化工学报
化工學報
화공학보
JOURNAL OF CHEMICAL INDUSY AND ENGINEERING (CHINA)
2015年
1期
351-356
,共6页
极限学习机%互信息%故障预测%多元时序%趋势分析
極限學習機%互信息%故障預測%多元時序%趨勢分析
겁한학습궤%호신식%고장예측%다원시서%추세분석
extreme learning machine%mutual information%fault prediction%multi-timing%trend analysis
多元时序驱动建模方法是复杂系统故障预测和系统状态评估的一种有效途径，其中人工神经网络作为一种数据驱动的处理非线性问题的有效建模工具，近年来在处理多元时序建模这个问题上得到了较广泛的关注。从全流程的角度出发，首先，运用k-近邻互信息方法对多元时序变量进行降维与相关性计算，从而选择特征变量；其次，提出了一种改进的趋势分析方法对系统的状态进行实时监测，并对系统运行状态进行有效细分；最后，针对系统潜在故障阶段，应用极限学习机(extreme learning machine, ELM)神经网络方法对其进行故障预测。通过对青霉素发酵过程(penicillin fermentation process)进行仿真实验，结果验证了所提方法的有效性。
多元時序驅動建模方法是複雜繫統故障預測和繫統狀態評估的一種有效途徑，其中人工神經網絡作為一種數據驅動的處理非線性問題的有效建模工具，近年來在處理多元時序建模這箇問題上得到瞭較廣汎的關註。從全流程的角度齣髮，首先，運用k-近鄰互信息方法對多元時序變量進行降維與相關性計算，從而選擇特徵變量；其次，提齣瞭一種改進的趨勢分析方法對繫統的狀態進行實時鑑測，併對繫統運行狀態進行有效細分；最後，針對繫統潛在故障階段，應用極限學習機(extreme learning machine, ELM)神經網絡方法對其進行故障預測。通過對青黴素髮酵過程(penicillin fermentation process)進行倣真實驗，結果驗證瞭所提方法的有效性。
다원시서구동건모방법시복잡계통고장예측화계통상태평고적일충유효도경，기중인공신경망락작위일충수거구동적처리비선성문제적유효건모공구，근년래재처리다원시서건모저개문제상득도료교엄범적관주。종전류정적각도출발，수선，운용k-근린호신식방법대다원시서변량진행강유여상관성계산，종이선택특정변량；기차，제출료일충개진적추세분석방법대계통적상태진행실시감측，병대계통운행상태진행유효세분；최후，침대계통잠재고장계단，응용겁한학습궤(extreme learning machine, ELM)신경망락방법대기진행고장예측。통과대청매소발효과정(penicillin fermentation process)진행방진실험，결과험증료소제방법적유효성。
A multiple timing-driven modeling method is an effective way for fault prediction and state evaluation of complex system, in which the artificial neural network is an effective data-driven modeling tool to deal with the nonlinear problems. Recently, it has been widely concerned on the multiple timing-driven modeling problems. In the paper, from the perspective of the whole process, the k-nearest neighbor mutual information method is firstly used to reduce the dimension of the multiple timing variables and calculate the correlation among the variables, so as the select the characteristic variable. Second, an improved trend analysis method is proposed to monitor the system state in real time and segment the system operation state. Finally, aiming at the potential fault stage, extreme learning machine (ELM) neural network is used for fault prediction. Through the simulation experiment on penicillin fermentation process, the results verify the effectiveness of the proposed method.
