基于LAMSTAR神经网络的全陶瓷轴承故障诊断

Jae Yoon, D. He, Bin Qiu
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引用次数: 13

摘要

提出了一种基于声发射传感器和大内存存储与检索(LAMSTAR)人工神经网络的全陶瓷轴承故障诊断系统。LAMSTAR是一种新开发的神经网络算法,并获得了美国专利。将该诊断系统的性能与其他类型的基于实验室种子故障测试数据的故障分类算法进行了比较。采用LAMSTAR网络的诊断系统实现了93%以上的单个故障检测准确率和96%以上的总体准确率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Full ceramic bearing fault diagnosis using LAMSTAR neural network
In this paper, an integrated full ceramic bearing fault diagnostic system developed with acoustic emission (AE) sensors and a large memory storage and retrieval (LAMSTAR) artificial neural network (ANN) is presented. LAMSTAR is a newly developed and US patented neural network algorithm. The performance of the diagnostic system is compared with those implemented with other types of fault classification algorithms using laboratory seeded fault test data. The presented diagnostic system with LAMSTAR network achieved over 93% individual fault detection accuracies along with over 96% overall accuracy.
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