人工智能时代下造纸机械设备故障智能监测系统设计  

Design of Paper Machine Fault Intelligent Monitoring System in the Era of Artificial Intelligence

作　　者：张明辉 于丽萍 ZHANG Minghui;YU Liping(Guangdong Vocational College of Innovation and Technology,Dongguan 523960,China)

机构地区：[1]广东创新科技职业学院,广东东莞523960

出　　处：《造纸科学与技术》2022年第6期35-39,共5页Paper Science & Technology

摘　　要：长时间不间断地使用机械设备,会降低设备使用寿命,生成多种故障,为获取准确的故障类型,结合人工智能技术设计造纸机械设备故障智能监测系统。在硬件系统中,分别设计振动传感器与滤波器芯片,得到振动传感器敏感元件结构以及滤波器芯片电路效果图。在软件中,分别获取峰值指标与峭度指标,结合频域特征参数与能量特征参数,提取故障信号特征参数。对样本权重值进行初始化,计算样本数据分类误差,更新权重系数,获取分类器线性集合的函数值,基于人工智能技术建立故障样本分类模型,设计故障智能监测算法,得到故障样本分类与监测结果。实验结果显示,该监测系统在六类分类样本中的F1_Score指标为0.8997,Kappa系数为0.7698,可见其监测精度较好,可以准确分类故障类型。If mechanical equipment is used continuously for a long time,its service life will be reduced and multiple faults will be generated.In order to obtain accurate fault types,an intelligent monitoring system for faults of paper machine equipment is designed by combining artificial intelligence technology.In the hardware system,the vibration sensor and filter chip are designed respectively,and the structure of the vibration sensor sensor and the circuit effect diagram of the filter chip are obtained.In the software,the peak index and kurtosis index are obtained respectively,and the fault signal characteristic parameters are extracted by combining the frequency domain characteristic parameters and energy characteristic parameters.Initialize the sample weight value,calculate the classification error of the sample data,update the weight coefficient,obtain the function value of the linear set of the classifier,establish the fault sample classification model based on artificial intelligence technology,design the fault intelligent monitoring algorithm,and obtain the fault sample classification and monitoring results.The experimental results show that the monitoring system has_The score index is 0.8997 and Kappa coefficient is 0.7698,which shows that the monitoring accuracy is good and the fault type can be classified accurately.

关 键 词：人工智能技术 造纸机械设备 机械设备故障 故障智能监测 监测系统 

分 类 号：TS734[轻工技术与工程—制浆造纸工程] TP18[自动化与计算机技术—控制理论与控制工程]