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Browse AllReal-time monitoring of metal wear debris in lubricating oil is crucial for accurately assessing aerospace equipment health and preventing catastrophic mechanical failures. Although three-coil differential inductive sensors are widely used due to their high sensitivity and robust structure, their detection performance inevitably degrades under the influence of multi-physics coupling effects inherent in complex industrial environments. Existing analytical models were primarily limited to single electromagnetic field simulations, failing to capture the intricate electromagnetic-thermal-mechanical interactions, which reduced analytical accuracy and hindered the optimization of sensor designs for extreme conditions. To overcome these challenges, this study established a comprehensive parametric simulation model to conduct a rigorous electromagnetic-thermal-mechanical coupled analysis of the differential sensor. Specifically, the research analyzed the underlying mechanisms by which wide temperature variations from -55 °C to 200 °C and specific debris kinematics exert varying degrees of influence on the induced voltage. Quantitative results indicated that both the isolated thermal and mechanical fields altered the voltage by a maximum of 0.649%, demonstrating that the sensor maintains a relatively stable response across extreme thermal gradients. Furthermore, the analysis revealed that increased debris velocity decreased the induced voltage amplitude due to the intensified motion-induced eddy current effects. Notably, in complex multiple debris scenarios, the strong dominant signals generated by ferromagnetic wear debris severely masked the weaker responses from non-ferromagnetic ones, indicating a substantial missed detection risk under mixed contamination conditions. Ultimately, this research successfully filled the critical simulation gap for evaluating these sensors under multi-physics coupling, providing robust theoretical guidance for their reliable deployment in complex industrial applications.
To satisfy the real-time beam tracking requirement of low earth orbit (LEO) satellite ground terminals under highly dynamic pass conditions, this paper proposes a three-state hierarchical state-machine beam-control algorithm for liquid crystal phased array antennas (LCPAs). The tracking process is divided into three stages. S1 coarse nine-point acquisition is used to rapidly cover a wide pointing-uncertainty region. S2 fine nine-point refinement introduces quadratic-surface fitting to obtain local peak estimation at sub-step resolution. S3 predictive tracking adopts an interacting multiple model Kalman filter (IMM-KF) based on the constant-acceleration (CA) model and the Singer model, and designs an adaptive covariance regulation mechanism for both measurement and process noise. Combined with explicit delay compensation for liquid-crystal response time, the method suppresses pointing lag and error accumulation in high-dynamic intervals. A dual-threshold hysteretic loss-of-lock criterion and a filter-reset mechanism are also integrated to guarantee autonomous link recovery. Simulation results show that, in an LEO pass scenario with a maximum angular rate of about 2 °/s, the proposed method reduces the median 2D RMSE by about 52%, reduces the 90% quantile error by about 54%, and lowers the median average power loss from 0.14 dB to 0.03 dB compared with the traditional scheme.
The demand for high-precision, real-time, and continuous localization in aerospace and low-altitude unmanned systems is steadily increasing. However, under weak GNSS availability, obstruction, and degraded ranging geometry, conventional 3D localization methods often suffer from insufficient observation constraints and severe accuracy degradation. To address observability degradation and localization error divergence in single-auxiliary-node systems, this paper proposes a 3D cooperative localization method based on maneuver reconfiguration of a single auxiliary node. Within a particle filtering framework, the method fuses target motion increments with single-shot range measurements to recursively estimate the target’s 3D position and heading. A geometric degradation metric and a radial-consistency metric are further constructed from the line-of-sight distribution and the alignment between target velocity and line of sight within a sliding window, enabling online detection of degraded observability. Once degradation is detected, the auxiliary node performs either lateral maneuvering or circular maneuvering to actively reshape the target–auxiliary relative geometry and restore observation diversity; meanwhile, the two metrics can also serve as feedback variables for adaptive maneuver adjustment. Comparative analyses are conducted under three modes, namely no reconfiguration, lateral maneuvering, and circular maneuvering, from the perspectives of 3D trajectory evolution, error time series, degradation-metric time series, and statistical error performance. Parameter sensitivity analyses and an additional nonlinear velocity-varying scenario are also included. Experimental results show that the no-reconfiguration mode exhibits clear error divergence under degraded conditions, whereas both maneuver-based strategies effectively reduce localization errors. Lateral maneuvering is more suitable for rapid recovery in the early degradation stage, while circular maneuvering achieves better overall accuracy and stronger post-degradation recovery capability. These results demonstrate the effectiveness and engineering feasibility of degradation-triggered active geometric reconfiguration for cooperative localization in low-configuration unmanned systems.