Abstract
Due to the long train marshaling and complex line conditions, the operating modes in heavy haul rail systems frequently change when trains travel. Improper traction or braking operation made by drivers will increase the longitudinal impact force to trains and causes the train decoupling, severely affecting the safe operations of trains. It is quite desirable to replace the manual control with intelligent control in heavy haul rail systems. Traditional machine learning-based intelligent control methods suffer from insufficient data. Due to lacking effective incentives and trust, data from different rail lines or operators cannot be shared directly. In this paper, we propose an approach on blockchain-based federated learning to implement asynchronous collaborative machine learning between distributed agents that own data. This method performs distributed machine learning without a trusted central server. The blockchain smart contract is used to realize the management of the entire federated learning. Using the historical driving data collected from real heavy haul rail systems, the learning agent in the federated learning method adopts a support vector machine (SVM) based intelligent control model. To deal with the imbalanced traction and braking data, we optimize the classic SVM model via assigning different penalty factors to the majority and minority classes. The data set are mapped to a high dimension using kernel functions to make it linearly separable. We construct a mixing kernel function composed of polynomial and radial basis function (RBF) kernel functions, which uses a dynamic weight factor changing with train speeds to improve the model accuracy. The simulation results demonstrate the efficiency and accuracy of our proposed intelligent control method.
Highlights
The heavy haul railway has the advantages of large transportation capacity, high efficiency, low energy consumption, and low transportation cost, which has attracted attention from all over the world and has been worldwide acknowledged as the main development direction for railway bulk cargo transportation.The associate editor coordinating the review of this manuscript and approving it for publication was Jun Wu .Due to the long train marshaling, and complex line conditions, the operating mode in heavy haul rail systems frequently changes when trains travel
BLOCKCHAIN-BASED FEDERATED MACHINE LEARNING FRAMEWORK we propose a federated learning framework based on blockchain, which is decentralized and privacypreserving and enables each operator to train our intelligent driving model without leaking their private data
The support vector machine (SVM) classification model has been introduced to realize the intelligent control of traction/electric braking of heavy haul trains
Summary
The heavy haul railway has the advantages of large transportation capacity, high efficiency, low energy consumption, and low transportation cost, which has attracted attention from all over the world and has been worldwide acknowledged as the main development direction for railway bulk cargo transportation. We apply joint federated learning and blockchain to the heavy haul rail systems to protect the data privacy and security of operators. We propose a federated learning framework based on blockchain, which enables different operators to train intelligent driving models without sharing data. Blockchain-based federated learning can protect the data privacy of operators and train intelligent driving models more accurately than single operator training. The problem in the intelligent control of traction and electric braking force for heavy haul trains is transformed into a classification problem of machine learning. The SVM algorithm is used to establish a classification model to implement the intelligent control of heavy haul train traction and electric braking. The final results are used to construct a mixed kernel function to optimize the model
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