[1]佘道明,贾民平,张 菀.一种新型深度自编码网络的滚动轴承健康评估方法[J].东南大学学报(自然科学版),2018,48(5):801-806.[doi:10.3969/j.issn.1001-0505.2018.05.004]
 She Daoming,Jia Minping,Zhang Wan.Deep auto-encoder network method for health assessment of rolling bearings[J].Journal of Southeast University (Natural Science Edition),2018,48(5):801-806.[doi:10.3969/j.issn.1001-0505.2018.05.004]
点击复制

一种新型深度自编码网络的滚动轴承健康评估方法()
分享到:

《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
48
期数:
2018年第5期
页码:
801-806
栏目:
机械工程
出版日期:
2018-09-20

文章信息/Info

Title:
Deep auto-encoder network method for health assessment of rolling bearings
作者:
佘道明贾民平张 菀
东南大学机械工程学院, 南京 211189
Author(s):
She Daoming Jia Minping Zhang Wan
School of Mechanical Engineering, Southeast Engineering, Nanjing 211189, China
关键词:
深度自编码 健康指标 最小量化误差 融合评价准则
Keywords:
deep auto-encoder heath index minimum quantization error fused evaluation rules
分类号:
TH133.3
DOI:
10.3969/j.issn.1001-0505.2018.05.004
摘要:
为了准确描述滚动轴承性能退化的动态过程,结合深度学习强大特征提取能力的优势,提出了一种新型深度自编码和最小量化误差方法相结合的滚动轴承全寿命健康评估方法.用深度自编码模型对原始特征进行压缩提取,将压缩特征按趋势进行排序,选取趋势大的特征运用最小量化误差方法构建健康指标.针对基于一个度量的评价准则常具有偏差的问题,提出基于遗传算法的融合评价准则.2组实例分析结果表明,用该方法构建的健康指标的趋势值、单调性值、鲁棒性值、融合评价准则值都大于单层的自编码模型(AE)和传统的PCA降维方法,第1个实例中,该方法构建的健康指标融合评价准则值比PCA,AE方法分别增加了13.30%,3.17%;第2个实例中,该方法构建的健康指标融合评价准则值比PCA,AE方法分别增加了9.68%,3.85%.基于遗传算法的融合评价准则比单一的评价准则更具有说服力.
Abstract:
To describe the dynamic process of rolling bearing performance degradation accurately, considering the advantage of strong feature extraction ability of deep learning, a novel method combining deep auto-encoder(DAE)with minimum quantization error(MQE)was proposed to evaluate the whole life health of rolling bearings. The original feature was compressed and extracted by the DAE model, and the compressed feature was sorted according to the trend. Then, the feature with large trend was selected to construct the health index by using the MQE method. The evaluation criterion based on a single metric was often biased, so a fused evaluation criterion based on genetic algorithm was proposed. The superiority of the proposed method was demonstrated by comparison with the single-layer auto-encoder(AE)and principal components analysis(PCA)method. Two groups of examples show that the health index constructed by the proposed method is superior to other two methods in four aspects: trendability, monotonicity, robustness, and values of the fused criterion. In the first example, the values of the fused criterion of the health index constructed by the proposed method are 13.30% and 3.17% higher than those of PCA and AE method, respectively. In the second example, the value of fused criterion of health index constructed by the proposed method are 9.68% and 3.85% higher than those of PCA and AE method, respectively. The fused evaluation criterion based on the genetic algorithm is more persuasive than the single evaluation criterion.

