| 摘要: |
| 目的 为了实现自动包装机热封工艺中温度传感器的故障实时故障检测。方法 使用广义回归神经网络（General Regression Neural Network, GRNN）构建了热封温度传感器状态自动检测网络，再采用混沌加速果蝇优化算法（Chaos Accelerated Fruit Fly Optimization Algorithm , CAFOA）进行广义回归神经网络的学习因子优化选取，求解出最优学习因子。通过建立CAFOA-GRNN自动检测模型，再结合统计学中置信区间的方法，对故障进行诊断分类。结果 在传感器故障实验中，将理想故障函数与历史运行数据叠加，产生故障数据集，并将其用于验证建立的模型，获得了较好的检测效果，准确率较高。结论 该方法实现了传感器故障的实时检测，可以用于提高生产的可靠性，具有一定的工程实用价值。 |
| 关键词: 广义回归神经网络 混沌加速果蝇优化算法 学习因子 置信区间 |
| DOI：10.19554/j.cnki.1001-3563.2019.13.030 |
| 分类号:TB486 |
| 基金项目: |
|
| Packaging Machine Heat Sealing Temperature Sensor Fault Detection Based on CAFOA-GRNN |
|
CHEN Xiao-kang1, TU Xuan2, XU Wei-dong1
|
|
1.University of Shanghai for Science and Technology, Shanghai 200093, China;2.Shanghai Institute of Process Automation and Instrumentation, Shanghai 200233, China
|
| Abstract: |
| The paper aims to realize the fault real-time fault detection of the temperature sensor in the heat sealing process of the automatic packaging machine. The generalized regression neural network (GRNN) was used to construct a state automatic detection network of heat-sealed temperature sensor, and then the Chaos Accelerated Fruit Fly Optimization Algorithm (CAFOA) was used to study the generalized regression neural network. Factor optimization was selected to solve the optimal learning factor. By establishing a CAFOA-GRNN automatic detection model, combined with the statistical confidence interval method, the faults were classified and diagnosed. In the sensor failure experiment, the ideal fault function was superimposed with the historical operation data to generate the fault data set, to verify the established model. Good detection effect was obtained, and the accuracy was high. The method realizes the real-time detection of sensor failure, and can be used to improve the reliability of production, and has certain engineering practical significance. |
| Key words: general regression neural network chaos accelerated fruit fly optimization algorithm learning factor confidence interval |
渝公网安备 50010702501716号