[FMECA and Failure Data Analysis of Domestic Wheeled Walking-Aid].

The gradual acceleration of the aging population in China has led to an increased demand for mobility aids, and the reliability of domestic wheeled walking-aid is one of their important attributes, but there is little research on the reliability of mobility aids. This paper conducts the failure mode, effects and criticality analysis on domestic wheeled walking-aid. By collecting, collating and analyzing the 26 failure modes, the key modules are the chassis and the lifting system. The key modules are obtained, and the failure data is analyzed to find out that the lifetime distribution model is log-normal and the 0.95 lower confidence limit for reliability life of product t 0.9 is 2489.4 hours. The study aims to provide ideas for the reliability analysis of other active medical devices and calls for the formation of a reliability study review point for the industry that meets the requirements of Chinese medical device regulations.

A novel machine learning-based imputation strategy for missing data in step-stress accelerated degradation test.

The presence of missing data is a significant data quality issue that negatively impacts the accuracy and reliability of data analysis. This issue is especially relevant in the context of accelerated tests, particularly for step-stress accelerated degradation tests. While missing data can occur due to objective factors or human error, high missing rate is an inevitable pattern of missing data that will occur during the conversion process of accelerated test data. This type of missing data manifests as a degradation dataset with unequal measuring intervals. Therefore, developing a more appropriate imputation method for accelerated test data is essential. In this study, we propose a novel hybrid imputation method that combines the LSSVM and RBF models to address missing data problems. A comparison is conducted between the proposed model and various traditional and machine learning imputation methods using simulation data, to justify the advantages of the proposed model over the existing methods. Finally, the proposed model is implemented on real degradation datasets of the super-luminescent diode (SLD) to validate its performance and effectiveness in dealing with missing data in step-stress accelerated degradation test. Additionally, due to the generalizability of the proposed method, it is expected to be applicable in other scenarios with high missing data rates.

ARBUR, a machine learning-based analysis system for relating behaviors and ultrasonic vocalizations of rats.

Deciphering how different behaviors and ultrasonic vocalizations (USVs) of rats interact can yield insights into the neural basis of social interaction. However, the behavior-vocalization interplay of rats remains elusive because of the challenges of relating the two communication media in complex social contexts. Here, we propose a machine learning-based analysis system (ARBUR) that can cluster without bias both non-step (continuous) and step USVs, hierarchically detect eight types of behavior of two freely behaving rats with high accuracy, and locate the vocal rat in 3-D space. ARBUR reveals that rats communicate via distinct USVs during different behaviors. Moreover, we show that ARBUR can indicate findings that are long neglected by former manual analysis, especially regarding the non-continuous USVs during easy-to-confuse social behaviors. This work could help mechanistically understand the behavior-vocalization interplay of rats and highlights the potential of machine learning algorithms in automatic animal behavioral and acoustic analysis.