Research on Construction of Crude Set Model of Critical Fault Information for Bus Based on CAN-BUS Data

Jing Li,Xin Zhu,Ruiqi Jia,Haixiao Liu,Zifan Yang,Ke Zhang,Xueyan Li

doi:10.1109/access.2020.2964791

Abstract

Under the high load, high frequency and high strength operating environment, the frequent occurrence of vehicle fault gradually attracts the attention of the society. The real-time monitoring and data recording function of vehicle-mounted equipment provides data support for vehicle status assessment and fault warning. In this paper, the real-time data collected by CAN-BUS system of Beijing Bus Group are preprocessed and discretized. On the basis of the traditional rough set theory, a new coding method is set up, and the dependency between conditional attributes and decision attributes is set as an adaptive function, which is reduced by genetic algorithm and cellular genetic algorithm respectively. The calculation results show that the key fault information of public transport vehicles is instrumental speed, oil pressure, percentage of torque, timing engine speed, and coolant temperature. By comparing the results of reduction, it is found that the cellular genetic algorithm has higher applicability than the genetic algorithm in terms of algorithm efficiency, stability, and convergence quality. Although the genetic algorithm attribute reduction is slightly better than the cellular genetic algorithm attribute reduction in the rule matching, the cellular genetic algorithm has a better ability to excavate information within the acceptable compatibility range. Finally, the selected key factors will be deployed on the Beijing Bus Group's big data platform and displayed in real time. The conclusion of this paper enriches the theory of bus engine fault warning and establishes an engine failure warning system, which can effectively reduce the failure rate of bus vehicles and reduce the maintenance cost expenditure. It has certain guiding significance for the bus operation work of Beijing Bus Group.

Highlights

As of the end of 2017, Beijing Bus Group operated 31,385 vehicles, of which 26,363 were public transport vehicles
2) ATTRIBUTE REDUCTION RESULTS BASED ON CELLULAR GENETIC ALGORITHM In order to compare the optimized model with the preoptimized model, this chapter need to be established on the basis of the above key bus fault information decision table, calculate the grey relational degree of condition attributes and decision attribute, put the decision table discretization by Rosetta software into the optimized model of attribute reduction based on cellular genetic algorithm for a new reduction
The grey relational degree of decision attributes and conditional attributes and the discretized bus fault decision table are brought into the attribute reduction algorithm based on cellular genetic algorithm for attribute reduction, and the results shown in Figure 7 are calculated

Summary

Introduction

As of the end of 2017, Beijing Bus Group operated 31,385 vehicles, of which 26,363 were public transport vehicles. There are 1,221 regular operating lines, with a mileage of 1.263 billion kilometers and an annual passenger volume of 3.187 billion passengers. It is the main task of Beijing’s ground buses and plays an important role in the development of urban public transportation in Beijing. In 2017, the Beijing Public Transport Group rushed to repair and rescue 48,942 vehicles caused by the failure of public transport vehicles in operation. Among the more than 30,000 public transport vehicles of the Beijing Bus Group, more than 6,000 new vehicles have begun to implement

Objectives

Results

Conclusion