纺织学报 ›› 2022, Vol. 43 ›› Issue (06): 171-179.doi: 10.13475/j.fzxb.20210505709

• 机械与器材 • 上一篇    下一篇

相依竞争失效模型下细纱机性能退化对成纱质量的影响

邵景峰(), 董梦园   

  1. 西安工程大学 管理学院, 陕西 西安 710048
  • 收稿日期:2021-05-24 修回日期:2021-12-16 出版日期:2022-06-15 发布日期:2022-07-15
  • 作者简介:邵景峰(1980—),男,教授,博士。研究方向为智能信息处理。E-mail: shaojingfeng1980@aliyun.com
  • 基金资助:
    陕西省重点研发计划项目(2020GY-122);陕西省教育厅服务地方专项计划项目(20JC013);西安市科技计划项目(2020KJRC0018)

Influence of performance degradation of spinning frames on yarn quality under dependent competition failure

SHAO Jingfeng(), DONG Mengyuan   

  1. School of Management, Xi'an Polytechnic University, Xi'an, Shaanxi 710048, China
  • Received:2021-05-24 Revised:2021-12-16 Published:2022-06-15 Online:2022-07-15

摘要:

针对细纱机性能退化对纺纱质量影响难以精确表征的问题,首先,利用棉纺生产数据分析了细纱机性能退化对成纱质量指标的影响,研究了质量指标之间的关系。然后,通过细纱机性能退化的参数筛选、指标建立以及突发失效过程预测,对细纱机性能退化过程进行了表征。其次,利用Wiener过程、Weibull分布对细纱机性能退化过程进行了模拟,构建了一种基于Copula函数的细纱机性能退化相依竞争失效模型,并对比分析了模型应用前后纺纱质量关键指标的变化。最后,依托海量棉纺生产数据,验证结果表明构建的相依竞争失效模型较好地解决了细纱机性能退化对纺纱质量影响的问题,而且经模型应用,使得纱线的粗节减少了10.09%,毛羽减少了23.5%,单纱断裂强度提升了4.26%。

关键词: 成纱质量, 相依竞争失效, 细纱机, 退化过程, 可靠性

Abstract:

This study tackles the difficulties in accurate characterization on the influence of the performance degradation of spinning frames on the yarn quality, and the relationship among quality indexes were studied based on data of cotton spinning production. The performance degradation process of spinning frames was characterized by parameter selection, index establishment and sudden failure process prediction. Wiener process and Weibull distribution were adopted to predict the sudden failure process of the performance of the spinning frame, and a copula function based performance degradation dependent competitive failure model of the spinning frame was constructed. The changes of the key yarn quality indexes before and after the application of the model were compared and analyzed. Through exemplar verification, the results show that the dependent competition failure model is able to predict the influence of the performance degradation of spinning frames on spinning quality. After the application of the model, the coarse knot value was decreased by 10.09%, the hairiness value was decreased by 23.5% and the breaking strength of single yarn was increased by 4.26%.

Key words: yarn quality, dependent competition failure, spinning frame, degradation process, reliability

中图分类号: 

  • TS101.8

图1

棉纺质量指标的聚类结果"

表1

JC7.29 tex纱线质量波动试样数据表"

