Search Results (1,065)

Up/down counters are ubiquitous in the alarm and fault-confirmation logic of electro-mechanical systems. In aircraft, several electro-mechanical modules provide position feedback for flight control; the Linear Variable Differential Transformer (LVDT) is a representative one, converting mechanical displacement into an electrical signal whose reliable monitoring is critical to flight safety. As such counters are deployed in ever more complex systems and more uncertain environments, rising safety requirements render their heuristic tuning unreliable. To address this challenge, this paper proposes a quantitative, reliability-oriented procedure for counter-based monitors, which replaces heuristic parameter tuning. Both healthy and faulty signal distributions are estimated by Kernel Density Estimation (KDE), so the framework handles non-Gaussian noise and FMEA-weighted failure modes. The threshold-and-counter logic is modeled as a finite-horizon absorbing Discrete-Time Markov Chain (DTMC), which yields the false-confirmation probability, missed-detection probability, and detection delay over a bounded horizon instead of long-run rates. Thresholds and counter parameters are then synthesized offline, leaving a lightweight online monitor that needs only threshold comparison and integer counter updates. We evaluate the method on an LVDT sum-voltage monitor using real aircraft healthy measurements and Simulink-based fault injection with 45 detectable modes, assessing the synthesized monitor on $\mathrm{558,001}$ real measured samples and an FMEA-driven fault population. Results show that, among the compared confirmation logics, our workflow yields a counter that meets the ${10}^{-9}$ false-confirmation target and the ${10}^{-6}$ missed-detection target while attaining the lowest detection delay. Full article

To address the difficulty in accurately characterizing the fault current response of renewable energy grid-connected devices during high-/low-voltage ride-through, this paper proposes a fault current response modeling and parameter identification method based on adaptive nonlinear compensation. First, with the fault voltage and pre-fault operating point as input variables, a basic quadratic equivalent model is established to describe the main variation characteristics of active and reactive currents during high-/low-voltage ride-through. Second, nonlinear compensation terms are introduced into the basic model to correct the response deviation caused by the simplification of fast electromagnetic control links in the electromechanical transient equivalent process, thereby improving the representation capability of the model for complex fault current characteristics. Furthermore, considering that the structural parameters of the nonlinear compensation terms are difficult to directly identify using the traditional least squares method, a differential evolution–ridge regression (DE–Ridge) hierarchical identification method is proposed. In this method, the differential evolution algorithm is used in the outer layer to adaptively optimize the nonlinear structural parameters, while ridge regression is used in the inner layer to solve the corresponding linear coefficients. Case study results show that, compared with the traditional quadratic equivalent model and the fixed nonlinear compensation model, the proposed method further reduces the fault current identification error on the validation set and improves the identification accuracy and generalization capability of fault current responses during high-/low-voltage ride-through. Full article

This study investigates the influence of road-induced vertical excitation on air-gap eccentricity in in-wheel permanent magnet synchronous motors. A coupled electromechanical simulation framework is developed by integrating a field-oriented controlled PMSM model, a quarter-vehicle vertical dynamics model, and stochastic road roughness generated according to the ISO 8608. The proposed motor model is validated against experimental data obtained from a dynamometer test bench. Mixed eccentricity conditions are introduced to investigate how road excitation affects air-gap variation and motor current characteristics. A normalized torque-ripple current index is then extracted from the time-domain features of the q-axis current $i_q$, which can be readily calculated from the phase currents and rotor position available in conventional inverter drives without requiring additional sensors. The simulation results reveal that road excitation significantly increases the fluctuation of air-gap eccentricity and amplifies torque-ripple-related current variations compared with no-road conditions. Furthermore, the proposed index increases consistently with eccentricity severity, while rougher road profiles shift the current response toward higher abnormality levels. These findings demonstrate that road–motor coupling has a significant impact on electrical fault signatures and should be considered when developing current-based condition monitoring methods for in-wheel PMSMs. The proposed framework provides a validated basis for evaluating eccentricity faults and supports the development of robust fault diagnosis techniques under realistic vehicle operating conditions. Full article

