混叠
样品(材料)
断层(地质)
计算机科学
任务(项目管理)
人工智能
样品空间
算法
数据挖掘
特征(语言学)
模式识别(心理学)
机器学习
工程类
欠采样
地质学
哲学
地震学
化学
色谱法
系统工程
语言学
作者
Fengqian Zou,Shengtian Sang,Ming Jiang,Hongliang Guo,Yan Song,Xiaoming Liu,Xiaowei Liu,Haifeng Zhang
标识
DOI:10.1016/j.isatra.2023.07.030
摘要
In recent years, pumps have become critical components in agriculture, industry, and the military, necessitating extensive development and implementation of the fault diagnosis method. In the majority of existing fault classification models, the connection between performance improvement and the amount of training data remains high, yet real-world samples are difficult to obtain. Combining domain migration theory and sample expansion method, this paper introduces a few-shot learning fault diagnosis method. Employing the T-SNE visualization algorithm, we examine the validity of the self-calibration attention mechanism (SCAM) and distribution edge prediction strategy (DEPS). The accomplishment demonstrated that the proposed algorithm could effectively map the expanded sample space within a separate interval, thereby avoiding the problem of feature aliasing caused by the overlap of sample features among similar categories and significantly enhancing the quality and quantity of training samples. The experimental analysis indicates that the proposed methodology can effectively increase the accuracy of few-shot tasks, especially in the 9way-15shot task, where it maintains a performance of 72 %, which leading the mean accuracy calculated from the others of about 30%. It is believed that much of the work has superior applicability to other few-shot diagnosis cases.
科研通智能强力驱动
Strongly Powered by AbleSci AI