期刊文献+

基于硬件自冗余的称重设备传感器故障检测方法 被引量:2

Fault detection method for weighing equipment sensor based on hardware redundancy
下载PDF
导出
摘要 传统的称重设备采用的是模拟称重传感器,不具有故障自检测功能。有研究人员研究了针对称重设备传感器故障检测的方法,但都是从称重传感器的输出进行检测,忽略了产生故障的原因,导致了检测过程复杂、运算量大。为此文章提出了基于硬件自冗余的称重设备传感器故障检测方法,利用称重传感器自身具有的硬件冗余现象对称重传感器的故障进行检测。实验表明,当称重传感器由于自身质量问题而发生故障时,此方法可以有效检测出称重传感器发生了故障。 The traditional weighing equipment uses analog weighing sensor,the sensor does not have the function of fault self-detection. Some researchers have studied the method of fault detection for weighing equipment,however,all of them are detected fault from the output of the weighing sensor,ignoring the cause of the failure,so those methods have complex detection process and huge amount of computation. So,this paper presents a method of fault detection based on hardware redundancy,detecting weighing sensor's fault based on the weighing sensor itself has hardware redundancy. Experimental results show that this method can effectively detect the fault of the weighing sensor when the fault due to their own quality problems.
出处 《微型机与应用》 2017年第15期84-86,共3页 Microcomputer & Its Applications
基金 江苏省科技支撑重点项目(BE2014003) 江苏省自然科学基金(BK20161149)
关键词 称重传感器 故障检测 硬件自冗余 weighing sensor fault detection hardware redundancy
  • 相关文献

参考文献7

二级参考文献40

  • 1徐蔚鸿,谢中科,杨静宇,叶有培.两类模糊推理算法的连续性和逼近性[J].软件学报,2004,15(10):1485-1492. 被引量:17
  • 2肖兴华.称重传感器亚健康及早诊断的方法[J].衡器,2007,36(1):41-42. 被引量:3
  • 3王祯荣.浅谈电子汽车衡秤台结构与刚度计算[J].衡器,1997(1):24-26. 被引量:8
  • 4吴道娣.非电量电测技术[M].西安:西安交通大学出版社,2001.30-43.
  • 5BLISS D, STICKEL C. BENTZ J W. Load cell diagnostics and failure prediction weighing apparatus and process[P]. United State Patent: 728638, 2000.
  • 6KAI G, YAN W Z. Correcting senor drift and intermittency faults with data fusion and automated learning[J]. IEEE Systems Journal, 2008, 2(2): 189 - 197.
  • 7WEI S Z, JIN N D. Fault diagnosis system based on information fusion and embedded intemet[C]//Proceedings oflEEE International Conference on Integration Technology. China: IEEE Press, 2007:203 - 207.
  • 8XU K J, LI Q L, MEI T, et al. Estimation of wrist force/torque using data fusion of finger force sensors[J]. Measurement, 2004, 36(1): 11 -19.
  • 9FAN C L, JIN Z H, ZHANG J, et al. Application of multi-sensor data fusion based on RBF neural networks for fault diagnosis of SAMS[C] //Proceedings of IEEE the 7th International Conference on Control, Automation, Robotics and Vision (ICARCV'02). Singapore: IEEE Press, 2002:1557 - 1562.
  • 10ZHANG J, WANG B S, MAY G, et al. Fault diagnosis of sensor network using information fusion defined on different reference sets[C] //Proceedings of IEEE International Conference on Radar(CIE'06). China: IEEE Press, 2006:1 - 5.

共引文献38

同被引文献14

引证文献2

二级引证文献5

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部