CAJ | 학술논문

围绕机电设备状态监测的需求，设计了基于视听信息融合的状态监测系统。分别利用图像可听化技术、数据归一化和主成分分析得到了较低维数的同质特征数据。以BP 神经网络为融合模型，对所得特征数据进行识别与融合，进而得到对机电设备运行状态的决策输出。实验结果表明，在外界噪声环境下，融合视听信息的状态监测系统能维持较高的正确识别率，同时以神经网络为融合模型保证了系统的稳定性和鲁棒性。
위요궤전설비상태감측적수구，설계료기우시은신식융합적상태감측계통。분별이용도상가은화기술、수거귀일화화주성분분석득도료교저유수적동질특정수거。이BP 신경망락위융합모형，대소득특정수거진행식별여융합，진이득도대궤전설비운행상태적결책수출。실험결과표명，재외계조성배경하，융합시은신식적상태감측계통능유지교고적정학식별솔，동시이신경망락위융합모형보증료계통적은정성화로봉성。
Focused on the demand of mechatronical device status monitoring , a surveiliance system based on audio-visual in-formation fusion was designed .The characteristic data with the same type and low dimensionality was obtained through the methods of image auralization , data normalization and principal component analysis .Based on BP neural network model , the charateristic data was recognised and fused , and the decisions of monitering of electromechanical device were obtain .The experimental results show that the correct recognition rate of monitoring based on audio-visual fusion is maintained at a high level under noisy environ-ment, meanwhile the stability and the robustness of the system was guaranteed by the neural network model .