Bayesian Averaging Evaluation Method of Accelerated Degradation Testing Considering Model Uncertainty Based on Relative Entropy.

To evaluate the lifetime and reliability of long-life, high-reliability products under limited resources, accelerated degradation testing (ADT) technology has been widely applied. Furthermore, the Bayesian evaluation method for ADT can comprehensively utilize historical information and overcome the limitations caused by small sample sizes, garnering significant attention from scholars. However, the traditional ADT Bayesian evaluation method has inherent shortcomings and limitations. Due to the constraints of small samples and an incomplete understanding of degradation mechanisms or accelerated mechanisms, the selected evaluation model may be inaccurate, leading to potentially inaccurate evaluation results. Therefore, describing and quantifying the impact of model uncertainty on evaluation results is a challenging issue that urgently needs resolution in the theoretical research of ADT Bayesian methods. This article addresses the issue of model uncertainty in the ADT Bayesian evaluation process. It analyzes the modeling process of ADT Bayesian and proposes a new model averaging evaluation method for ADT Bayesian based on relative entropy, which, to a certain extent, can resolve the issue of evaluation inaccuracy caused by model selection uncertainty. This study holds certain theoretical and engineering application value for conducting ADT Bayesian evaluation under model uncertainty.

Flight test results for microgravity active vibration isolation system on-board Chinese Space Station.

The Fluid Physics Research Rack (FPR) is a research platform employed on-board the Chinese Space Station for conducting microgravity fluid physics experiments. The research platform includes the Microgravity Active Vibration Isolation System (MAVIS) for isolating the FPR from disturbances arising from the space station itself. The MAVIS is a structural platform consisting of a stator and floater that are monitored and controlled with non-contact electromagnetic actuators, high-precision accelerometers, and displacement transducers. The stator is fixed to the FPR, while the floater serves as a vibration isolation platform supporting payloads, and is connected with the stator only with umbilicals that mainly comprise power and data cables. The controller was designed with a correction for the umbilical stiffness to minimize the effect of the umbilicals on the vibration isolation performance of the MAVIS. In-orbit test results of the FPR demonstrate that the MAVIS was able to achieve a microgravity level of 1-30 µg0 (where g0 = 9.80665 m ∙ s-2) in the frequency range of 0.01-125 Hz under the microgravity mode, and disturbances with a frequency greater than 2 Hz are attenuated by more than 10-fold. Under the vibration excitation mode, the MAVIS generated a minimum vibration acceleration of 0.4091 µg0 at a frequency of 0.00995 Hz and a maximum acceleration of 6253 µg0 at a frequency of 9.999 Hz. Therefore, the MAVIS provides a highly stable environment for conducting microgravity experiments, and promotes the development of microgravity fluid physics.

Similarity-Based Remaining Useful Lifetime Prediction Method Considering Epistemic Uncertainty.

Measuring the similarity between two trajectories is fundamental and essential for the similarity-based remaining useful life (RUL) prediction. Most previous methods do not adequately account for the epistemic uncertainty caused by asynchronous sampling, while others have strong assumption constraints, such as limiting the positional deviation of sampling points to a fixed threshold, which biases the results considerably. To address the issue, an uncertain ellipse model based on the uncertain theory is proposed to model the location of sampling points as an observation drawn from an uncertain distribution. Based on this, we propose a novel and effective similarity measure metric for any two degradation trajectories. Then, the Stacked Denoising Autoencoder (SDA) model is proposed for RUL prediction, in which the models can be first trained on the most similar degradation data and then fine-tuned by the target dataset. Experimental results show that the predictive performance of the new method is superior to prior methods based on edit distance on real sequence (EDR), longest common subsequence (LCSS), or dynamic time warping (DTW) and is more robust at different sampling rates.