融合BiSTM与改进注意力机制的电子设备故障诊断系统设计

Electronic device fault automatic diagnosis system integrating BiSTM and improved attention mechanism

摘要:

为了提高电子设备故障效果,提出融合BiSTM与改进注意力机制的电子设备故障自动诊断系统。该系统在数据采集时,采用低功耗微处理器和多传感器获取设备运行数据,引入抗干扰因子的自适应加权平均融合处理,保障数据可靠传输。在故障诊断环节,将BiSTM与改进注意力机制结合,BiSTM双向提取特征,捕捉设备动态变化；改进注意力机制通过故障特征增强因子定向加权关键特征,提升诊断准确率与泛化能力。同时引入多任务学习与动态学习率调整策略,增强模型性能与训练效率,最终输出电子设备故障诊断结果。测试结果显示,该系统数据采集可靠性高,数据失真程度低；拟合优度指标值均在0.92以上,能够满足电子设备故障诊断应用需求。

Abstract:

In order to improve the effectiveness of electronic device faults, an electronic device fault automatic diagnosis system that integrates BiSTM and improved attention mechanism is proposed. The system uses low-power microprocessors and multiple sensors to obtain device operation data during data collection, and introduces adaptive weighted average fusion processing with anti-interference factors to ensure reliable data transmission. In the fault diagnosis process, BiSTM is combined with an improved attention mechanism to extract features bidirectionally and capture device dynamic changes; Improving the attention mechanism by enhancing the targeted weighting of key features through fault feature enhancement factors, enhances diagnostic accuracy and generalization ability. Simultaneously introducing multi task learning and dynamic learning rate adjustment strategies to enhance model performance and training efficiency, ultimately outputting electronic device fault diagnosis results. The test results show that the system has high reliability in data collection and low degree of data distortion; The goodness of fit index values are all above 0.92, which can meet the requirements of electronic equipment fault diagnosis applications.