基于N-BEATS神经网络的滚动轴承剩余寿命预测

doi:10.3969/j.issn.1000-1158.2023.08.14

Abstract
Figure/Table
References (20)
Related Citation (15)

Download: PDF (843 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract In order to effectively predict the remaining service life of the bearing, and to solve the problem of complex and unstable signal sequences collected during the prediction, resulting in low prediction accuracy and performance, the empirical mode decomposition is used to adaptively decompose the signal sequence, the dynamic time warping algorithm is used to screen the main degradation features, and the trend features of the signal sequence are extracted. The deep neural network N-BEATS with residual principle is used for prediction. For the problem of less prediction history data, the prediction structure combining recursion and direct is used to predict the remaining life in multiple steps. Comparing N-BEATS with long-term and short-term memory neural network and grey prediction model, the results show that the average absolute error of the prediction results of the proposed method is increased by 3.2% and 3.3% respectively compared with LSTM and grey prediction model under different working conditions, the relative root mean square error increased by 3.5% and 3.1% respectively.

Key words： metrology rolling bearing remaining useful life dynamic time warping N-BEATS neural network

Received: 11 May 2022 Published: 22 August 2023

PACS:	TB936
	TB973

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	SHI Pei-ming
	SU Shi-min
	MA Hui-zhong
	XU Xue-fang
	HAN Dong-ying

Cite this article:

SHI Pei-ming,SU Shi-min,MA Hui-zhong, et al. Prediction of Residual Life of Rolling Bearing Based on N-BEATS Neural Network[J]. Acta Metrologica Sinica, 2023, 44(8): 1240-1247.

URL:

http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2023.08.14 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2023/V44/I8/1240

