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.

Temperature sensor systems are critical for nuclear power plant (NPP) condition monitoring, whose reliability underpins unit safety and stability. Fault localization and diagnosis are essential to sustain their stable service. Conventional Principal Component Analysis (PCA) and Graph Neural Network (GNN) methods suffer clear drawbacks: PCA is vulnerable to noise and cannot classify fault types accurately, while GNNs struggle to quantify correlations among temperature data. This paper fuses PCA’s anomaly representation capability and GNN’s structural feature extraction capacity to propose an Abnormal Feature-GCN method for joint fault localization and diagnosis. First, an Adaptive PCA (APCA) model fed with multi-dimensional sensor features computes abnormal features. These features are then transformed into edge weights to construct a weighted graph. A dual-branch GCN is finally trained via a joint loss function for parallel multi-task learning to simultaneously locate faulty sensors and identify fault types. Validated on a nuclear primary circuit temperature sensor system under constant-, rising-, and falling-temperature working conditions, the proposed method realizes accurate fault localization and classification. The mean overall accuracy of the proposed method surpasses mainstream baselines by 2.23%, 1.62%, and 1.89% for the three typical working conditions, respectively.