Blade tip clearance (BTC) measurement and active clearance control (ACC) are becoming crucial technologies in aero-engine health monitoring so as to improve the efficiency and reliability as well as to ensure timely maintenance. Eddy current sensor (ECS) offers an attractive option for BTC measurement due to its robustness, whereas current approaches have not considered two issues sufficiently. One is that BTC affects the response time of a measurement loop, the other is that ECS signal decays with increasing speed. This paper proposes a speed adjustment model (SAM) to deal with these issues in detail. SAM is trained using a nonlinear regression method from a dynamic training data set obtained by an experiment. The Levenberg–Marquardt (LM) algorithm is used to estimate SAM characteristic parameters. The quantitative relationship between the response time of ECS measurement loop and BTC, as well as the output signal and speed are obtained. A BTC measurement method (BTCMM) based on the SAM is proposed and a geometric constraint equation is constructed to assess the accuracy of BTC measurement. Experiment on a real-time BTC measurement during the running process for a micro turbojet engine is conducted to validate the BTCMM. It is desirable and significative to effectively improve BTC measurement accuracy and expand the range of applicable engine speed.

Bolt looseness detection is critical in preventing bolt connection failure. Compared to traditional sensor-based bolt looseness detection, image-based methods are low-cost and contactless and have thus become the highlight of research. However, current monocular vision-based detection methods are prone to error scaused by the camera perspective . In this paper, we present a novel bolt loosening angle detection method based on binocular vision. Key points on the bolt are detected and matched by SuperPoint Gauss network for 3D coordinates reconstruction and motion tracking. The bolt loosening angle is solved by fitting the rotation equation using random sample consensus. Experiments verify the proposed method performs well under different perspectives of camera and illumination conditions with an average error of 1.5°. Comparative test shows our method is superior to the monocular vision-based method in terms of accuracy when there is a large perspective angle. The proposed method is mark-free and robust to various working conditions, which makes it of great value for engineering application.

Most subdomain models do not solve the magnetic vector potential in the iron part of the machine, this paper proposes a complete subdomain model for (PM) machines with toroidal windings and mainly solves the magnetic vector potential in iron part. The proposed model can accurately predict the field distribution under load or no-load conditions. The electromagnetic parameters of the motor (including magnetic flux density, back-emf, electromagnetic torque, and cogging torque) are calculated using the proposed analytical model, and validated by a FEM model. This paper presents a technique to handle with the stator of machines with outer teeth and the very specific techniques to solve the magnetic field in the iron part.

The recycling of garbage is an effective way to protect environment. However, garbage recycling is still adopting manual sorting which is of low efficiency and may damage to the health of operators. To improve the efficiency of the garbage recycling, it is considered to use an automated robot system which integrates modern control method of robots and machine vision to replace manual sorting. Using a robotic arm and a RGB-D camera, a visual servo robotic arm grasping system was produced, which mainly depends on tools in robot operating system (ROS). The object recognition kitchen (ORK) in ROS is used to identify and locate the grasping targets. The relationship between the camera coordinate system and the robot coordinate system is established by hand-eye calibration technology. The kinematics calculation and motion planning of the robotic arm are completed with the help of ROS-Moveit!. Through experimental testing, it is estimated that the hand-eye calibration is accurate, and the robotic arm can perfectly grasp the target object.

Modal identification and extraction of monitoring signals are of great significance for aero-engine condition monitoring and health management. Empirical mode decomposition (EMD) method and variational mode decomposition (VMD) method have wide applications in time–frequency signal analysis, but EMD method is very sensitive to noise and sampling frequency. The accuracy of the VMD method relies on the input parameter settings. The dynamic mode decomposition (DMD) method is considered as a high-dimensional data-driven method which can predict the short-term behavior. However, the one-dimensional data decomposed by the DMD method only contains one mode. In this paper, the DMD method based on Hankel reconstruction (HDMD) is applied to the signal sequence to investigate its capability and potential as a signal processing method. First, multi-frequency, similar-frequency, and variable-frequency-variable-amplitude signals are constructed and resolved by various methods. It is shown that the HDMD method is more robust to noise, has a higher frequency resolution, and is capable of uncovering potential frequency trends compared to the EMD method and the VMD method. In addition, this paper applies the HDMD method to identify the measured signals of the aero-engine. It is shown that the HDMD method can accurately decompose the frequency modes of the core components. Compared with the EMD and VMD methods, the HDMD method can reveal the pattern of the engine frequency when it varies within a small range, which is more applicable to the analysis of the actual signals.