This paper develops a reduced-order electromechanical model for piezoelectric energy harvesting from a beam traversed by a moving mass. The beam is described by the Euler–Bernoulli theory, and the coupled equations of motion are derived through modal expansion combined with the linear piezoelectric constitutive relations. Unlike most existing formulations, the model accounts for non-uniform transit by including moving-mass acceleration, accommodates an arbitrary number of piezoelectric patches distributed along the span, and incorporates von Kármán strain–displacement relations. So, moderately large deflections and mid-plane stretching as well as various boundary conditions may be investigated within the same framework. The resulting coupled nonlinear ordinary differential equations are integrated in time using a numerical solver. On the other hand, predictions of midpoint deflection, output voltage, and harvested power are validated against the COMSOL Multiphysics Finite element model. The experimental setup has been established, and a dedicated laboratory experiment provides additional verification under controlled conditions. Parametric analyses investigating the individual and combined effects of the mass ratio, velocity ratio, acceleration profile, patch length, patch position, number of patches, and external load resistance are elaborated. Distributed multi-patch configurations are shown to recover more energy than a single-centered patch once higher modes contribute appreciably to the response. Design charts relating the governing parameters to the harvested power are constructed for each set of support conditions. These results are intended to assist the preliminary sizing and placement of piezoelectric transducers on some practical energy harvesting applications. Full article

High-g accelerometers are in high demand across sectors such as aerospace, defense, and industrial inspection. This paper presents a MEMS accelerometer based on graphene piezoresistors, designed for precise acceleration measurement under sudden impacts, intense vibrations, and extreme conditions, such as engine fault diagnosis and weapon impact testing. A step-by-step structural optimization and simulation analysis were conducted using finite-element simulation. Taking the peak strain at the beam root, the first-order natural frequency, and the maximum equivalent stress as optimization objectives, progressive parametric optimization was sequentially performed on four progressive architectures: a simple beam, a beam mass, a beam mass with stress concentration grooves, and a beam mass with stress concentration grooves and symmetric masses. The results indicate that the introduction of a central mass enhances the peak strain by more than 15 times compared to the simple beam. The addition of stress concentration grooves further increases the strain by approximately 30%. Finally, the incorporation of symmetric masses yields a further 9% strain enhancement while reducing cross-axis sensitivity by 5.6%, effectively suppressing off-axis interference. The final structure achieves maximized strain while maintaining a first-order natural frequency above 200 kHz, with the maximum equivalent stress staying within the allowable limit. This optimal comprehensive performance provides essential technical support for high-performance graphene-based accelerometers. In addition to the mechanical structural optimization, the graphene piezoresistors were treated as surface sensing regions at the beam-root locations, and the area-averaged longitudinal strain was extracted as the input of a piezoresistive transduction model. The simulated strain was converted to resistance variation and bridge output voltage using a graphene gauge-factor-based readout model incorporating contact-resistance effects, thereby providing a sensor-level electromechanical performance estimation for the proposed high-g accelerometer. Full article

The startup process of gas turbines driven by the static frequency converter (SFC) exhibits complicated electromechanical coupling characteristics. Conventional simulation methods fail to integrate physical modeling with sequence of event (SOE) data and cannot support co-simulation of multiple startup schemes at the power station level. In this paper, a hierarchical digital twin architecture oriented to gas turbine SFC startup is established to realize intelligent deduction of sequential control and break through the technical limitations of traditional simulations. Relevant waveforms and data of the F-class heavy-duty gas turbine during startup are obtained via the digital twin. The maximum effective value of voltage is 12.07 kV, the maximum effective value of current is 1.6 kA, and the peak output power of the SFC reaches 15.67 MW. The system achieves the rated speed (3000 rpm) within an acceptable start-up duration, demonstrating satisfactory dynamic response. All the above data conform to the preset startup parameters and operation control logic of heavy-duty gas turbines. Full article

To address the insufficient fusion of electromechanical impedance (EMI) response features from multiple sensors and the limited characterization of spatial relationships between sensors and bolt nodes in four-bolt connection structures, this study proposes an improved graph convolutional network (GCN) model integrating Batch Normalization (BN) and Distance Weighting (DW) strategies for bolt preload-loss state identification. First, PZT sensor nodes and bolt nodes are jointly represented as a graph structure, and the correlation coefficient deviation (CCD) is extracted as the EMI response feature. Then, a weighted adjacency matrix is constructed according to the geometric distances between sensor nodes and bolt nodes to describe the spatial coupling relationships among different nodes. Subsequently, the weighted adjacency matrix and node features are input into the GCN, and a BN layer is introduced after the graph convolutional layers to reduce the influence of multi-channel feature distribution variations on model training stability. Experimental results on a four-bolt connection structure show that the proposed GCN-BN-DW model outperforms the Basic GCN, GCN-BN, GCN-DW, and several benchmark models in terms of prediction accuracy and stability. Under the strict five-fold cross-validation protocol, the proposed model achieves a test MAE of $2.400\pm 0.100$, RMSE of $3.302\pm 0.239$, MASE of $0.300\pm 0.013$, and $R^2$ of $0.821\pm 0.033$. These results indicate that the proposed model can effectively integrate multi-sensor EMI features and sensor–bolt spatial relationships, providing a feasible graph-based modeling approach for bolt preload-loss state identification. Full article

