A Hybrid Temporal Data Mining Method for Intelligent Train Braking Systems

Abstract

As big data mining technology penetrates into various fields, cross-domain topics driven by data predictive analysis have become important entry points for solving traditional problems. Due to the complex changes of the pressure sensor and the interaction of different grouped trains during the train braking process, the mechanism modeling is difficult, the data is highly temporalized, and the data distribution is not stable. Facing the development trend of long-grouped-heavy-duty train captains, if the braking analysis of the train by temporal data mining of small groups can be used for predictive analysis, it will make innovative progress in the entire train braking field. This paper focuses on combining latest technology such as machine learning, transfer learning and lifelong learning to construct the first predictive analysis research framework in the field of train braking systems. Based on the principle of train braking process and temporal data collected from intelligent experiment platform, a baseline has firstly been built to solve fixed-grouped and multi-grouped temporal prediction problems. Then a predictive algorithm for model verification and update for lifelong learning is established to automatically update model parameters over time. Finally, relying on the parameter transfer in transfer learning, a multi-grouped temporal data prediction analysis is performed. Through comparing the training results of the "pre-trained" model on the general domain, the "tuned" model on both general domain and the target domain, and the "target only" model on the target domain separately, multi-domain tuning results show their applicable scope and transfer conditions. In summary, this work can contribute to intelligently upgrading the semi-physical intelligent test platform for long-grouped-heavy-duty trains.INDEX TERMS train braking system, temporal data mining, lifelong learning, transfer learning

Clutch Pressure Plate Temperature Prediction Based on Bi-LSTM and Migration Learning

“…Various hyper-parameters in migration learning such as train-test split ratio, learning rate, optimizer, and batch size were varied and the optimal hyper-parameters suitable for achieving high classification accuracy were found for each pre-trained network. Liu W J [11] constructed a predictive analytics research framework for train braking systems, integrating machine learning, migration learning, and lifelong learning techniques. Based on train braking process principles and timing data collected from an intelligent experimental platform, a baseline was established to solve the timing prediction problem with fixed grouping and multiple grouping.…”