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  • Extraction of rotor unbalance characteristics from non-stationary vibration signals during speed fluctuations
    Speed fluctuations introduce pronounced non-stationarity into rotor vibration signals, which makes the accurate extraction of rotational-frequency, amplitude, and phase characteristics considerably more difficult than under steady-speed conditions. To address this problem, a rotor-unbalance characteristic-extraction method is developed for speed-fluctuation conditions. The procedure first evaluates supervised normalized least mean square (NLMS) filtering followed by fixed forward–backward zero-phase filtering for noise suppression without additional phase distortion. The multisynchrosqueezing transform (MSST) is then applied to improve time–frequency energy concentration and track the instantaneous rotational-frequency component of the non-stationary vibration signal. The instantaneous phase is subsequently resampled using the keyphasor signal, and phase tracking regression (PTR) is applied to estimate the rotational-frequency phase under speed fluctuation and keyphasor jitter. Simulation results show that the combined NLMS and zero-phase filtering method achieves the highest output signal-to-noise ratio and the lowest root mean square error among the compared filtering methods. MSST provides better time–frequency energy concentration than the other investigated time–frequency methods, while PTR maintains relatively stable phase-estimation performance under variations in noise level, keyphasor jitter, frequency modulation ratio, and sampling duration. Experiments on the investigated three-disk rotor further show that PTR yields a phase standard deviation of 4.363° across the segmented steady-state measurements, and the vibration amplitudes at all three measurement locations are reduced by more than 70% in both balancing tests at 5000 rpm, demonstrating the effectiveness of the proposed method for the present rotor configuration and tested operating condition.

  • Robust synchronization of cardiac-linked biosensors under peak detection failures: a modality-agnostic approach via structural repair and Huber-IRLS regression
    Cooperative sensing in clinical instruments, distributed medical devices, and body-area networks requires accurate relative timing between independently clocked sensors, such as electrocardiography (ECG) and photoplethysmography (PPG). Interval-only synchronization operates directly on inter-beat intervals and avoids waveform alignment, but conventional estimators generally assume one-to-one reference–device beat correspondence. Missed beats, motion-induced false peaks, and gross interval errors violate this assumption by creating merged or split intervals and index shifts. We propose a robust interval-only synchronization pipeline comprising: (i) Dynamic Repair of abnormally short split intervals caused by extra (false) peaks; (ii) greedy peak-failure-aware mapping with to represent isolated missed beats as merged intervals; and (iii) Huber-iteratively reweighted least squares regression with median absolute deviation-based residual gating. Approximate uncertainty is quantified using an OLS-style Newey–West HAC covariance estimate (approximate and conditional when gating) and Fieller confidence sets for the ratio-defined delay-dynamics parameter. Monte Carlo ablation on synthetic ECG/PPG data (ORACLE and SIGNAL pipelines) shows that structural peak-detection failures can be catastrophic for OLS (e.g. under Extra 10% and under a mixed extreme regime). In contrast, the proposed pipeline maintains robustness; in the mixed extreme regime it achieves (SIGNAL) and ≈35.9 ms (ORACLE). The real-data experiment was performed as a semi-synthetic feasibility validation using matched ECG/PPG beats from a real recording, with known clock skew imposed on the PPG timestamps and controlled peak failures injected into the resulting device interval sequence.

  • FBG-based 3D shape reconstruction of flexible rods using multi-source error compensation and sparrow search optimization
    Three-dimensional (3D) shape reconstruction of flexible rods using fiber Bragg grating (FBG) sensors is susceptible to temperature drift, strain transfer loss, and fiber installation angle deviations, which introduce curvature errors that accumulate along the rod length and ultimately degrade reconstruction fidelity. This paper proposes a 3D shape reconstruction framework that integrates temperature compensation, strain transfer correction, and sparrow search algorithm (SSA)-based angle optimization for a Nitinol flexible rod sensor with three optical fibers symmetrically arranged at 120°. A temperature–strain–curvature decoupled model is established to eliminate curvature errors induced by temperature drift and strain transfer loss. Subsequently, a curvature–bending direction correction model is constructed, in which SSA is employed to globally optimize the circumferential installation angle deviations and calibration direction deviations, yielding more accurate curvature estimates. 3D shape reconstruction is then achieved through Frenet–Serret frame numerical integration of the corrected curvatures. Experiments on C-shaped, S-shaped, and Ω-shaped rods demonstrate that the proposed framework reduces the root mean square relative error from 9.76%, 9.59%, and 10.34% to 1.66%, 1.05%, and 2.99%, respectively. These results confirm that the framework effectively suppresses multi-source error accumulation and substantially improves FBG-based 3D shape reconstruction accuracy for flexible rods.

  • An availability-aware multi-domain measurement filter for antenna-array GNSS/INS under concurrent multipath and distributed spoofing
    Multipath and spoofing are difficult to separate when both appear as biased range measurements. We develop a Multi-Domain Resilient Navigation Filter (MDRNF) for an antenna-array Global Navigation Satellite System/Inertial Navigation System deeply integrated receiver. For each satellite, the filter uses navigation innovations, inter-antenna carrier-phase residuals and tracking-loop features. The array model predicts the direct path, so a reflected component is allowed to disturb the spatial residual. Each available domain is described by a normalized scale-chi-square working density; the temporal–spectral coupling term is used only as a generalized-Bayesian regularizer. Missing spatial evidence is left out of the update instead of being filled with a nominal value. The resulting hypothesis weights adjust pseudorange and Doppler covariances, isolate a channel when spoofing evidence is strong, and activate a conditional pseudorange-bias tracker. The receiver is evaluated in a 300 s MATLAB software-in-the-loop scenario with five antenna elements and eight GPS L1 C/A satellites. The scenario covers nominal tracking, satellite-dependent multipath, three distributed spoofers and recovery. An 80-run intermediate-frequency (IF) signal-level Monte Carlo simulation is used, with the IF signal and downstream receiver processing regenerated independently for every run. During concurrent multipath and spoofing, the pooled 3D position root-mean-square error is 3.933 m for MDRNF, compared with 18.684 m for an array-based resilient filter and 21.119 m for a standard deeply integrated extended Kalman filter. These results describe the variation across the 80 independently generated IF signal-level realizations under the specified scenario.

  • Lightweight defect detection on mica sheets via focused distillation for industrial measurement
    To address the dual demands of accuracy and efficiency in detecting irregular surface defects on mica sheets, this paper proposes a comprehensive solution that integrates the design of a vision system and a lightweight detection method. First, we build a high-precision automated vision acquisition system to capture mica sheet images in batches and construct a dataset based on the defect characteristics. Next, we build a mica sheet defect detector using Faster Region-based Convolutional Neural Network, which achieves accurate classification and localization of defects after training on the custom dataset. Finally, we introduce a knowledge distillation framework based on focused features and responses, transferring the feature extraction and response evaluation capabilities from a high-accuracy, large-capacity model to a lightweight model. Extensive experimental results demonstrate the effectiveness of the proposed method. The resulting lightweight model achieves 30.8% mean Average Precision with only 28.1 M parameters, striking a favorable balance between detection accuracy and efficiency. This provides reliable support for the insulation safety risk assessment of mica sheets.