通过引入“辅助角速度”，将并联式六维加速度传感器的二阶非线性动力学方程组化为一阶线性微分方程组，实现了动力学方程的完全解耦。通过运用并联机构的正向运动学理论推导系统输入量之间的固有约束关系，建立起该传感器故障诊断的模型，并给出具体的可修复故障组合。定义一种故障诊断修复率的概念，对传感器故障的可修复性进行量化，发现传感器的故障诊断修复率较低。基于此，提出一种改进的并联式六维加速度传感器构型，依据故障自诊断模型归纳出故障判定方程并结合故障树分析法进行故障诊断，对具体的故障工况提供故障修复方案，结果表明，相较于原结构，并联式六维加速度传感器故障诊断修复率提高了28%。运用ADAMS软件仿真验证，结果显示故障支链修复后的综合误差不超过6.38%，验证了所提出的故障诊断及修复方案的正确性和可行性。
Abstract
By introducing an auxiliary angular velocity, the Euler dynamical differential equations were transferred from second-order nonlinear ones into first-order linear unsteady ones, and the dynamic equation were completely decoupled. Using the forward kinematics theory of parallel mechanism to explore the inherent constraint relations between system input, then establish the fault diagnosis model of the sensor and give out specific combination of fault that can be concrete can be restored. The concept of fault self-diagnosis and restoration rate were defined, on account of the low rate of fault self-diagnosis and restoration, a Parallel Type Six-axis Accelerator whose structure were improved were proposed. Based on the model of fault diagnosis, the fault judgement equation were induced, by combining with fault tree analysis, and fault could be diagnosed and the troubleshooting solutions to specific fault condition were provided. Eventually the fault self-diagnosis and restoration rate of Parallel Type Six-axis Accelerator increased by 28%. Using ADAMS software to do simulation analysis, results show that the comprehensive error was less than 6.38% after the fault branched chain was replaced. The feasibility of fault handling scheme was verified.
关键词
六维加速度传感器 /
故障诊断 /
故障树分析法 /
故障修复
{{custom_keyword}} /
Key words
Six-axis accelerator /
Fault diagnosis /
Fault tree analysis /
troubleshooting
{{custom_keyword}} /
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] 解旭辉, 刘危, 张明亮, 等. 微惯性测量组合关键技术与应用[J]. 光学精密工程, 2002(02):154-159.
Xie X H, Liu W, Zhang M L, et al. Key technologies and applications of micromechanical inertial measurement units[J]. Optics and Precision Engineering, 2002(02):154-159. (in Chinese)
[2] Staufer P, Gattringer H. State estimation on flexible robots using accelerometers and angular rate sensors[J]. Mechatronics, 2012,22(8):1043-1049.
[3] Wei X. Subdivided locating method of star image for star sensor[J]. Journal of Beijing University of Aeronautics & Astronautics, 2003,29:812-815.
[4] Zou X, Xu X, Yao L, et al. A 1-V 450-nW Fully Integrated Programmable Biomedical Sensor Interface Chip[J]. Solid-State Circuits, IEEE Journal of, 2009,44(4):1067-1077.
[5] 张娅玲, 陈伟民, 章鹏, 等. 传感器故障诊断技术概述[J]. 传感器与微系统, 2009(01):4-6.
Zhang Y L, Chen W M, Zhang P, et al. Overview on sensor fault diagnosis technology[J]. Transducer and Microsystem Technologies, 2009(01):4-6. (in Chinese)
[6] 何富君, 刘小磊, 卢晓昭, 等. 传感器的故障诊断技术研究[J]. 科学技术与工程, 2010(26):6481-6487.
He F J, Liu X L, Lu X Z, et al. A research on fault detection diagnosis technology for sensor[J]. Science Technology and Engineering, 2010(26):6481-6487. (in Chinese)
[7] 赵志刚, 赵伟. 基于动态不确定度理论的多传感器系统传感器失效检测方法[J]. 传感技术学报, 2006(06):2723-2726.
Zhao Zh G, Zhao W. A new sensor failure detection method based on dynamic uncertainty theory[J]. Chinese Journal of Sensors and Actuators, 2006(06):2723-2726. (in Chinese)
[8] Fraher P M A, Clarke D W. Fouling detection and compensation in Clark-type DOx sensors[J]. Instrumentation & Measurement IEEE Transactions on, 1998,47(3):686-691.
[9] 李志农, 朱明, 褚福磊, 等. 基于经验小波变换的机械故障诊断方法研究[J]. 仪器仪表学报, 2014(11):2423-2432.
Li Zh N, Zhu M, Chu F L, et al. Mechanical fault diagnosis method based on empirical wavelet transform[J]. Chinese Journal of Scientific Instrument, 2014(11):2423-2432. (in Chinese)
[10] Niaki S T A, Abbasi B. Fault Diagnosis in Multivariate Control Charts Using Artificial Neural Networks[J]. Quality & Reliability Engineering International, 2005,21(8):825, 840.
[11] 尤晶晶, 李成刚, 吴洪涛. 基于并联机构的六维加速度传感器的方案设计及建模研究[J]. 振动工程学报, 2012,25(6):658-666.
You J J, Li Ch G, Wu H T. Study on the scheme design and modeling of six-axis accelerometer based on parallel mechanism[J]. Journal of Vibration Engineering, 2012,25(6):658-666. (in Chinese)
[12] 赵元喜, 胥永刚, 高立新, 等. 基于谐波小波包和BP神经网络的滚动轴承声发射故障模式识别技术[J]. 振动与冲击, 2010(10):162-165.
Zhao Y X, Xu Y G, Gao L X, et al. Fault pattern recognition technique for roller bearing acoustic emission based on harmonic wavelet packet and BP neural network[J]. Journal of Vibration and Shock, 2010(10):162-165. (in Chinese)
[13] Shu M H, Cheng C H, Chang J R. Using intuitionistic fuzzy sets for fault-tree analysis on printed circuit board assembly[J]. Microelectronics Reliability, 2006,46(12):2139-2148.
[14] 李建国, 崔祜涛, 田阳. 飞行器姿态确定的四元数约束滤波算法[J]. 哈尔滨工业大学学报, 2013(01):35-40.
Li J G, Cui H T, Tian Y. Quaternion constrained filter algorithm for spacecraft attitude determination[J]. Journal of Harbin Institute of Technology, 2013(01):35-40. (in Chinese)
[15] 戎保, 芮筱亭, 王国平, 等. 多体系统动力学研究进展[J]. 振动与冲击, 2011(07):178-187.
Rong B, Rui X Y, Wang G P, et al. Developments of studies on multibody system dynamics[J]. Journal of Vibration and Shock,2011(07):178-187. (in Chinese)
[16] 尤晶晶. 基于冗余并联机构的压电式六维加速度传感器研究[D]. 南京航空航天大学, 2013.
You J J. Research on a piezoelectric six-axis accelerometer based on 6-SPS parallel mechanism[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2010. (in Chinese)
{{custom_fnGroup.title_cn}}
脚注
{{custom_fn.content}}
PDF(1783 KB)
