Research on Vehicle State Segmentation and Failure Prediction Based on Big Data Nanjing University of Science and Technology

Format:

Book

Conference/Event

Author/Creator:

Lu, Zheng, author.

Contributor:

Liu, Jingxing

Wang, Liangmo

Zhang, Ailei

Zhong, Hong

Zou, Xiaojun

Conference Name:

WCX SAE World Congress Experience (2022-04-05 : Detroit & Online, Michigan, United States)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2022

Summary:

Vehicle failure prediction technology is an important part of PHM (Prognostic and Health Management) technology, which is of great significance to the safety of vehicles and to improve driving safety. Based on the vehicle operating data collected by the on-board terminal (T-box) of the telematics system, the research on the state of vehicle failure is conducted. First, this paper conducts statistical analysis on vehicle historical fault data. Preprocessing procedures such as cleaning, integration, and protocol are performed to group the data set. Then, three indexes including recency (R) frequency (F), and days (D) are selected to construct a vehicle security status subdivision system, and K -Means algorithm is utilized to divide different vehicle categories from the perspective of vehicle value. Labeled information of vehicles in different security status are further established. Moreover, taking engine faults as an example, this paper uses gray correlation analysis method to extract key fault characteristic parameters. Based on self-organizing mapping network theory (SOM), the fault prediction model is built. And, by learning the fault data of the vehicles and obtaining the characteristic differences between different states, the prediction of fault and evaluation of vehicle condition is completed. Finally, test data is selected to verify the prediction accuracy of the model

Notes:

Vendor supplied data

Publisher Number:

2022-01-0223

Access Restriction:

Restricted for use by site license