Deterioration Trend Prediction of Spalling Damage for Heavy-Haul Railway Based on PCA and TCN-Attention
- 17 November 2023
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 7425-7430
Abstract
This paper proposes a new prediction method for damages such as spalling and wear of heavy haul railways caused and deteriorated easily by the continuous rail-wheel contact during the high intensity and heavy load transportation. Based on the combined model of PCA and TCN-Attention, it is capable of accurately predicting the deterioration trend of spalling damage for heavy haul railways. Firstly, a feature set with high dimension is constructed with the features of time domain and frequency domain extracted from the original vibration signal of rail damage. Secondly, PCA is used to reduce the dimension of the high-dimensional feature set of rail damages. The first principal component is used as the health indicator of rail damages. Further, the TCN-Attention model is adopted to predict the deterioration trend of the rail spalling damage. Finally, the feasibility of the proposed method is verified by using the vibration data of the life cycle of a rail track, from normal working to damaged to failure, collected from a railway depot. The results of the proposed method are also compared with those of prediction models constructed by TCN, LSTM and GRU networks. The comparison shows that the proposed method has better performance and higher prediction accuracy. The proposed method can more accurately predict the deterioration trend of the rail spalling damage.Keywords
This publication has 8 references indexed in Scilit:
- Joint study on health state assessment and degradation trend prediction of industrial equipmentSCIENTIA SINICA Technologica, 2021
- Journal bearing seizure degradation assessment and remaining useful life prediction based on long short-term memory neural networkMeasurement, 2020
- Adapted Receptive Field Temporal Convolutional Networks with Bar-Shaped Structures Tailored to Industrial Process Operation ModelsIndustrial & Engineering Chemistry Research, 2020
- A recurrent neural network approach for remaining useful life prediction utilizing a novel trend features construction methodMeasurement, 2019
- Bearing performance degradation assessment using long short-term memory recurrent networkComputers in Industry, 2019
- Degradation Data-Driven Time-To-Failure Prognostics Approach for Rolling Element Bearings in Electrical MachinesIEEE Transactions on Industrial Electronics, 2018
- A Hybrid Prognostics Technique for Rolling Element Bearings Using Adaptive Predictive ModelsIEEE Transactions on Industrial Electronics, 2017
- A novel approach for bearing remaining useful life estimation under neither failure nor suspension histories conditionJournal of Intelligent Manufacturing, 2015