摘要
因为Spiking神经网络(Spiking neural networks,SNNs)能同时传递时空信息,SNNs包含优于传统神经网络的许多特性,因而更适用于动态时序信号的分析。碰撞和受阻是机械臂在靠近抓取位置时常见的两种故障。为区别此两种故障状态与正常工作状态,提出一种基于SNNs的新型机械臂故障诊断方法。讨论所提出的SNNs故障诊断方法的体系结构,比较了当SNNs故障诊断方法选用不同Spiking神经网络拓扑结构和不同参数时的诊断结果。试验结果表明所提出的基于Spiking神经网络的机械臂故障诊断方法是有效的。该方法有助于机械臂故障的正确诊断,并且对平稳安全的生产具有重要意义。
Because spiking neural networks （SNNs） could convey both temporal and spatial information at the same time, and contain features that were more attractive than those of traditional neural networks （ NNs）, SNNs were more suitable for analyzing the dynamic time-series signals. A novel fault diagnosis method based on SNNs was proposed to distinguish manipulators＇ collision and obstruction failure states from the normal working state,as manipulators approaching the grasping position. The architecture of the SNNs for fault diagnosis was discussed, and the results for SNNs fault diagnosis methods with different SNNs＇ topologic structures and parameters were compared. Experimental results showed that the proposed fault diagnosis method based on SNNs was effective and helpful for manipulators＇ fault diag- nosis, which was also important for manufacture industries＇ smooth and safe running.
出处
《山东大学学报（工学版）》
CAS
北大核心
2017年第5期15-21,共7页
Journal of Shandong University（Engineering Science）
基金
supported by the nature science foundation of China under contract (Grants No.61175059,61375010,61673160)
