基于自学习算法的起重机减速器故障报警系统  

Fault alarm system of crane reducer based on self-learning algorithm

作　　者：姚世选 陈智元[2] 刘小臣 Yao Shixuan;Chen Zhiyuan;Liu Xiaochen(School of Application and Maintenance Engineering, Dalian Jiaotong University, Dalian 116028, China;School of Mechanical Engineering, Dalian Jiaotong University, Dalian 116028, China)

机构地区：[1]大连交通大学动车运用与维护工程学院,大连116028 [2]大连交通大学机械工程学院,大连116028

出　　处：《电子测量技术》2018年第6期90-94,共5页Electronic Measurement Technology

基　　金：国家科技支撑计划(2015BAF20B02)项目资助

摘　　要：为实现起重机减速器运行状态在线监测和故障实时报警,设计了一种基于自学习算法的减速器实时故障报警系统。系统将自学习算法融入减速机检测之中,通过传感器、采集卡、数据处理单元和上位机组态系统构成的硬件系统,实现多档位起重机减速器的实时监测和故障报警功能。与传统故障诊断系统相比,引入自学习算法获取判断值,提高针对多档位检测的诊断精度。为验证系统的准确性和可靠性,对起重机减速器进行实验测试,结果表明,系统报警准确可靠。In order to realize the on-line monitoring and real-time fault alarm of the reducer,a real-time fault alarm system based on self-learning algorithm is designed.This system will be integrated into the self-learning algorithm of speed reducer detection,hardware acquisition card,through the sensor,data processing unit and PC configuration system,real-time monitoring and fault gear reducer to realize multi crane alarm function.Compared with the traditional fault diagnosis system,the self-learning algorithm is used to obtain the judgment value and improve the diagnostic accuracy for multi-gear detection.In order to verify the accuracy and reliability of the system,the crane reducer was tested.The results show that the system alarm is accurate and reliable.

关 键 词：自学习 减速器 起重机 振动测量 在线监测 故障报警 

分 类 号：TN911.7[电子电信—通信与信息系统]