试样
编号
条干
不匀
率/%
细节/
(个·
km-1)
粗节/
(个·
km-1)
毛羽
H
单纱断
裂强度/
(cN·tex-1)
断裂
伸长
率/%
1 14.79 16 57 2.41 5.99 29
2 14.74 25 73 2.96 6.11 0
3 14.89 19 69 2.03 5.96 17
4 14.87 27 81 2.41 5.88 15
5 14.97 20 80 1.35 6.01 13
6 15.08 21 61 2.54 5.94 10
7 14.95 15 68 2.25 6.08 25
8 14.94 16 71 2.23 5.84 18
9 14.97 14 75 2.18 5.92 15
10 14.81 15 50 2.28 6.04 20
11 14.95 11 66 3.22 5.95 5
12 14.92 13 78 2.86 6.07 5
13 15.05 24 108 2.23 6.05 22
14 14.87 54 85 2.19 6.10 12
15 14.94 43 72 2.78 5.91 13
16 15.08 35 87 2.67 6.03 15
17 15.05 40 78 2.17 5.92 20
18 15.06 32 83 2.18 5.94 28
19 14.97 45 108 2.31 6.04 25
20 14.86 57 83 2.17 6.15 15
21 15.07 44 104 2.98 6.05 19
22 14.96 18 88 2.94 6.07 32
23 14.94 55 85 3.02 5.98 33
24 15.01 28 78 2.07 6.12 35
25 15.00 34 80 3.02 5.96 30
26 15.20 26 58 3.01 6.10 48
27 15.87 32 63 2.38 5.89 45
28 14.74 25 56 2.47 5.94 15

图2

JC7.29 tex纱线质量指标关系仿真结果"

表2

细纱机常见故障模式"

序号 细纱机本体 电气系统
1 罗拉头断裂 电动机损坏
2 前罗拉头发热 线路损坏
3 滚盘轴头断裂 电气过热
4 钢领板升降打顿 线路与电缆接触不良
5 主轴轴承发烫 熔断器损坏
6 车头车尾滚动轴承损坏 电动机过载
7 钢丝圈跑道磨损 元器件参数飘移
8 罗拉偏心 发动机报警
9 锭子转速偏差 开关失灵
10 前胶辊损伤 电压不稳

表3

细纱机性能参数"

序号 参数名称 单位
1 锭长 m
2 当前管纱长度 m
3 当前机台总质量 kg
4 锭子速度 r/min
5 前罗拉转速 r/min
6 中罗拉转速 r/min
7 前罗拉线速度 m/min
8 捻度 捻/(10 cm)
9 总牵伸倍数
10 电动机功率 kW
11 后罗拉转速 r/min
12 钢领直径 mm
13 钢领板上升速度 r/min
14 钢领板下降速度 r/min
15 钢领板级升 mm

图3

细纱机性能参数筛选"

图4

综合指标变化趋势"

图5

突发失效可靠度曲线"

图6

概率密度函数"

图7

联合分布函数"

图8

不同模型可靠性曲线"

表4

纱线质量指标数据对比"

