The metal wear debris in mechanical transmission can feedback the characteristics of mechanical equipment failure, and its online monitoring efficacy directly affects the operating performance of mechanical equipment. Specific to the need for online fault detection of the mechanical transmission system, a double-coil reverse excitation method is adopted to provide a magnetic field environment for metal wear debris testing in the lubricating oil circuit of the mechanical transmission system. Based on the magnetic field distribution model of a single-coil solenoids, a coupling magnetic field distribution model of reverse-symmetric double-coil magnetic excitation is established. On the basis of detecting the coils to obtain the signals of magnetic field disturbance when metal water debris passing through the magnetic excitation field, an online monitoring sensor for metal wear debris with a reverse-symmetric double-coil magnetic excitation balance magnetic field is developed. Taking the monitoring of metal wear debris in a large-aperture flow channel as an example, a semi-physical simulation test has been carried out on metal wear debris in different grain diameters and textures, whose experimental results indicate that this sensor can detect $100 ~\mu \text{m}$ ferromagnetic metal and $1000 ~\mu \text{m}$ non-ferromagnetic metal in an 8 mm flow channel, which, as a detection sensor for metal wear debris, can also be used for metal wear debris monitoring in the mechanical lubricants of mechanical transmission and engines.

Aiming at the problem of the failure of the global positioning system in the satellite denied environment, and facing the needs of the navigation and positioning in indoor space, the technology of indoor magnetic field vector positioning and navigation have been studied. Based on the geomagnetic matching technology, the wavelet analysis method is used to estimate the noise components and statistical characteristics of the magnetic field three-dimensional signal; combined with the Kalman filtering algorithm, through estimating and optimizing the magnetic field vector signal, an indoor magnetic positioning signal processing algorithm with wavelet analysis and Kalman fusion is established, realizing the optimization of the measurement accuracy of the geomagnetic signal and the accuracy of the fingerprint map. The experimental results indicate that the signal-to-noise ratio of the test sequenceis increasedby 3db, the rootmean square error is reducedby about30%, and themaximumpositioning error reaches 1.34m, which can meet the requirements of indoor positioning.

Bionic polarized light navigation is a new navigation method that uses atmospheric polarization patterns to obtain compass information. It has been extensively studied in the fields of autonomous navigation and integrated navigation in recent years. However, studies have found that polarized light navigation is susceptible to occlusion interference when navigating in complex environments, which will lead to the loss of atmospheric polarization information and severely reduce navigation accuracy. Therefore, this paper presents an orientation method for inpainting atmospheric polarization patterns, employing an enhanced U-Net convolutional neural network combined with sample block matching. To address the issue of error accumulation during the restoration process, an image integrity evaluation index based on visual coherence and visual saliency is utilized as a constraint to guide the restoration. The outdoor experimental results demonstrate that the proposed method effectively reduces the heading angle error from 7.3505° to 1.6619° when multiple occlusions, including branches, clouds, and buildings, are present simultaneously. This corresponds to an approximately 77.3% improvement compared to the error before occlusion removal. These findings underscore the ability of the method outlined in this paper to obtain relatively accurate navigation information, even in cases where the atmospheric polarization mode is disrupted.

Specific to the full-range detection demand for the magnetic anomaly targets such as metal in shallow strata, transportation pipelines, and mined-out regions during the geophysical survey, based on the transient electromagnetic method, using the Gauss inverse Laplace transform, a magnetic excitation model for metal targets in medium-shallow strata is proposed. By using the adjustment of the electrical parameters such as frequency, amplitude, and phase of the excitation current, a spatial multimode magnetic excitation field is formed, an array magnetic excitation detection device is developed, and a distribution model for the array magnetic excitation field is established. The hardware-in-the-loop simulation results show that the frequency and amplitude of the excitation signal are directly proportional to the spectral amplitude and the induced amplitude of the secondary field of the signal, where the signal has the most spectral components when selecting a frequency of 25 Hz. The duty cycle of the excitation signal is of nonlinear correlation to the magnitude of the signal frequency spectrum. When the duty cycle is 50%, the signal spectral components are the least and concentrated in the part with lower frequency, and when the duty cycle is lower or higher than 50%, the spectral components increase and spread to the middle and high frequencies. The indoor test results indicate that changing the excitation signal parameters can also increase the detection range and depth to a certain extent without changing the sensor parameters. It can also reduce the detection blind area to a certain extent. Therefore, the conclusion of this article can solve the problem of large detection blind areas in transient electromagnetic detection, and the application field can also be extended from deep mineral detection to new areas, such as medium and shallow unexploded bombs, metal pipelines, and goafs.

Purpose: Modeling and analysis of dimensional variation propagation is a crucial support technology for variation reduction, product/process design evaluation and recognition of variation source. However, owing to the multi-deviation (i.e. part deviations and fixture deviations) and multi-interaction (i.e. part-to-part interaction, part-to-fixture interaction and station-to-station interaction) in assembly processes, it is difficult for designers to describe or understand the variation propagation (or accumulation) mechanism clearly. The purpose of this paper is to propose a variation propagation modeling and analysis (VPMA) method based on multiple constraints aiming at a single station. Design/methodology/approach: Initially, part-to-part constraints (PPCs) and part-to-fixture constraints (PFCs) are applied for the multi-interaction of assembly, and multiple constraints graph (MCG) model is proposed for expressing PPCs, PFCs, parts, as well as the variation propagation relation among them. Then, locating points (LPs) are adopted for representing the deviations in constraints, and formulas for calculating the deviations of LPs are derived. On that basis, a linearized relation between LPs’ deviations and part’s locating deviations is derived. Finally, a wing box is presented to validate the proposed method, and the results indicate the methodology’s feasibility. Findings: MCG is an effective tool for dimensional VPMA, which is shown as an example of this paper. Originality/value: Functions of geometric constraints in dimensional variation propagation are revealed, and MCG is proposed to formulize dimensional variation propagation.

A hi-speed rotation angular rate sensor based on an electromagnetic induction signal is proposed to provide a possibility of wide range measurement of high angular rates. An angular rate sensor is designed that works on the principle of electromagnetism (EM) induction. In addition to a zero-phase detection technique, this sensor uses the feedback principle of magnetic induction coils in response to a rotating magnetic field. It solves the challenge of designing an angular rate sensor that is suitable for both low and high rotating rates. The sensor was examined for angular rate measurement accuracy in simulation tests using a rotary table. The results show that it is capable of measuring angular rates ranging from 1 rps to 100 rps, with an error within 1.8‰ of the full scale (FS). The proposed sensor is suitable to measurement applications where the rotation angular rate is widely varied, and it contributes to design technology advancements of real-time sensors measuring angular acceleration, angular rate, and angular displacement of hi-speed rotary objects.