电子琴琴键开关短路故障自动诊断方法研究

Research on automatic diagnosis method of short circuit fault of electronic piano key switch

针对电子琴琴键开关短路故障诊断过程中,故障特征获取环节较为落后,导致电子琴琴键开关短路故障诊断误诊率较高的问题,提出电子琴琴键开关短路故障自动诊断方法。根据电子琴琴键开关信号历史数据,确定滤波阈值,完成电子琴琴键开关信号采集及处理。使用信息熵技术提取电子琴琴键开关短路故障特征,获取故障类别判定依据。构建卷积神经网络,实现电子琴琴键开关短路故障诊断过程。至此,电子琴琴键开关短路故障自动诊断方法设计完成。构建实验环节,经实验结果证实,此方法可有效降低故障诊断误诊率,提升故障诊断效率。整体应用效果优于当前故障诊断方法。

Aiming at the problem of high misdiagnosis rate of short-circuit fault diagnosis of electronic piano key switch due to the backward link of fault feature acquisition in the process of short-circuit fault diagnosis of electronic piano key switch,an automatic diagnosis method of short-circuit fault of electronic piano key switch is proposed.According to the historical data of the key switch signal of the electronic organ,the filtering threshold is determined,and the key switch signal acquisition and processing of the electronic organ are completed.The information entropy technology is used to extract the short-circuit fault characteristics of the key switch of the electronic organ and obtain the judgment basis of the fault category.The convolution neural network is constructed to realize the short-circuit fault diagnosis process of electronic piano key switch.So far,the design of automatic diagnosis method for short circuit fault of electronic piano key switch has been completed.The experimental results show that this method can effectively reduce the misdiagnosis rate of fault diagnosis and improve the efficiency of fault diagnosis.The overall application effect is better than the current fault diagnosis methods.