［2］	石怀涛,尚亚俊,白晓天,等. 基于贝叶斯优化的SWDAE-LSTM滚动轴承早期故障预测方法研究［J］. 振动与冲击, 2021, 40(18):286-297.
	Shi H T, Shang Y J, Bai X T, et al. Early fault prediction method combining SWDAE and LSTM for rolling bearings based on Bayesian optimization［J］. Journal of Vibration and Shock, 2021, 40(18):286-297.
	Zhang X L, Wang B J, Ma M, et al. A Review of Life Prediction for Roller Bearing［J］. Machinery Design & Manufacture, 2015(10):221-224.
［5］	陈剑, 刘圆圆, 黄凯旋, 等. 基于奇异值分解和独立分量分析的滚动轴承故障诊断方法［J］. 计量学报, 2022, 43(6):777-785.
［6］	陈维兴, 崔朝臣, 李小菁, 等. 基于多种小波变换的一维卷积循环神经网络的风电机组轴承故障诊断［J］. 计量学报. 2021, 42(5):615-622.
	Meng Z, Wang X Y, Zhou M J, et al. Method of Roatating Machinery Fault Diagnoise Based on Gabor Transfer and Blind Source Separation［J］. Acta Metrologica Sinica, 2016, 37(5):499-504.
［4］	张小丽, 王保建, 马猛, 等. 滚动轴承寿命预测综述［J］. 机械设计与制造, 2015(10):221-224.
［7］	汪朝海, 蔡晋辉, 曾九孙. 基于经验模态分解和主成分分析的滚动轴承故障诊断研究［J］. 计量学报, 2019, 40(6):1077-1082.
	Wang C H, Cai J H, Zeng J S. Research on Fault Diagnosis of Ｒolling Bearing Based on Empirical Mode Decomposition and Principal Component Analysis［J］. Acta Metrologica Sinica, 2019, 40(6):1077-1082.
［9］	Sakoe H, Chiba S. Dynamic programming algorithm optimization for spoken word recognition［J］. IEEE transactions onacoustics, speech, and signal processing, 1978, 26(1): 43-49.
［10］	徐洲常, 王林军, 刘洋, 等. 采用改进回归型支持向量机的滚动轴承剩余寿命预测方法［J］. 西安交通大学学报, 2022, 56(3):197-205.
［11］	黎慧, 张国文. 基于灰色模型的滚动轴承剩余寿命预测［J］. 机械设计与研究, 2018, 34(1):113-116.
［12］	Singleton R K, Strangas E G, Aviyente S. Time-frequency complexity based remaining useful life (RUL) estimationfor bearing faults［C］//2013 9th IEEE Intern-ational Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED). IEEE,2013:600-606.
	Yang Y, Zhang N, Cheng J S, et al. Global parameters dynamic learning deep belief networks and its appication in rolling bearing life prediction［J］. Journal of Vibration and Shock, 2019, 38(10):199-205.
［14］	陈保家, 陈正坤, 陈学良, 等. 基于注意力TCN的滚动轴承剩余使用寿命预测方法［J］. 电子测量技术. 2021,44(24):153-160.
［15］	唐旭, 徐卫晓, 谭继文, 等. 基于LSTM的滚动轴承剩余使用寿命预测［J］. 机械设计, 2019, 36(S1):117-119.
	Tang X, Xu W D, Tan J W, et al. Prediction for remaining useful life of rolling bearings based on Long Short-Term Memory［J］. Journal of Machine Design, 2019, 36(S1):117-119.
［17］	Taieb S B, Bontempi G, Atiya A F, et al. A review and comparison of strategies for multi-step ahead time seriesforecasting based on the NN5 forecasting competition［J］. Expert systems with applications, 2012,39(8):7067-7083.
［19］	童晓阳, 杨明杰, 张广骁. 基于改进DTW的行波波形相似性的高压直流输电线路保护方案［J］. 中国电机工程学报, 2020, 40(12):3878-3888.
［3］	孟宗, 王晓燕, 周明军, 等. 基于Gabor变换和盲源分离的旋转机械故障诊断方法［J］. 计量学报, 2016, 37(5):499-504.
［8］	Guo R, Wang Y, Zhang H, et al. Remaining useful life prediction for rolling bearings using EMD-RISI-LSTM ［J］. IEEE Transactions on Instrumentation and Measurement, 2021,70:1-12.
	Chen B J, Chen Z K, Chen X L, et al. Prediction method of remaining useful life of rolling bearing based on attentional temporal convolutional network ［J］. Electronic Measurement Technology, 2021,44(24):153-160.
［1］	Meng Z, Li J, Yin N, et al. Remaining useful life prediction of rolling bearing using fractal theory［J］. Measurement, 2020, 156:107572.
	Chen W X, Cui C C, Li X J, et al. Bearing Fault Diagnosis of Wind Turbine Based on Multi-wavelet-1D Convolutional LSTM［J］. Acta Metrologica Sinica, 2021, 42(5):615-622.
［13］	杨宇, 张娜, 程军圣. 全参数动态学习深度信念网络在滚动轴承寿命预测中的应用［J］. 振动与冲击, 2019, 38(10):199-205.
	Tang X Y, Yang M J, Zhang G X. A Protection Scheme for HVDC Transmission Line Based on Traveling Waveform Similarity Using Improved DTWAlgorithm［J］. Proceedings of the CSEE,. 2020, 40(12):3878-3888.
	Chen J, Liu Y Y, Huang K X, et al. Rolling Bearing Fault Diagnosis Method Based on Singular Value Decomposition and Independent Component Analysis［J］. Acta Metrologica Sinica, 2022, 43(6):777-785.
	Li H, Zhang G W. Rolling Bearing Ｒesidual Life Prediction Based on Grey Model［J］. Machine Design &Research, 2018, 34(1):113-116.
［18］	Huang N E, Shen Z, Long S R, et al. The empiricalmode decomposition and the Hilbert spectrum for no-nlinearand non-stationary time series analysis［J］. Proceedings of the Royal Society of London, Series A: mathematical, physicalandengineering sciences, 1998, 454(1971):903-995.
	Xu Z C, Wang L J, Liu Y, et al. A Prediction for Remaning Life of Rolling Bearing Using Improved Rgression Support Vector Machien［J］. Journal of Xi'an Jiaotong University, 2022,56(3):197-205.
［16］	Oreshkin B N,Dudek G, Peka P, et al. N-BEATS neural network for midterm electricity load forecasting［J］. Applied Energy, 2021, 293:116918.
［20］	Nectoux P, Gouriveau R, Medjaher K, et al. An ex perimental platform for bearings accelerated degradation tests［C］//Proceedings of the IEEE International Conference on Prognostics and Health Management. Beijing, China, 2012: 23-25.