euclidean errors below 1 mm where the source produces strong gradients. Given the dimensions of the sensors (0.82 × 0.82 mm²), this system is suitable for tracking minimally invasive surgical tools. Full article