Wear is one of the main factors of machine failure. If abnormal wear was not detected in time during the operation of a mechanical system, it probably leads to catastrophic consequences. The wear debris in the lubricating oil circuit contains much information about equipment wear. Consequently, debris detection is regarded as an effective way to detect mechanical faults. In this paper, an inductive debris sensor based on a high-gradient magnetic field is presented for high-throughput lubricating oil circuits. The excitation coil of the sensor is driven by a constant current to generate a high-gradient magnetic field, and the induction coil is wound around the flow path. When wear debris cuts the magnetic line through the flow path, a corresponding induced voltage is generated. The experimental results show that the sensor output signal is linear with the drive current and the wear debris velocity. In addition, the shortest distance between the particles that the sensor output signals can be completely separated is 25 mm. When the distance is smaller, the induced signals are superimposed.

To improve the wear monitoring accuracy of steel tribo-pairs, a ferromagnetic particle sensor that consists of a magnetic loop with two cylindrical magnetic poles and one magnetic housing is proposed. The induction coil is placed inside the magnetic poles to shield against external electromagnetic interference. The air gap is designed to 1 mm between the two magnetic poles, where a rotational symmetric high-gradient static magnetic field will be generated under constant direct current (dc) drive. The sine-wave-like signal of one period will be induced when one ferromagnetic particle flows through the sensor. The working principle of the sensor and the internal magnetic field distribution is explained by establishing a mathematical model and finite element analysis (FEA). Furthermore, the results about the effects of coil length and position show that the output voltage is not sensitive to the axial mounting positions of the induction coil, and a shorter inductive coil produces a larger output voltage. By the oil dynamic monitoring experiment, the sensor can distinguish 13- $\mu \text{m}$ ferromagnetic particles in an oil tube of 10-mm inner diameter. With its high sensitivity to ferromagnetic particles, the sensor is suitable for online wear monitoring of steel tribo-pairs.

Aiming at the issue of active obstacle avoidance control for intelligent vehicles under emergency, an active safety collaborative control system integrated with adaptive cruise control (ACC), rear steering control (RSC), and rollover braking control (RBC) is developed. The proposed novel control architecture consists of a supervisor, an upper controller, and a lower controller. The supervisor decides which control mode to be used based on multi-sensors information of the driving vehicle. Active rear wheel steering based on lateral acceleration feedback and active braking control strategies based on fuzzy PID are added into the control model to enhance driving stability. To make the vehicle motion control model suitable for highway path tracking with obstacle avoidance under emergency, a priority is given to the RBC controller even though the vehicle speed is lower than the expected speed when the rollover index exceeds the safety threshold. A nonlinear vehicle simulation model is established to validate the proposed control scheme based on Carsim. Finally, a Carsim-Simulink co-simulation model is constructed, and simulation results show that the proposed integrated control strategy based on active safety collaborative control has good performance in both path tracking and driving stability, and has better stability of rollover under emergency.

In this study, we successfully prepared core–shell heterostructured nanocomposites (Fe NWs@SiO₂), with ferromagnetic nanowires (Fe NWs) as the core and silica (SiO₂) as the shell. The composites exhibited enhanced electromagnetic wave absorption and oxidation resistance and were synthesized using a simple liquid-phase hydrolysis reaction. We tested and analyzed the microwave absorption properties of Fe NWs@SiO₂ composites with varied filling rates (mass fractions of 10 wt%, 30 wt%, and 50 wt% after mixing with paraffin). The results showed that the sample filled with 50 wt% had the best comprehensive performance. At the matching thickness of 7.25 mm, the minimum reflection loss (RL_min) could reach −54.88 dB at 13.52 GHz and the effective absorption bandwidth (EAB, RL < −10 dB) could reach 2.88 GHz in the range of 8.96–17.12 GHz. Enhanced microwave absorption performance of the core–shell structured Fe NWs@SiO₂ composites could be attributed to the magnetic loss of the composite, the core–shell heterogeneous interface polarization effect, and the small-scale effect induced by the one-dimensional structure. Theoretically, this research provided Fe NWs@SiO₂ composites with highly absorbent and antioxidant core–shell structures for future practical applications.

The detection of wear condition is critical for the prediction of mechanical equipment failure and the avoidance of catastrophic accidents. A large-diameter inductive debris detection sensor was previously presented to satisfy the requirement of high throughput, and the characteristics of induced voltage generated by single debris were analyzed. However, in practice, plenty of wear particles carried by lubricant flowing through the sensors probably form the aliasing signal, thus affecting the evaluation of concentration and size of particles. In order to analyze the mechanism of aliasing, this paper studies the influence of the motion state of wear debris, such as velocity, relative spatial position and trajectory, on the waveform of the aliasing induction signal. The experimental results show that the aliasing induced voltage is proportional to the debris velocity and magnetic induction intensity. Additionally, the voltage peak-peak of aliasing signal is approximately proportional to the radius of the debris instead of the volume, from which the relational expression about the aliasing signal and component singles derives. Finally, the theoretical results and experimental results are performed to validate this relation.