With the increasing penetration of renewable energy, smoothing wind-farm-level power fluctuations at the point of common coupling has become an important requirement for maintaining grid stability. However, the dynamic power-tracking mechanism of large-inertia slope-track solid gravity energy storage systems remains insufficiently understood. This paper presents a theoretical and control-oriented study of a slope-track solid gravity energy storage system by establishing a low-speed-branch electromechanical coupling model, deriving its steady-state power-speed characteristics, and formulating several feedforward-feedback electromagnetic torque control laws. Pure feedforward control, power feedback, speed feedback, and a gain-scheduled speed feedback law are analyzed within a unified framework. The results show that, for a given power command and under sufficient torque-control authority, increasing the moving load or slope angle enhances the gravity-driven torque and reduces the required operating speed, acceleration, and cumulative displacement. This mechanism indicates that systems with heavier loads or steeper slopes have an advantage in smoothly responding to wind-power fluctuations at a fixed power scale. Frequency-response analysis further shows that the low-speed branch is suitable for slow and medium time-scale power smoothing, whereas rapidly varying commands remain constrained by the closed-loop response time and inertial correction terms. To demonstrate a finite-track implementation of the theory, a single-track multi-unit scheme with 30 standard load units is designed and numerically tested for wind-farm-side smoothing and slow-varying command tracking. This analysis provides a theoretical basis for prototype design and parameter selection before costly full-scale experimental validation. Full article

Protein lysine lactylation is an emerging post-translational modification with broad roles in metabolic regulation. The yak (Bos grunniens) has evolved strong metabolic adaptability on the Qinghai–Tibetan Plateau, yet its tissue-specific lactylation patterns remain poorly characterized. Here, we collected liver, muscle, and heart tissues from three adult male yaks (4.5 years; 305–355 kg) and integrated quantitative proteomics with lactylomics to map lactylation profiles across these tissues. After normalizing each lactylation site to its parent protein abundance and applying Benjamini–Hochberg correction, we identified 628, 982, and 541 differentially lactylated sites (|log2FC| ≥ 0.585, adjusted p < 0.05) in liver–muscle, liver–heart, and muscle–heart comparisons, with median fold changes of 4.11, 7.08, and 3.08, corresponding to 267, 372, and 219 proteins, respectively. Subcellular localization showed that approximately 27–30% of these sites were localized to mitochondria. Functional enrichment across these comparisons consistently highlighted pathways such as the TCA cycle (fold enrichment: 1.56–2.38) and HIF-1 signaling (up to 2.69). A total of 135 proteins were common to all three comparisons, some with both up- and downregulated sites within the same tissue pair. Our results reveal decoupling between protein abundance and lactylation levels, tissue-specific lactylation patterns on the same proteins, and expression-independent lactylation of key enzymes (e.g., LDHA, SIRT3). Functional enrichment suggests lactylation serves as a multimodal regulatory mechanism coordinating energy metabolism, protein homeostasis, and electromechanical coupling across tissues. Expression profiling of lactate-metabolizing enzymes and lactylation regulators further supports an organ-specific model of post-translational regulation. Collectively, these findings detail a complex, tissue-specific lactylation network in yaks and provide insights into the metabolic homeostasis essential for high-altitude life. Full article

