一种自适应的控制棒驱动机构滚轮丝杠副剩余寿命预测方法研究

李琳; 张黎明; 焦猛; 张帅; 王本猛

doi:10.13832/j.jnpe.2022.03.0196

一种自适应的控制棒驱动机构滚轮丝杠副剩余寿命预测方法研究

doi: 10.13832/j.jnpe.2022.03.0196

李琳^1,,
张黎明^2, ,,
焦猛²,
张帅²,
王本猛³

1.
武昌理工学院，武汉，430070
2.
海军工程大学，武汉，430033
3.
92001部队，山东青岛，266000

基金项目: 核反应堆系统设计技术重点实验室开放基金（LRST2017202）；湖北省自然科学基金面上项目（2019cfc889）

详细信息

作者简介:
李　琳（1979—），男，副教授，主要研究工作为机械设备状态检测与故障诊断，E-mail: 23541959@qq.com

通讯作者:
张黎明，E-mail: zlm060101@aliyun.com

中图分类号: TL351.5
计量
- 文章访问数: 304
- HTML全文浏览量: 81
- PDF下载量: 37
- 被引次数: 0
出版历程
- 收稿日期: 2021-03-25
- 录用日期: 2021-03-25
- 修回日期: 2022-03-14
- 刊出日期: 2022-06-07

Research on an Adaptive Method for Predicting the Remaining Life of the Roller Screw Pair of the Control Rod Drive Mechanism

1.
Wu Chang University of Technology, Wuhan, 430070, China
2.
Naval University of Engineering, Wuhan, 430033, China
3.
Unit 92001, Qingdao, Shandong, 266000, China

摘要

摘要: 针对反应堆控制棒驱动机构（CRDM）滚轮丝杠副剩余使用寿命（RUL）预测中如何选取有效的健康状态指标和合理构建预测模型的难题，提出了一种新的滚轮丝杠副RUL预测模型。采用基于生成拓扑映射算法（GTM）的负对数似然概率（NLLP）指标作为滚轮丝杠副的健康状态指标，利用K均值聚类算法对NLLP指标进行状态划分。利用历史数据和在线监测数据构建基于Markov模型和最小均方算法（LMS）的自适应预测模型，根据设定的阈值预测得出剩余寿命。通过实验验证，结果表明本文选取的健康状态指标能够有效地反映设备状态，所给出的自适应预测模型比一般的预测模型的预测精度高，为合理构建RUL预测模型提供了依据。
- 控制棒驱动机构（CRDM） /
- 剩余使用寿命（RUL) /
- 生成拓扑映射算法（GTM） /
- K均值聚类算法 /
- 自适应预测模型
Abstract: Aiming at the problem of how to select effective health indicators and reasonably construct the prediction model in the remaining useful life (RUL) prediction of roller screw pair of reactor control rod drive mechanism (CRDM), a new RUL prediction model of roller screw pair is proposed. The negative logarithmic likelihood probability (NLLP) index based on the generated topology mapping algorithm (GTM) is used as the health index of the roller screw pair, and the K-means clustering algorithm is used to divide the NLLP index. Using historical data and online monitoring data, an adaptive prediction model based on Markov model and least mean square algorithm (LMS) is constructed, and the remaining life is predicted according to the set threshold. Through experimental verification, the results show that the health status indicators selected in this paper can effectively reflect the equipment status. The prediction accuracy of the proposed adaptive prediction model is higher than that of the general prediction model, which provides a basis for the reasonable construction of RUL prediction model.
- Control rod drive mechanism(CRDM) /
- Remaining useful life (RUL) /
- Generative topographic mapping (GTM) /
- K means clustering algorithm /
- Adaptive prediction model

