期刊文献+

称重设备传感器零漂检测方法研究 被引量:1

Study on zero drift fault detection of the sensor in weighing equipment
下载PDF
导出
摘要 数字化改造后的称重设备其称重传感器具有故障诊断的功能,但目前缺少针对称重传感器零漂故障诊断的方法。为此,文章提出了基于滑窗原理和零点采样序列标准差的零漂故障检测方法。首先对称重传感器的零点输出信号采样,然后利用滑窗取出其中的n个连续值,求出这n个值的标准差,最后用此标准差与正常输出标准差的比值作为检测依据。当比值大于设定阈值时,传感器存在故障,否则不存在故障。测试表明该方法能有效检测传感器的零漂故障。 The load cells in weighing equipment have the self-diagnosis function when they are digitized. However,at present,the method of zero drift fault diagnosis for load cells is lack. So,this paper presents a method for detecting load cell's zero drift,which based on sliding window and standard deviation of the zero-crossing points sample series. Firstly,the null point outputs of load cell are sampled,then n continuous values are extracted by using sliding window,and the standard deviation of these numbers is calculated. Lastly,using the ratio of this standard deviation and normal standard deviation as the basis for diagnosis. When the ratio value is greater than the set threshold,the cell has fault,otherwise it is normal. Test results show that this way can effectively diagnose zero drift of the load cells.
出处 《微型机与应用》 2017年第7期88-90,94,共4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 滑窗 标准差 零漂 故障检测 称重设备 sliding window standard deviation zero drift fault detection weighing equipment
  • 相关文献

参考文献4

二级参考文献30

  • 1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
  • 2施汉谦 宋文敏.电子秤技术[M].北京:中国计量出版社,1990..
  • 3BLISS D, STICKEL C. BENTZ J W. Load cell diagnostics and failure prediction weighing apparatus and process[P]. United State Patent: 728638, 2000.
  • 4KAI 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.
  • 5WEI 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.
  • 6XU 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.
  • 7FAN 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.
  • 8ZHANG 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.
  • 9NICOLAO B K. Reformulated radial basis neural networks trained by gradient descent[J]. IEEE Transactions on Neural Networks, 1999, 10(3): 657 - 671.
  • 10WANG W J, LEH L. Stability and stabilization of fuzzy large-scale systems[J]. IEEE Transactions on Fuzzy Systems, 2004, 12(3): 309 - 315.

共引文献19

同被引文献15

引证文献1

二级引证文献7

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