This paper presents a constrained robust procedure for synthesizing fixed positive input shapers for a trolley–pendulum crane that accounts for motor dynamics, actuator lag, and PD trajectory tracking. Rope length L and payload mass m are treated as uncertain parameters within a prescribed operating domain, while remaining constant during each maneuver. The method searches offline for a single impulse sequence that can be used across the entire (L,m) domain without online retuning. The impulse amplitudes and delays are obtained from a linearized closed-loop electromechanical model. Mean and worst-case peak payload sway are evaluated during synthesis, while residual RMS sway, terminal-position error, peak motor current, and peak actuator voltage are imposed as feasibility constraints. The resulting shapers are tested on a dense nonlinear validation grid and compared with nominal classical shapers, frequency-robust references, and pointwise-retuned benchmarks. The synthesized four-impulse robust shaper yields the lowest worst-case peak sway among the tested fixed shapers and satisfies all imposed feasibility limits over the validation domain. A frequency-robust reference retains a lower mean peak sway, a shorter shaping horizon, and lower drive demand. The proposed shaper is better suited to applications in which limiting the maximum payload excursion is the primary design objective. Full article

This paper addresses indirect sensing in resonant, oscillating, and periodically forced sensors, where the physical measurand is not directly available as a static output but is encoded in the dynamic response of the device. The sensor and its excitation are described as a single autonomous system, in which the excitation phase and the slowly varying measurand define a compact state representation after the decay of transients. Within this setting, delayed samples of the available output define an observation map that can be inverted, under suitable smoothness and observability conditions, to reconstruct the measurand in a deadbeat-like fashion. Compared with a preliminary conference study based on a simplified scalar-output RLC benchmark, the present work extends the formulation to vector-valued outputs, introduces a local conditioning indicator based on the Jacobian matrix, and focuses on a micromechanical sensing case with nonlinear electromechanical transduction. The inverse observation map is approximated by a feedforward neural network trained on synthetic data generated from the autonomous model. The methodology is applied to a vibratory MEMS gyroscope, where the signed angular rate is reconstructed from a delayed-output sequence combining the nonlinear capacitive current readout and the known AC drive reference. The augmented output is introduced to overcome the lack of observability affecting the raw current signal over signed angular-rate ranges. Numerical results show accurate reconstruction in ideal conditions and provide a preliminary robustness assessment under additive output noise. Full article

Acute myocardial ischemia, caused by a sudden reduction in coronary blood flow, initiates metabolic disturbances that lead to severe pathophysiological consequences. These include electrophysiological alterations, such as changes in action potential morphology and impaired electrotonic coupling between cardiomyocytes, and mechanical dysfunction, characterized by reduced contractile force and subsequent mechanical discoordination across the ventricular wall. This study employs multi-scale mathematical modeling to investigate the effects of acute ischemia on the electromechanical activity of a single human cardiomyocyte and a one-dimensional myocardial tissue. We identify the conditions for conduction block initiation and the parameters governing conduction restoration in ischemic tissue, and analyze the underlying mechanisms. Our simulations demonstrate that conduction slowing in the one-dimensional strand under ischemia directly results from the hyperkalemia-induced reduction in the fast sodium current (i_Na). This i_Na reduction is enhanced by direct electromechanical coupling and mechano-electric/mechano-calcium feedback in the mechanically and electrically interacting cardiomyocytes of the one-dimensional tissue. Under 15 min ischemia conditions, i_Na decreases to a level insufficient to sustain excitation propagation, causing conduction block. Under the conditions of this simulation, where gap junction conductance was held unchanged, the block occurred via the i_Na reduction which is itself amplified by mechano-calcium feedback. Furthermore, our model suggests a potential compensatory mechanism against conduction block in ischemic myocardium. Experimental evidence indicates that ischemia can disrupt gap junctions. A moderate reduction in the electrodiffusion coefficient along the strand, simulating reduced gap junction conductance, can convert persistent conduction block into a transient form and even eliminate it completely, facilitating the maintenance of excitation wave propagation. Full article

Global navigation satellite system (GNSS) has the characteristics of high-precision positioning, which makes it an essential part of mobile terminal positioning. In urban environments, satellite signals are easily blocked and reflected, which affects the positioning results. In this case, inertial sensors manufactured by Micro Electromechanical Systems (MEMS) technology become the key to achieving continuous positioning. Although the integration of a micro inertial system (MINS) and GNSS provides the continuous output of position information, the position error will increase with time. This paper proposes a prediction model based on an adaptive neuro-fuzzy inference system (ANFIS) and a method to obtain model parameters. The model takes the position error $\delta P^b$ of the carrier system as the output, and the rejection time (the time when GNSS positioning information remains unavailable), accelerometer data, and gyroscope data as the model inputs. Aiming at the model parameters, the consequent parameters acquisition method based on the least squares method and the antecedent parameters acquisition method based on a genetic algorithm are proposed. When GNSS outages last for 60 s, the maximum value of horizontal positioning error is reduced by 80% in six outage sections. Therefore, the proposed method in this paper is a potential method to predict the positioning error of a MINS/GNSS integrated navigation system. Full article