指标 应用状态
(指标质量变
化情况)
细纱机工作时间/d
0~10 10~20 20~30 30~40
粗节/ 15.57 13.85 19.28 34.00
(个·km-1) 8.45 9.35 8.50 7.30
毛羽H 2.27 2.45 2.46 2.71
1.46 2.03 1.11 1.25
单纱断裂强度/ 4.35 4.25 3.57 3.53
(cN·tex-1) 5.96 5.95 6.05 6.08
[1] HE S, XUE W, CHEN G, et al. Experimental investigation and simulation of the performance of a pre-twister based on the jet vortex field used for the ring spinning frame[J]. Textile Research Journal, 2019, 89(21/22): 4647-4657.
doi: 10.1177/0040517519839938
[2] LI C, LIU R X, PAN F K. Simulation of reliability prediction based on multiple factors for spinning machine[J]. Autex Research Journal, 2020, 20(1): 17-23.
doi: 10.2478/aut-2019-0009
[3] CUI Y, SONG H, CHENG L, et al. Experimental study of a modified drafting system based on the ring spinning frame[J]. Textile Research Journal, 2021, 91(13/14):1486-1496.
doi: 10.1177/0040517520984977
[4] 宋晓亮, 刘建立, 徐阳, 等. 光电式环锭断纱在线检测系统[J]. 纺织学报, 2014, 35(8):94-98,103.
SONG Xiaoliang, LIU Jianli, XU Yang, et al. Photoelectric online detection system for ring spun yarn breakage[J]. Journal of Textile Research, 2014, 35(8): 94-98, 103.
[5] 杨敏, 谢春萍, 刘新金. 集聚纺纱线结构对成纱质量的影响[J]. 纺织学报, 2015, 36(8):28-32.
YANG Min, XIE Chunping, LIU Xinjin. Effect of compact spinning yarn structure on yarn quality[J]. Journal of Textile Research, 2015, 36 (8): 28-32.
[6] 朱艳萍, 包文杰, 涂晓彤, 等. 改进的经验小波变换在滚动轴承故障诊断中的应用[J]. 噪声与振动控制, 2018, 38(1):199-203.
ZHU Yanping, BAO Wenjie, TU Xiaotong, et al. Application of improved empirical wavelet transform in rolling bearing fault diagnosis[J]. Noise and Vibration Control, 2018, 38 (1): 199-203.
[7] 邵景峰, 马创涛. 多工序递阶的棉纺过程质量智能控制模型[J]. 纺织学报, 2018, 39(7):137-147.
SHAO Jingfeng, MA Chuangtao. Multi process hierarchical quality intelligent control model for cotton spinning process[J]. Journal of Textile Research, 2018, 39 (7): 137-147.
[8] 魏艳红, 谢春萍, 刘新金, 等. 基于大直径软胶辊的细纱牵伸机制及其应用效果[J]. 纺织学报, 2019, 40(10):62-67.
WEI Yanhong, XIE Chunping, LIU Xinjing, et al. Spinning drafting mechanism based on large diameter soft top roller and its application effect[J]. Journal of Textile Research, 2019, 40 (10): 62-67.
[9] SI X S, WANG W, HU C H, et al. A Wiener process based degradation model with a recursive filter algorithm for remaining useful life estimation[J]. Mechanical Systems and Signal Processing, 2013, 35(1/2): 219-237.
doi: 10.1016/j.ymssp.2012.08.016
[10] LI J, WANG Z, ZHANG Y, et al. A nonlinear Wiener process degradation model with autoregressive errors[J]. Reliability Engineering & System Safety, 2018, 173(5): 48-57.
doi: 10.1016/j.ress.2017.11.003
[11] 牛一凡, 邵景峰. 基于非线性数据融合的设备多阶段寿命预测[J]. 信息与控制, 2019, 48(6):729-737.
doi: 10.13976/j.cnki.xk.2019.8639
NIU Yifan, SHAO Jingfeng. Multi stage life prediction of equipment based on nonlinear data fusion[J]. Information and Control, 2019, 48 (6): 729-737.
doi: 10.13976/j.cnki.xk.2019.8639
[12] 王新刚, 张鑫垚, 杨禄杰, 等. 竞争失效条件下针对磨损退化数据的刀具可靠性分析[J]. 中国机械工程, 2020, 31(14):1672-1677,1746.
doi: 10.3969/j.issn.1004-132X.2020.14.005
WANG Xingang, ZHANG Xinyao, YANG Lujie, et al. Tool reliability analysis for wear degradation data under competitive failure[J]. China Mechanical Engineering, 2020, 31 (14): 1672-1677, 1746.
doi: 10.3969/j.issn.1004-132X.2020.14.005