HTML全文

图 1 自适应预测流程图

Figure 1. Flow Chart of the Adaptive Prediction

下载: 全尺寸图片幻灯片

图 2 4组数据全寿命峭度

Figure 2. Kurtosis Map of the Full Life of Four Groups of Data

下载: 全尺寸图片幻灯片

图 3 4组数据的NLLP指标

Figure 3. NLLP Index Map of Four Groups of Data

下载: 全尺寸图片幻灯片

图 4 基于自适应模型的预测图

Figure 4. Prediction Map Based on Adaptive Model

下载: 全尺寸图片幻灯片

图 5 基于在线监测数据预测模型的预测图

Figure 5. Prediction Map of the Prediction Model Based on Online Monitoring Data Prediction Model

下载: 全尺寸图片幻灯片

图 6 基于历史数据预测模型的预测图

Figure 6. Prediction Map of the Prediction Model Based on Historical Data

下载: 全尺寸图片幻灯片

参考文献(14)

[1]	CHOPRA O K, SHACK W J. Effects of LWR environments on fatigue life of carbon and low-alloy steels: ANL/ET/CP-84133[R]. New York: American Society for Testing and Materials, 1995.
[2]	YUKAWA S, JONES D P, MEHTA H S. Fatigue and crack growth: environmental effects, modeling studies, and design considerations[C]//Presented at the ASME/JSME Pressure Vessels and Piping Conference. Honolulu, Hawaii: American Society of Mechanical Engineers, 1995.
[3]	CHOPRA O K, SHACK W J. Evaluation of effects of LWR coolant environments on fatigue life of carbon and low-alloy steels[C]//Symposium on Effects of the Environment on the Initiation of Crack Growth. Orlando, FL, 1997.
[4]	张黎明,李琳,洪力阳,等. 基于奇异值谱熵复杂度的CRDM滚轮磨损程度识别研究[J]. 核动力工程,2019, 40(4): 108-112.
[5]	YAQUB M F, GONDAL I, KAMRUZZAMAN J. Multi-step support vector regression and optimally parameterized wavelet packet transform for machine residual life prediction[J]. Journal of Vibration and Control, 2013, 19(7): 963-974. doi: 10.1177/1077546311435349
[6]	秦丽晔,吴素君,赵海涛,等. 航空发动机机匣损伤容限评估及剩余寿命预测[J]. 北京航空航天大学学报,2011, 37(7): 895-900.
[7]	HU Y, BARALDI P, DI MAIO F, et al. Online performance assessment method for a model-based prognostic approach[J]. IEEE Transactions on Reliability, 2016, 65(2): 718-735. doi: 10.1109/TR.2015.2500681
[8]	夏邓成. 基于LSSVR-HSMM的滚动轴承剩余使用寿命预测研究[D]. 武汉: 华中科技大学, 2019.
[9]	焦猛,蔡琦,张黎明,等. 基于评价函数和BP网络的CRDM滚轮状态评估方法[J]. 核动力工程,2021, 42(1): 133-137.
[10]	YU J B. A nonlinear probabilistic method and contribution analysis for machine condition monitoring[J]. Mechanical Systems and Signal Processing, 2013, 37(1-2): 293-314. doi: 10.1016/j.ymssp.2013.01.010
[11]	BISHOP C M, SVENSÉN M, WILLIAMS C K I. GTM: the generative topographic mapping[J]. Neural Computation, 1998, 10(1): 215-234. doi: 10.1162/089976698300017953
[12]	BISHOP C M, SVENSÉN M, WILLIAMS C K I. Developments of the generative topographic mapping[J]. Neurocomputing, 1998, 21(1-3): 203-224. doi: 10.1016/S0925-2312(98)00043-5
[13]	HARTIGAN J A, WONG M A. Algorithm AS 136: A k-means clustering algorithm[J]. Journal of the Royal Statistical Society. Series C (Applied Statistics), 1979, 28(1): 100-108.
[14]	LIU Q M, DONG M, PENG Y. A novel method for online health prognosis of equipment based on hidden semi-Markov model using sequential Monte Carlo methods[J]. Mechanical Systems and Signal Processing, 2012, 32: 331-348. doi: 10.1016/j.ymssp.2012.05.004