Osteoarthritis (OA) is a degenerative joint disease characterized by progressive loss of articular cartilage and an associated decline in its intrinsic mechanoelectrical signaling. Current osteoarthritis treatments relieve symptoms but fail to prevent cartilage degeneration or restore its native biophysical microenvironment. Here, we present an ultrasound-activated, mussel-inspired bio-adhesive hydrogel that addresses these challenges by recreating the cartilage’s piezoelectric cues in situ while achieving stable intra-articular retention under synovial conditions. The hydrogel, denoted SFHD-BT@PDA, consists of a silk fibroin (SF) matrix integrated with dopamine-functionalized hyaluronic acid (HADA) and embedded barium titanate nanoparticles coated with polydopamine (BT@PDA). This multi-level design imparts strong interfacial adhesion to wet cartilage (via catechol-mediated bonding to collagen) and piezoelectric sensitivity to external ultrasound. Under ultrasound stimulation, SFHD-BT@PDA generates localized electrical microcurrents that recruit endogenous MSCs via electrotaxis and subsequently promote their chondrogenic differentiation. In vitro, ultrasound-triggered electrical cues upregulated chondrogenic markers (SOX9, collagen II, aggrecan) in MSCs and activated TGF-β signaling, demonstrating restoration of the pro-anabolic bioelectric microenvironment. In a murine DMM model, the adhesive hydrogel exhibited prolonged retention on cartilage surfaces and, with ultrasound, induced robust cartilage regeneration and OA reversal. Treated joints showed preserved proteoglycan and Type II collagen content, inhibited osteophyte formation, and protection of subchondral bone microarchitecture. In summary, this mussel-inspired piezoelectric hydrogel provides an electromechanical stimulation platform that effectively couples physical cues with bio-adhesion to regenerate cartilage. Full article

This study proposes an integrated self-aligning-torque energy recovery and DC-bus voltage stabilization strategy for a permanent-magnet synchronous motor (PMSM)-driven steer-by-wire system in new energy vehicles. During the front-wheel return-to-center process, self-aligning torque may provide excess mechanical energy to the steering actuator. Instead of dissipating this energy through a braking resistor, the proposed strategy converts part of the self-aligning-torque-induced mechanical energy into electrical energy and feeds it back to the low-voltage DC bus. To avoid ambiguity in the operating-mode description, this paper distinguishes the standard PMSM torque–speed quadrants from the mechanical stages of the steering process. Regenerative operation is defined according to the condition ($T_e{\omega }_m<0$), corresponding to the second or fourth quadrant of the PMSM torque–speed plane, whereas the return-to-center regenerative stage refers to the self-aligning-torque-dominated stage of the steer-by-wire motion. Based on this definition, an electromechanical energy-flow model is established to describe the transfer path from self-aligning torque to the PMSM and then to the DC bus. Considering that regenerative energy injection may cause DC-bus voltage fluctuation or braking-resistor activation, a single-loop bus-voltage stabilization method based on active disturbance rejection control is developed. A third-order linear extended state observer is adopted to estimate the lumped disturbance caused by self-aligning-torque variation, current coupling, load variation, parameter uncertainty, and inverter loss. The observer bandwidth, controller gains, current limitation, and overvoltage protection mechanisms are further discussed to improve the practical implementability of the proposed control strategy. In addition, an energy-accounting method is introduced to distinguish total steering energy consumption, available self-aligning-torque mechanical energy, gross recovered electrical energy, system losses, net recovered energy, and recovery efficiency. Simulation and experimental results show that the proposed strategy can suppress DC-bus voltage rise, reduce braking-resistor energy dissipation, and achieve measurable steering-actuator-level energy recovery during repeated return-to-center maneuvers. The results verify the feasibility of using self-aligning-torque-induced regenerative energy in PMSM-driven steer-by-wire systems, while the actual vehicle-level energy benefit depends on the driving cycle, low-voltage load demand, battery charging acceptance, and converter efficiency. Full article