RUL Prediction Method for Quay Crane Hoisting Gearbox Bearing Based on LSTM-CAPF Framework

doi:10.16183/j.cnki.jsjtu.2022.440

Abstract

Abstract:

The health condition of hoisting gearbox bearings of quay cranes is of great importance for the safety of port production. A remaining useful life (RUL) predicting framework for lifting gearbox bearings of quay crane under time-varying operating conditions is proposed. First, the working load is discretized and the condition boundaries are determined. Then, the long short-term memory (LSTM) network model is adopted to predict the load and the corresponding operating conditions. Afterwards, considering the degradation rates and jump coefficients under different operating conditions, the state degradation function is established based on the Wiener process. Finally, the condition-activated particle filter (CAPF) is used to predict the degradation state and RUL of bearings. The proposed prediction framework is verified by the full-life data of the hoisting gearbox bearings in a port in Shanghai collected by the NetCMAS system. A comparison with the other three prediction methods shows that the proposed framework is able to obtain more accurate degradation states and RUL predictions under time-varying operating conditions.

Key words: quay crane bearing, remaining useful life (RUL) prediction, long and short-term memory network (LSTM), condition-activated particle filter (CAPF), time-varying operating conditions

CLC Number:

TH215

SUN Zhiwei, HU Xiong, DONG Kai, SUN Dejian, LIU Yang. RUL Prediction Method for Quay Crane Hoisting Gearbox Bearing Based on LSTM-CAPF Framework[J]. Journal of Shanghai Jiao Tong University, 2024, 58(3): 352-360.

Figures/Tables 12

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超参数	取值
LSTM层数	2
初始学习率	0.005
学习率衰减系数	0.2
隐藏层神经元个数	200
输入序列长度	20
输出序列长度	30
迭代次数	500
训练优化算法	Adam

预测步长	I_MAE	工况准确率/%
1	0.085 48	95.8
5	0.265 87	91.3
10	0.315 39	89.8
20	0.367 99	87.3
30	0.598 95	70.6

预测步长	M1	M2	M3	M4
1	0.37	0.72	0.96	0.68
5	0.42	1.33	1.77	1.29
10	1.08	2.11	2.44	2.01
20	1.50	2.44	3.56	2.21
30	2.01	3.86	7.63	3.47

方法	平均方差×10³
M1	1.972
M2	1.039
M3	3.159
M4	1.028