【摘要】 针对工业仪表种类繁多、运行环境复杂导致故障频发，严重影响工业自动化生产效率与质量的问题，设计了基于人工智能的工业仪表故障自动化识别系统。采用温度、电压、电流三类传感器优化配置，改进数据采集电路设计，并配置STM32F407VGT6嵌入式处理器；软件上，提取工业仪表运行数据的时域、频域等多维特征，并基于深度神经网络算法实现故障识别。通过软硬件协同工作，实验结果显示，设计系统工业仪表运行数据方差的提取结果与实际数据一致性达98.7%，马修斯相关系数最大值为0.95，较对照系统分别提升31.9%和39.7%，故障识别准确率达96.3%，验证了系统的有效性。更多还原

【Abstract】 Aiming at the problem of frequent failures caused by the wide variety of industrial instruments and complex operating environments,which seriously affect the efficiency and quality of industrial automation production,an Artificial Intelligence based industrial instrument fault automation recognition system design is proposed. Optimize the configuration of temperature,voltage and current sensors,improve the data acquisition circuit design,and configure the STM32 F407 VGT6 embedded processor;On the software side,extract multi-dimensional features such as time-domain and frequency-domain of industrial instrument operation data,and implement fault recognition based on deep neural network algorithms. Through the collaborative work of software and hardware,the experimental results show that the variance extraction of industrial instrument operation data designed by the system has a consistency of98.7% with actual data,and the maximum Matthews correlation coefficient is 0.95,which is 31.9% and39.7% higher than the control system. The fault recognition accuracy reaches 96.3%,verifying the effectiveness of the system.更多还原