参考文献/References:

[1] Lei Y G, Li N P, Guo L, et al. Machinery health prognostics: A systematic review from data acquisition to RUL prediction[J]. Mechanical Systems and Signal Processing, 2018, 104: 799-834. DOI:10.1016/j.ymssp.2017.11.016.
[2] Boskoski P, Gasperin M, Petelin D, et al. Bearing fault prognostics using Rényi entropy based features and Gaussian process models[J]. Mechanical Systems & Signal Processing,2015, 52-53: 327-337.
[3] Yu J B. Bearing performance degradation assessment using locality preserving projections[J]. Expert Systems with Applications, 2011, 38(6): 7440-7450. DOI:10.1016/j.eswa.2010.12.079.
[4] Wang D, Tse P W. Prognostics of slurry pumps based on a moving-average wear degradation index and a general sequential Monte Carlo method[J]. Mechanical Systems and Signal Processing, 2015, 56-57: 213-229. DOI:10.1016/j.ymssp.2014.10.010.
[5] Qiu H, Lee J, Lin J, et al. Robust performance degradation assessment methods for enhanced rolling element bearing prognostics[J]. Advanced Engineering Informatics, 2003, 17(3/4): 127-140. DOI:10.1016/j.aei.2004.08.001.
[6] Hong S, Zhou Z, Zio E, et al. Condition assessment for the performance degradation of bearing based on a combinatorial feature extraction method[J]. Digital Signal Processing, 2014, 27(1): 159-166. DOI:10.1016/j.dsp.2013.12.010.
[7] Widodo A, Yang B S. Application of relevance vector machine and survival probability to machine degradation assessment[J]. Expert Systems with Applications, 2011, 38(3): 2592-2599. DOI:10.1016/j.eswa.2010.08.049.
[8] Guo L, Li N, Jia F, et al. A recurrent neural network based health indicator for remaining useful life prediction of bearings[J]. Neurocomputing, 2017, 240(C): 98-109. DOI:10.1016/j.neucom.2017.02.045.
[9] Liao L, Jin W, Pavel R. Enhanced restricted Boltzmann machine with prognosability regularization for prognostics and health assessment[J]. IEEE Transactions on Industrial Electronics, 2016, 63(11): 7076-7083. DOI:10.1109/tie.2016.2586442.
[10] Hasani R M, Wang G, Grosu R. An automated auto-encoder correlation-based health-monitoring and prognostic method for machine bearings[EB/OL].(2017-03-18)[2018-05].https://arxiv.org/abs/1703.06272.
[11] LeCun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015, 521(7553): 436-444. DOI:10.1038/nature14539.
[12] Yu J B, Wang S J. Using minimum quantization error chart for the monitoring of process states in multivariate manufacturing processes[J]. Computers & Industrial Engineering, 2009, 57(4): 1300-1312. DOI:10.1016/j.cie.2009.06.009.
[13] Zhang B, Zhang L J, Xu J W. Degradation feature selection for remaining useful life prediction of rolling element bearings[J]. Quality and Reliability Engineering International, 2016, 32(2): 547-554. DOI:10.1002/qre.1771.
[14] Nectoux P, Gouriveau R, Medjaher K, et al. PRONOSTIA: An experimental platform for bearings accelerated degradation tests[C]// IEEE International Conference on Prognostics and Health Management. Colorado, USA,2012:1-8.
[15] Lei Y, Li N, Gontarz S, et al. A model-based method for remaining useful life prediction of machinery[J]. IEEE Transactions on Reliability, 2016, 65(3): 1314-1326.
[16] Javed K, Gouriveau R, Zerhouni N, et al. A feature extraction procedure based on trigonometric functions and cumulative descriptors to enhance prognostics modeling[C]// Prognostics and Health Management IEEE.Gaithersburg, USA, 2013:1-7.

备注/Memo

备注/Memo:
收稿日期: 2018-03-09.
作者简介: 佘道明(1989—),男,博士生;贾民平(联系人),男,博士,教授,博士生导师,mpjia@seu.edu.cn.
基金项目: 国家自然科学基金资助项目(51675098)、江苏省研究生科研与实践创新计划资助项目(KYCX18_0066).
引用本文: 佘道明,贾民平,张菀.一种新型深度自编码网络的滚动轴承健康评估方法[J].东南大学学报(自然科学版),2018,48(5):801-806. DOI:10.3969/j.issn.1001-0505.2018.05.004.
更新日期/Last Update: 2018-09-20