吉林大学学报(工学版) ›› 2022, Vol. 52 ›› Issue (2): 392-399.doi: 10.13229/j.cnki.jdxbgxb20211154
• 车辆工程·机械工程 • 上一篇
Wei LUO1,2(),Bo LU3,Fei CHEN4(),Teng MA1,2
摘要:
提出了一种基于粒子群-支持向量机(PSO-SVM)及时序环节的数控刀架故障诊断方法。首先,将数控刀架划分为5个子系统,并将一个工作周期划分为4个时序环节(T1、T2、T3、T4);其次,探索了数控刀架不同时序环节振动、电机电流、油压以及接近开关等信号的特征提取方法;最后,提出了基于PSO-SVM的数控刀架故障诊断方法,并开展了不同时序环节的数控刀架故障试验。根据故障数据对支持向量机(SVM)和PSO-SVM两种故障诊断方法进行了对比验证。结果表明:时序环节T2、T3和T4的故障诊断准确率分别提高了28%、23%和5%,验证了该故障诊断方法的有效性。本文方法不仅适用于数控刀架,还为其他复杂机电系统的故障诊断研究提供了一个新思路。
中图分类号:
1 | Sun B, Balakrishnan N, Chen F, et al. Reliability evaluation of the NC turret with accurate failure data and interval censored data based on EM algorithm[J]. Journal of Mechanical Science and Technology, 2020, 34(4): 1503-1513. |
2 | Li Chang-you, Wang Wei, Zhang Yi-min, et al. Indexing accuracy reliability sensitivity analysis of power tool turret[J]. Eksploatacja I Niezawodność, 2015, 17(1): 27-34. |
3 | Wang Y Q, Yam R C M, Zuo M J,et al.A comprehensive reliability allocation method for design of CNC lathes[J]. Reliability Engineering & System Safety, 2001, 72 (3): 247-252. |
4 | Yan C F, Li J W, Liu J. Study on CNC lathe electric turret fault diagnosis[J]. Applied Mechanics and Materials, 2012, 220-223: 381-384. |
5 | Yuan L, Fu F U, Li H B. CNC Lathe turret typical failure analysis and resolution[J]. Manufacturing Technology & Machine Tool, 2010(1): 128-130. |
6 | Su Z, Wei W, Zhang C, et al. Fault diagnosis of general turret electrically controlled system based on fuzzy neutral network optimized by genetic algorithm[C]∥IEEE 2011 10th International Conference on Electrically Mersurement Instruments, Chengdu, 2011: 275-278. |
7 | 田致富. 基于贝叶斯网络的刀架系统故障预测[D]. 长春: 吉林大学机械与航空航天工程学院, 2020. |
Tian Zhi-fu. Turret system fault prediction based on bayesian network[D]. Changchun: School of Mechanical and Aerospace Engineering, Jilin University, 2020. | |
8 | Chen W Z, Wei H, Fei C, et al. Designing of condition monitoring system for NC turret test platform[C]∥6th International Conference on Mechanical, Automotive and Materials Engineering, Hong Kong2018: 61-65. |
9 | 胡炜. 面向数控刀架可靠性试验的CPS研究[D]. 长春: 吉林大学机械与航空航天工程学院, 2019. |
Hu Wei. Research on CPS of NC turret reliability test[D] .Changchun: School of Mechanical and Aerospace Engineering, Jilin University, 2019. | |
10 | Basir O, Yuan X. Engine fault diagnosis based on multi-sensor information fusion using dempster-shafer evidence theory[J]. Information Fusion, 2007, 8(4): 379-386. |
11 | Stavropoulos P, Papacharalampopoulos A, Vasiliadis E, et al. Tool wear predictability estimation in milling based on multi-sensorial data[J]. International Journal of Advanced Manufacturing Technology, 2016, 82: 509-521. |
12 | Praveenkumar T, Saimurugan M, Rb H H, et al. A multi-sensor information fusion for fault diagnosis of gearbox utilizing discrete wavelet features[J]. Measurement Science & Technology, 2019, 30: 085101. |
13 | Saravanan N, Siddabattuni V, Ramachandran K I. A comparative study on classification of features by SVM and PSVM extracted using morlet wavelet for fault diagnosis of spur bevel gear box[J]. Expert Systems with Applications, 2008, 35(3): 1351-1366. |
14 | Fernández-Francos D, Martínez-Rego D, Fontenla-Romero O, et al. Automatic bearing fault diagnosis based on one-class ν -SVM[J]. Computers & Industrial Engineering, 2013, 64(1): 357-365. |
15 | Abbasion S, Rafsanjani A, Farshidianfar A, et al. Rolling element bearings multi-fault classification based on the wavelet denoising and support vector machine[J]. Mechanical System and Signal Processing, 2007, 21(7): 2933-2945. |
16 | Wang D J, Meng L, Chao L, et al. Fault diagnosis of automobile engine based on support vector machine[J]. Applied Mechanics and Materials, 2011, 80/81:1060-1064. |
17 | Ji Jun-jie, Qu Jian-feng, Chai Yi, et al. An algorithm for sensor fault diagnosis with EEMD-SVM[J]. Transactions of the Institute of Measurement and Control, 2018, 40(6): 1746-1756. |
18 | Wen Shuo, Wang Jie-sheng, Gao Jie. Fault diagnosis strategy of polymerization kettle equipment based on support vector machine and cuckoo search algorithm[J]. Engineering Letters, 2017, 25(4): EL-25-4-15. |
19 | Liu Z, Chen X, He Z, et al. LMD method and multi-class RWSVM of fault diagnosis for rotating machinery using condition monitoring information[J]. Sensors(Basel, Switzerland), 2013, 13(7): 8679-8694. |
20 | Luo S, Cheng J, Ao H L. Application of LCD-SVD technique and CRO-SVM method to fault diagnosis for roller bearing[J]. Shock and Vibration, 2015(2): 847802. |
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