In the pulse eddy current thermal imaging experiments, the trends of temperature response curves of buried defects at different depths are same, but there are differences in cooling rates. The accuracy of depth quantification of buried defects can be improved by making full use of the rich information contained in the temperature response curves. To this end, a feature extraction, defect segmentation and depth quantification algorithm named curves-based similarity method is proposed in this paper. By making comprehensive use of the global and local features of thermal image sequences, the average similarity of the temperature response curve is used as the feature extraction method and quantification parameter. The effectiveness of the method is verified by simulation and experiment. The results show that the method can better enhance the defect information and suppress the noise compared with PCA (Principal Component Analysis) and PPT (Pulsed Phase Thermography). Additionally, the average error of the quantization results of the algorithm is reduced by 1.33% compared with the characteristic time quantization method.

As the core sensor of inertial measurement units, the reliability of Micro-Electro-Mechanical Systems (MEMS) gyroscopes is critical for long-term navigation and motion control applications. To bridge the mechanism-data gap in MEMS multi-mechanism degradation modeling, this paper proposes a physics-informed dual-indicator reliability assessment framework based on Wiener processes. Two degradation indicators under consideration are frequency-related degradation caused by stiffness degradation and Q-factor degradation caused by damping degradation, for which corresponding physics-embedded stochastic degradation models are formulated. The two indicators are normalized and fused through a generalized weighted limit state function, where failure is defined as gyroscope-level performance failure. Closed-form reliability expressions are derived for linear limit states, while Monte Carlo simulation is used for nonlinear cases. Reduced-order multiphysics simulation cases, including a double-ended fixed beam and a cantilevered MEMS mass block, are used to demonstrate the mechanism-to-indicator-to-reliability modeling procedure. The results show that the proposed dual-indicator framework provides more balanced reliability assessment than single-indicator analysis under the simulation setting. The proposed method offers an alternative mechanism-informed approach for reliability analysis and lifetime prediction of other MEMS devices.

Data-driven fault diagnosis is a method widely used in the maintenance and support of radio frequency (RF) front-end. Its effectiveness depends on a large volume of high-quality training data. However, due to the low probability and uneven occurrence of faults, quantity of fault data is often less than the normal data. This data imbalanced problem greatly affects the accuracy of fault diagnosis for RF front-end. Data generation is an important way to address this problem, including generative adversarial networks (GAN), variational autoencoders (VAE), and denoising diffusion probabilistic models (DDPM). However, these methods suffer from issues such as unstable training, mode collapse, and uncontrollable generation. To solve these problems, this article proposed an adaptive conditional DDPM (Ada-CDDPM) based on improved U-Net3+, aiming to enable the model to better extract features of RF fault data and then guide it to generate the modal data required for RF system diagnosis. More specifically, this article proposed an adaptive CDDPM, which uses a conditional function with learnable coefficients vector to guide direction of training data generation. A differentiable loss function is proposed by this article to evaluate the quality of the generated training data. The learnable coefficient vector is optimized adaptively based on the evaluation results, enabling the final generated data to have good fidelity, diversity, and modal characteristics. This article constructed a simulation platform and an experimental platform to test the performance of the method. Comprehensive experiments show that data generated by the proposed method has significant advantages in diversity, fidelity, and is effective in solving RF front-end diagnosis problem under imbalanced data.