Abstract: In light of redundant data, noisy data and data reliability existing in fault diagnosis of node in wireless sensor network, this paper proposes a fault diagnosis algorithm（RSOPNN）of wireless sensor network nodes based on the Rough Set theory and Optimized Probabilistic Neural Network. The method uses rough set theory to get reductions of fault diagnosis from the collection of samples’ properties, so that it can decrease the impact of redundant attributes, noisy data on fault diagnosis, and save energy. Then, it chooses the best reduction from multiple reductions above by using the correlation between properties of reduction instead of subjective choice to solve the irrationality of subjective choice. Finally, the method reconstructs the fault samples with the best reduction of fault diagnosis as the input of the optimized probabilistic neural network, builds classification model to diagnose faults. Experimental results show that in the case of different data reliabilities, the RSOPNN proposed in this paper can eliminate redundant and noisy data in the original data effectively with higher diagnosis rate, which meets the demand of wireless sensor network.

Key words: wireless sensor network, discernibility matrix, attribute reduction, probabilistic neural network, fault diagnosis

摘要： 针对无线传感器网络节点故障诊断中存在的冗余故障属性、噪声数据以及数据可靠性等问题，提出基于粗糙集-优化概率神经网络的无线传感器网络节点故障诊断算法（简称RSOPNN）。通过粗糙集从故障样本属性集合中求解故障诊断属性约简，从而去除冗余故障属性，降低冗余属性、噪声数据对故障诊断的影响，节省能耗。对于多个属性约简选择，以属性间的相关程度作为度量标准，代替常规的主观选择，从多个约简中确定最优故障诊断属性约简，解决主观选择的不合理性。以最优的故障诊断属性重构故障样本，作为优化概率神经网络的输入，建立故障分类模型，从而对故障进行诊断。实验结果表明，在不同的数据可靠性下，RSOPNN方法能够有效删减样本中的冗余属性和噪声数据，保持高效的故障诊断水平，符合无线传感器网络的需求。

关键词: 无线传感器网络, 可辨识矩阵, 属性约简, 概率神经网络, 故障诊断