电测与仪表
電測與儀錶
전측여의표
ELECTRICAL MEASUREMENT & INSTRUMENTATION
2015年
14期
118-123
,共6页
模糊%神经网络%动力锂电池%故障诊断%专家系统
模糊%神經網絡%動力鋰電池%故障診斷%專傢繫統
모호%신경망락%동력리전지%고장진단%전가계통
fuzzy%neural network%power lithium battery%fault diagnosis%expert system
动力锂电池故障的产生原因具有一定的复杂性和不确定性。为此，提出一种基于模糊神经网络的故障诊断专家系统，该方法结合了模糊数学，神经网络以及专家系统的优点。用模糊数学可以将症状模糊化以表征故障的隶属度、神经网络具有良好的自学习能力、专家系统具有推理能力强，三者的相互结合，即提高了系统的准确性和可操作性，又满足了对故障诊断智能化、自动化的要求。试验结果表明该方法可以准确的判断出系统的故障，不仅将故障检测的精度提高到，预测误差在之间，而且检测时间大大缩短。提高了动力锂电池的自适应能力，自主学习能力，为动力锂电池故障诊断提出了一种科学高效的新方法。
動力鋰電池故障的產生原因具有一定的複雜性和不確定性。為此，提齣一種基于模糊神經網絡的故障診斷專傢繫統，該方法結閤瞭模糊數學，神經網絡以及專傢繫統的優點。用模糊數學可以將癥狀模糊化以錶徵故障的隸屬度、神經網絡具有良好的自學習能力、專傢繫統具有推理能力彊，三者的相互結閤，即提高瞭繫統的準確性和可操作性，又滿足瞭對故障診斷智能化、自動化的要求。試驗結果錶明該方法可以準確的判斷齣繫統的故障，不僅將故障檢測的精度提高到，預測誤差在之間，而且檢測時間大大縮短。提高瞭動力鋰電池的自適應能力，自主學習能力，為動力鋰電池故障診斷提齣瞭一種科學高效的新方法。
동력리전지고장적산생원인구유일정적복잡성화불학정성。위차，제출일충기우모호신경망락적고장진단전가계통，해방법결합료모호수학，신경망락이급전가계통적우점。용모호수학가이장증상모호화이표정고장적대속도、신경망락구유량호적자학습능력、전가계통구유추리능력강，삼자적상호결합，즉제고료계통적준학성화가조작성，우만족료대고장진단지능화、자동화적요구。시험결과표명해방법가이준학적판단출계통적고장，불부장고장검측적정도제고도，예측오차재지간，이차검측시간대대축단。제고료동력리전지적자괄응능력，자주학습능력，위동력리전지고장진단제출료일충과학고효적신방법。
The cause of power lithium battery failure has a certain complexity and uncertainty.To this end,this paper proposes a fault diagnosis expert system based on fuzzy neural network.This method combines the advantages of fuzzy mathematics,neural network and expert system.Using fuzzy mathematics can be blurred to characterize the member-ship degree of the fault symptoms,the neural network has good self-learning ability,the expert system has strong rea-soning ability,All three together,that is not only to improve the accuracy of the system and operability,but also meet the requirement of the intelligent and automatic diagnosis for faults.The test results show that the method can accurate-ly judge the fault in the system,it not only to increase the accuracy of fault detection to 0.001,control the prediction error at between 1%and 8%, but also shorten the testing time.This method improves the self-adaptive ability of the power lithium batteries, the independent learning ability, and puts forward a new scientific and efficient method for power lithium battery fault diagnosis.
