Life State Recognition of Space Rolling Bearings Based on Dual Scale Flexible Prototype Transfer Network

doi:10.3901/JME.2022.21.114

Abstract

Abstract: Aiming at the problem that the life state recognition accuracy of the existing life state recognition methods of space rolling bearings is low due to the large difference of sample distribution and the unbalanced number of samples in different life states under variable working conditions, a novel life state recognition method of space rolling bearings based on dual scale flexible prototype transfer network (DSFPTN) is proposed. In the proposed DSFPTN, a dual scale flexible domain sensing module is constructed and embedded in feature extractor to enhance the feature extractor to explore private features in different domains, thus improving the feature extractor to learn sample features in source and target domains of space rolling bearings; moreover, the same-domain imprecise prototype learning is designed to prevent the indiscriminate feature learning and incorrect clustering of cross domain samples, thus increasing the discrimination of heterogeneous samples in two domains; additionally, the two-domain prototype transfer mechanism is built to obtain the domain-invariant prototype and realize the transfer from source domain prototype to target domain prototype; finally, the dual classifiers after loading the domain-invariant prototype are used to align the distribution between two domains, and calculate the similarity between testing samples in target domain and domain-invariant prototype to complete the classification of testing samples in target domain of space rolling bearings, which can improve the recognition accuracy of various life state samples when the number of samples in different life states is unbalanced. The instances of life state recognition of space rolling bearings in space environment simulated on the ground verify the effectiveness of the proposed life state recognition method based on DSFPTN. To sum up, the constructions of the dual scale flexible domain sensing module, the same domain imprecise prototype, the two-domain prototype transfer mechanism, and the dual classifiers loaded with domain-invariant prototype make DSFPTN use only the unbalanced labeled samples in source domain of space rolling bearings to recognize the life states of testing samples in target domain with high accuracy under the large difference in sample distribution and the unbalanced number of samples in different life states.

Key words: space rolling bearings, life state recognition, dual scale flexible domain sensing module, same-domain imprecise prototype learning, two-domain prototype transfer, dual classifiers

CLC Number:

TH133
TH17

WANG Teng, LI Feng, LUO Ling, TANG Baoping. Life State Recognition of Space Rolling Bearings Based on Dual Scale Flexible Prototype Transfer Network[J]. Journal of Mechanical Engineering, 2022, 58(21): 114-125.

References

[1] DONG Shuzhi, WEN Guangrui, LEI Zihao, et al. Transfer learning for bearing performance degradation assessment based on deep hierarchical features[J]. ISA Transactions, 2021, 108:343-355.
[2] ZHANG Li, GUO Liang, GAO HongLi, et al. Instance-based ensemble deep transfer learning network:a new intelligent degradation recognition method and its application on ball screw[J]. Mechanical Systems and Signal Processing, 2020, 140:106681.
[3] DONG Shaojiang, SHENG Jinlu, TANG Baoping, et al. Bearings in simulated space conditions running state detecton based on tsallis entropy-KPCA and optimized fuzzy C-means model[J]. Noise Control Engineering Journal, 2017, 65(2):62-70.
[4] 陈仁祥, 陈思杨, 杨黎霞, 等. 基于振动敏感时频特征的航天轴承寿命状态识别方法[J]. 振动与冲击, 2016, 35(17):134-139. CHEN Renxiang, CHEN Siyang, YANG Lixia, et al. Life state recognition method for space bearings based on sensitive time-frequency features of vibration[J]. Journal of Vibration and Shock, 2016, 35(17):134-139.
[5] MIAO Xuewen, NIU Cong, YANG Yun, et al. Grade-life prognostic model of aircraft engine bearing[J]. Transactions of Nanjing University of Aeronautics & Astronautics, 2012(2):73-80.
[6] LI Xiang, ZHANG Wei, MA Hui, et al. Partial transfer learning in machinery cross-domain fault diagnostics using class-weighted adversarial networks[J]. Neural Networks, 2020, 129:313-322.
[7] 雷亚国, 杨彬, 杜兆钧, 等. 大数据下机械装备故障的深度迁移诊断方法[J]. 机械工程学报, 2019, 55(7):1-8. LEI Yaguo, YANG Bin, DU Zhaojun, et al. Deep transfer diagnosis method for machinery in big data rra[J]. Journal of Mechanical Engineering, 2019, 55(7):1-8.
[8] ZHOU Funa, YANG Shuai, FUJITAj H, et al. Deep learning fault diagnosis method based on global optimization gan for unbalanced data[J]. Knowledge-based Systems, 2020, 187:104837.
[9] ZHOU Qiang, ZHOU Wenan, WANG Shirui, et al. Multiple adversarial networks for unsupervised domain adaptation[J]. Knowledge-based Systems, 2021, 212:106606.
[10] SAITO K, WATANABE K, USHIKU Y, et al. Maximum classifier discrepancy for unsupervised domain adaptation[J]. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2018:3723-3732.
[11] WU Yuxin, HE Kaiming. Group normalization[J]. International Journal of Computer Vision, 2019, 128:742-755.
[12] SAITO K, KIM D, SCLAROFF S, et al. Semi-supervised domain adaptation via minimax entropy[J]. Proceedings of the IEEE International Conference on Computer Vision, 2019:8049-8057.
[13] QIU Hai, LEE J, LIN Jing, et al. Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics[J]. Journal of Sound & Vibration, 2006, 289:1066-1090.
[14] HELMI H, FOROUZANTABAR A. Rolling bearing fault detection of electric motor using time domain and frequency domain features extraction and ANFIS[J]. IET Electric Power Applications, 2019, 13(5):662-669.
[15] 丁锋, 何正嘉, 訾艳阳, 等. 基于设备状态振动特征的比例故障率模型可靠性评估[J]. 机械工程学报, 2009, 45(12):89-94. DING Feng, HE Zhengjia, ZI Yangyang, et al. Reliability assessment based on equipment condition vibration feature using proportional hazards model[J]. Journal of Mechanical Engineering, 2009, 45(12):89-94.
[16] 张焱, 汤宝平, 韩延, 等. 融合失效样本与截尾样本的滚动轴承寿命预测[J]. 振动与冲击, 2017, 36(23):10-16. ZHANG Yan, TANG Baoping, HAN Yan, et al. Life prediction for rolling bearings utilizing both failure and truncated samples[J]. Journal of Vibration and Shock, 2017, 36(23):10-16.
[17] 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. ZHANG Xiaoli, WANG Baojian, MA Meng, et al. A review of life prediction for roller bearing[J]. Machinery Design & Manufacture, 2015(10):221-224.
[18] ZHANG Zhongwei, CHEN Huaihai, LI Shunming, et al. Unsupervised domain adaptation via enhanced transfer joint matching for bearing fault diagnosis[J]. Measurement, 2020, 165:108071.