基于改进BP神经网络的风机检测机器人多传感器信息融合算法研究

Lele Jin, Zhiwei Kou, Liqiang Liu, Yongsheng Qi, Xiaoming Cui
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引用次数: 1

摘要

风机操作系统的功能结构复杂,单一信号源的状态检测必然会出现错误和虚警。信息融合的诊断方法可以充分利用更多的信息。这样就可以避免这个问题:风扇检测机器人配备了多个传感器,这些传感器通过合理的信息融合算法进行融合。融合后的传感器信息可以获得更准确的风机状态。节省成本,使风机稳定运行的目的是减少人为检修。BP神经网络具有非线性映射、自学习和自适应的能力,且融合性能好,应用范围广。因此,可以选择BP神经网络算法进行传感器信息融合。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on multi-sensor information fusion algorithm of fan detection robot based on improved BP neural network
The functional structure of the fan operating system is complex, and the condition detection of a single signal source will inevitably result in errors and false alarms. The diagnostic method of information fusion can make full use of more information. Thus the problem can be avoided: the Fan Detection robot is equipped with multiple sensors, and these sensors are fused by a reasonable information fusion algorithm. The fused sensor information can obtain a more accurate statement of the fan. The purpose of saving cost and making the fan run stably aims to reduce man-made overhauls. BP neural network has the capability of non-linear mapping, self-learning and self-adaptation, and the fusion performance is good, the application of a wide range. Therefore, BP Neural Network algorithm can be chosen to carry out sensor information fusion.
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