[1]刘东林,秦玉焘,麻浩军,等.基于马尔科夫链的车桥齿轮剩余寿命预测研究[J].机械与电子,2023,41(03):76-80.
　LIU Donglin,QIN Yutao,MA Haojun,et al.Research on Residual Life Prediction of Axle Gear Based on Markov Chain[J].Machinery & Electronics,2023,41(03):76-80.
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基于马尔科夫链的车桥齿轮剩余寿命预测研究()
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《机械与电子》[ISSN:1001-2257/CN:52-1052/TH]

卷:
41
期数:
2023年03期
页码:
76-80
栏目:
机电一体化技术
出版日期:
2023-03-31

文章信息/Info

Title:
Research on Residual Life Prediction of Axle Gear Based on Markov Chain
文章编号:
1001-2257 ( 2023 ) 03-0076-05
作者:
刘东林 1 ; 秦玉焘 1 ; 麻浩军 1 ; 徐 中 1 ; 郭美娜 2
1. 中核核电运行管理有限公司,浙江 海盐 314399 ;
2. 苏州微著设备诊断技术有限公司,江苏 苏州 215211
Author(s):
LIU Donglin1 ;  QIN Yutao1 ;  MA Haojun1 ;  XU Zhong1 ;  GUO Meina 2
( 1.CNNC Nuclear Power Operation Management Corporation , Haiyan 314399 , China ;
2.Suzhou Veizu Equipment Diagnosis Technology Co. , Ltd. , Suzhou 215211 , China )
关键词:
齿轮; 剩余寿命预测; 马尔科夫链; k-means 聚类
Keywords:
gear ;  remaining useful life-prediction ;  markov chain ;  k-means clustering
分类号:
TP391
文献标志码:
A
摘要:
为解决车桥齿轮剩余寿命随时间变化趋势难以预测的问题,提出一种基于马尔科夫链的齿轮剩余寿命预测模型。该方法首先从采集的原始振动信号中提取齿轮的 1~3 倍啮合频率边频能量作为退化指标,再求取退化指标的增量序列并用于寿命预测;然后通过聚类方法对增量序列进行状态划分,从而获得状态转移概率矩阵;从而建立基于马尔科夫链的剩余寿命预测模型。采用车桥耐久试验的全寿命数据验证模型的有效性,结果表明,提出的模型在车桥开始退化后的预测平均相对误差为 9.5% ,相比于传统的马尔科夫模型具有更高的预测精度。
Abstract:
To solve the problem that it is difficult to predict the change trend of residual life of axle gear with time , a remaining useful life prediction model of gear based on Markov chain was proposed.Firstly,3 times of the meshing frequency side frequency energy was extracted from the original vibration signals as the degradation index , and then the incremental sequence of the degradation index was obtained and used for life prediction.Then the state transition probability matrix is obtained by classifying the incremental sequence by clustering method.Then the model of residual life prediction based on Markov chain is established.The validity of the model was verified by the life-span data of the vehicle bridge durability experiment.The results show that the average relative error of the proposed model is 9.5% after the vehicle bridge begins to degrade , which has a higher prediction accuracy than the traditional Markov model.

参考文献/References:

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[ 9 ] 滕红智,赵建民,贾希胜,等 . 基于 CHMM 的齿轮箱状态识别研究 [ J ] . 振动与冲击, 2012 , 31 ( 5 ): 92-96 , 127.
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备注/Memo

备注/Memo:
收稿日期: 2022-05-18
作者简介:刘东林 ( 1987- ),男,黑龙江双鸭山人,高级工程师,研究方向为可靠性数据分析。
更新日期/Last Update: 2023-04-07