[13] 胡昌华, 施权, 司小胜, 等. 数据驱动的寿命预测和健康管理技术研究进展[J]. 信息与控制, 2017, 46(1):72-82.
HU Changhua, SHI Quan, SI Xiaosheng, et al. Research progress of data driven life prediction and health management technology[J]. Information and Control, 2017, 46 (1): 72-82.
[14] 杨春波, 陶青, 张健, 等. 基于综合健康指数的设备状态评估[J]. 电力系统保护与控制, 2019, 47(10):104-109.
YANG Chunbo, TAO Qing, ZHANG Jian, et al. Equipment condition assessment based on comprehensive health index[J]. Power System Protection and Control, 2019, 47 (10): 104-109.
[15] 张静, 李柠, 李少远, 等. 基于数据的风电机组发电机健康状况评估[J]. 信息与控制, 2018, 47(6): 694-701,712.
doi: 10.13976/j.cnki.xk.2018.7488
ZHANG Jing, LI Ning, LI Shaoyun, et al. Health assessment of wind-turbine generator based on date[J]. Information and Control, 2018, 47(6): 694-701, 712.
doi: 10.13976/j.cnki.xk.2018.7488
[16] GAO H, CUI L, DONG Q. Reliability modeling for a two-phase degradation system with a change point based on a Wiener process[J]. Reliability Engineering & System Safety, 2020, 193(1):1-9.
[17] 戴林送, 王枞. 基于广义逆威布尔分布的应力强度模型可靠度估计[J]. 统计与决策, 2020, 36(9):25-29.
DAI Linsong, WANG Zong. Reliability estimation of stress intensity model based on generalized inverse Weibull distribution[J]. Statistics & Decision, 2020, 36 (9): 25-29.
[18] 王燕, 师义民. 加速寿命试验下相依竞争失效模型的统计分析[J]. 统计与决策, 2020, 36(16):184-188.
WANG Yan, SHI Yimin. Statistical analysis of dependent competition failure model under accelerated life test[J]. Statistics & Decision, 2020, 36 (16): 184-188.
[19] 杨梓鑫, 薛源, 孙畅, 等. 基于Elman神经网络和Copula函数的多维装备效能评估模型[J]. 兵工学报, 2020, 41(8):1633-1645.
doi: 10.3969/j.issn.1000-1093.2020.08.018
YANG Zixin, XUE Yuan, SUN Chang, et al. Multidimensional equipment effectiveness evaluation model based on Elman neural network and Copula function[J]. Acta Armamentarii, 2020, 41 (8): 1633-1645.
doi: 10.3969/j.issn.1000-1093.2020.08.018
[20] 黄文平, 周经伦, 宁菊红, 等. 基于变失效阈值的竞争失效可靠性模型[J]. 系统工程与电子技术, 2017, 39(4): 941-946.
HUANG Wenping, ZHOU Jinglun, NING Juhong, et al. Reliability model for competing failure with shift threshold[J]. Journal of Systems Engineering and Electronics, 2017, 39(4): 941-946.
[21] GAO H, KONG D, SUN Y. Reliability modeling and analysis for systems governed by multiple competing failures processes[J]. Journal of Risk and Reliability, 2020. DOI: 10.1177/1748006X2097447.
doi: 10.1177/1748006X2097447
[22] 贺志远, 吕卫民, 胡文林. 基于Copula函数的导弹部件非线性退化研究[J]. 弹箭与制导学报, 2020, 40(3):75-78, 84.
HE Zhiyuan, LÜ Weimin, HU Wenling. Research on nonlinear degradation of missile components based on Copula function[J]. Journal of Projectiles, Rockets, Missiles and Guidance, 2020, 40(3): 75-78, 84.
[23] 贾祥, 程志君, 郭波. 基于信息熵和Bayes理论的高可靠性产品可靠性评估[J]. 系统工程理论与实践, 2020, 40(7):1918-1926.
doi: 10.12011/1000-6788-2019-1306-09
JIA Xiang, CHENG Zhijun, GUO Bo. Reliability evaluation of high reliability products based on information entropy and Bayes theory[J]. Systems Engineering-Theory & Practice, 2020, 40 (7): 1918-1926.
[24] 胡启国, 高展. 多元参数退化的系统相关竞争失效可靠性评估方法[J]. 西北工业大学学报, 2019, 37(6):1191-1199.
HU Qiguo, GAO Zhan. Reliability evaluation method of system dependent competitive failure with multi parameter degradation[J]. Journal of Northwestern Polytechnical University, 2019, 37 (6): 1191-1199.
doi: 10.1051/jnwpu/20193761191
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