基于分时段规范变量残差分析的高速自动机动态特性监测

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振动与冲击 ›› 2019, Vol. 38 ›› Issue (20) : 90-96.

论文

王宝祥1,2，潘宏侠1

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Dynamic performance monitoring of high-speed automata based on phase-partitioned canonical variate dissimilarity analysis

WANG Baoxiang，PAN Hongxia

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摘要

针对高速自动机运动形态的多行程特点，提出一种分时段规范变量残差分析（phase-partitioned canonical variate dissimilarity analysis, PCVDA）方法用于高速自动机的动态特性监测。通过建立整个行程与短时瞬态冲击信号的对应关系，将冲击信号划分为多个时段。采用正弦波辅助经验模态分解（sinusoid-assisted empirical mode decomposition, SEMD）将每个时段的冲击信号分解为高频和低频成分，分别计算两种成分的过去和未来数据的规范变量的残差，建立基于高低频成分的PCVDA模型监测高速自动机在不同时段的动态特性。对某12.7mm高速自动机的监测结果验证了PCVDA模型的有效性。

Abstract

Aiming at monitoring the dynamic characteristics of high-speed automata with multi-stroke features, a phase-partitioned canonical variate dissimilarity analysis (PCVDA) method was proposed.The short-term transient shock signals were firstly partitioned into multiple phases through establishing the matching relationship between the whole strokes and the signals, the sinusoid-assisted empirical mode decomposition (SEMD) was then employed to transform each phase into high-frequency and low-frequency components, and by calculating the departure between the past-and future-projected canonical variables, the PCVDA models on those two components were built to monitor the dynamic characteristics of high-speed automata at different phases, respectively.Dynamic performance monitoring of a 12.7mm high-speed automata validates the efficiency of the proposed work.

关键词

时段划分

/ 规范变量残差分析 / 正弦辅助经验模态分解 / 动态监测 / 高速自动机

Key words

phase partition

/ canonical variate dissimilarity analysis / sinusoid-assisted

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王宝祥1,2，潘宏侠1. 基于分时段规范变量残差分析的高速自动机动态特性监测[J]. 振动与冲击, 2019, 38(20): 90-96

WANG Baoxiang，PAN Hongxia. Dynamic performance monitoring of high-speed automata based on phase-partitioned canonical variate dissimilarity analysis[J]. Journal of Vibration and Shock, 2019, 38(20): 90-96

参考文献

1. 潘宏侠, 都衡,马春茂, 局域波信息熵在高速自动机故障诊断中的应用[J]. 振动.测试与诊断, 2015(6): p. 1159-1164.
PAN Hongxia, DU Heng, MA Chunmao, High-speed automaton fault diagnosis based on local wave and information entropy[J]. Journal of Vibration, Measurement & Diagnosis, 2015(6): p. 1159-1164.
2. 潘宏侠, 兰海龙,任海峰, 基于局域波降噪和双谱分析的自动机故障诊断研究[J]. 兵工学报, 2014. 35(7): p. 1077-1082.
PAN Hongxia, LAN Hailong, REN Haifeng, Fault diagnosis for automata based on local wave noise
reduction and bispectral analysis[J]. Acta Armamentarii, 2014. 35(7): p. 1077-1082.
3. 任海锋,潘宏侠, 基于多分形特征的枪械自动机裂纹故障诊断[J]. 兵工学报, 2018. 39(3): p. 457-462.
Ren Haifeng, PAN Hongxia, Crack fault diagnosis of gun automatic mechanism based on multifractal features[J]. Acta Armamentarii, 2018. 39(3): p. 457-462.
4. 邓士杰, 唐力伟,张晓涛, 邻域自适应增量式 PCA-LPP 在齿轮箱故障诊断中的应用[J]. 振动与冲击, 2017. 36(14): p. 111-115.
DENG Shijie, TANG Liwei, ZHANG Xiaotao, Gear fault diagnosis based on an adaptive neighborhood incremental PCA-LPP manifold learning algorithm[J]. Journal of Vibration and Shock, 2017. 36(14): p. 111-115.
5. 李锋, 汤宝平,郭胤, Laplacian 双联最小二乘支持向量机用于早期故障诊断[J]. 振动与冲击, 2017. 36(16): p. 85-92.
LI Feng, TANG Baoping, Guo Yin, Early fault diagnosis using Laplacian twin least squares support vector machine[J]. Journal of Vibration and Shock, 2017. 36(14): p. 111-115.
6. YU J, Bearing performance degradation assessment using locality preserving projections and Gaussian mixture models[J]. Mechanical Systems and Signal Processing, 2011. 25(7): p. 2573-2588.
7. LU C, CHEN J, HONG R, et al., Degradation trend estimation of slewing bearing based on LSSVM model[J]. Mechanical Systems and Signal Processing, 2016. 76: p. 353-366.
8. ŽVOKELJ M, ZUPAN S,PREBIL I, EEMD-based multiscale ICA method for slewing bearing fault detection and diagnosis[J]. Journal of Sound and Vibration, 2016. 370: p. 394-423.
9. YU J, Local and nonlocal preserving projection for bearing defect classification and performance assessment[J]. IEEE Transactions on Industrial Electronics, 2012. 59(5): p. 2363-2376.
10. WU J, WU C, CAO S, et al., Degradation data-driven time-to-failure prognostics approach for rolling element bearings in electrical machines[J]. IEEE Transactions on Industrial Electronics, 2019. 66(1): p. 529-539.
11. RUIZ-C RCEL C, CAO Y, MBA D, et al., Statistical process monitoring of a multiphase flow facility[J]. Control Engineering Practice, 2015. 42: p. 74-88.
12. PILARIO K E S,CAO Y, Canonical variate dissimilarity analysis for process incipient fault detection[J]. IEEE Transactions on Industrial Informatics, 2018. 14(12): p. 5308-5315.
13. WANG C, KEMAO Q,DA F, Regenerated phase-shifted sinusoid-assisted empirical mode decomposition[J]. IEEE Signal Processing Letters, 2016. 23(4): p. 556-560.
14. RUIZ-C RCEL C, LAO L, CAO Y, et al., Canonical variate analysis for performance degradation under faulty conditions[J]. Control Engineering Practice, 2016. 54: p. 70-80.
15. ODIOWEI P-E P,CAO Y, Nonlinear dynamic process monitoring using canonical variate analysis and kernel density estimations[J]. IEEE Transactions on Industrial Informatics, 2010. 6(1): p. 36-45.