标题：Life prediction of the tensile damage progress for high-speed train gearbox shell based on acoustic emission sensor and an automatic optimization method
期刊名称：International Journal of Distributed Sensor Networks
印刷版ISSN：1550-1329
电子版ISSN：1550-1477
出版年度：2022
卷号：18
期号：6
页码：1-9
DOI：10.1177/1550147718781455
语种：English
出版社：Hindawi Publishing Corporation
摘要：The high-speed train gearbox is one of the key components of the train system, and its working state is related to passengers’ safety. The gearbox shell is the protective shell for gears under harsh and complex service environment. Instantaneous impact of hard material during failure can lead to the rapid damage accumulation and fracture of the shell material. In this article, the accumulation of tensile damage of the high-speed train gearbox shell material has been studied. For the short tensile process leading to significant amount of damage, a real-time and non-destructive detection method has been used to monitor the tensile damage progression and predict the residual life of the material. An automatic optimization algorithm has been proposed to deal with data quality problems such as fluctuations, imbalances, and large intervals. In addition, an automatic life prediction optimization model for material tensile process has been established. The life prediction errors are controlled within 50 s, and the majority of errors are less than 20 s. For the accelerated test, in respect to real situation, a 6 h time slot is designed for disaster control such as passenger evacuation and train failure prevention.
