收稿日期:2022-07-05∗基金项目:《新时代高校美育背景下的原创经典文化品牌培育推广机制研究》(2020FKT67)作者简介:陈莎莎(1988-),女,湖南娄底人,硕士研究生,讲师。基于神经网络的钢琴击弦机故障排除方法研究∗陈莎莎(陕西铁路工程职业技术学院,陕西渭南714099)摘要:针对传统钢琴击弦机故障诊断方法准确率低,导致机械故障排除效果不佳的问题,提出基于改进果蝇算法优化BP神经网络的故障诊断方法。基于果蝇算法FOA加入混沌映射、动态搜索半径策略和优化气味浓度判定公式,得到改进的UFOA算法;然后利用UFOA算法优化BP神经网络,并构建基于UFOA-BP的击弦机故障诊断模型;最后获取钢琴击弦机械故障数据,并通过小波包分解法进行故障数据特征提取。将本模型应用到数据集中进行实验发现,相较于未优化的BP神经网络,提出的UFOA-BP模型的故障预测误差绝对值仅为1.01和0.61,通过UFOA算法提升了BP神经网络的预测精度。且在单弦和多弦故障诊断中,对比于其他诊断模型,本模型的故障诊断准确率分别提升了7.75%、10.08%和7.19%、9.05%。由此说明,通过本模型可提升钢琴击弦机故障诊断率和排除效果。关键词:钢琴击弦机;故障排除;果蝇算法;BP神经网络;小波包分解法中图分类号:TP392文献标识码:ADOI编码:10.14016/j.cnki.1001-9227.2023.01.266StudyontroubleshootingofPianoStrMachinebasedonNeuralNetworkCHENShasha(ShaanxiRailwayEngineeringVocationalandTechnicalCollege,WeinanShaanxi714099,China)Abstract:Inviewofthelowaccuracyofthefaultdiagnosismethodofthetraditionalpianoandstringstrikemachine,thefaultdiagnosismethodofoptimizingtheBPneuralnetworkisproposed.FOAalgorithmbasedonFOA,dynamicsearchradi-usstrategyandodorconcentrationdeterminationformula,getimprovedUFOAalgorithm,optimizeBFOAneuralnetworkbyUFOAalgorithmandbuildUFOA-BP,obtainthemechanicalfaultdata,andextractthefaultdatabythewaveletpacketde-compositionmethod.Applicationofthismodeltothedataset,wefoundthattheabsolutevalueoftheUFOA-BPmodelisonly1.01and0.61,whichimprovesthepredictionaccuracyofBPneuralnetworkthroughtheUFOAalgorithm.Moreover,inthesingle-stringandmulti-stringfaultdiagnosis,thefaultdiagnosisaccuracyofthismodelisimprovedby7.75%,10.08%,and7.19%and9.05%,respectively.Thismodelshowsthatthetroubleshootingrateandtroubleshootingeff...
