A Method for Fault Prediction of Air Brake System in Vehicles

Abstract

Fault diagnosis is a complex problem that concerns effective decision-making. Air brake system is a crucial safety unit and failure of which leads to the loss of vehicle yaw stability and increase in stopping distance of the vehicle. Fault detection and isolation in brake system is critical for continuity of the performance and the safe running of autonomous vehicles. Carrying out timely system diagnosis whenever a fault occur is important to prevent component degradation and vehicle breakdown. This work use the Rough Set Theory to develop fault diagnostic scheme for classifying the "fault" and "Nofault" conditions of air brake system with the knowledge of wheel speed sensor data. The rough set reduction principle is applied to find all reducts, and then a set of generalized classification rules for predicting the faults of Air brake system was extracted. The results show that the presented method can effectively enrich the vehicle condition monitoring system, able to give a feedback to the driver regarding the working condition of air brake system, detect and identify the location of faults, minimize the resources, such as time, cost of maintenance, etc.

Analyzing the Relationship between the Personality Traits and Drug Consumption (Month-based user Definition) using Rough Sets Theory

“…Rough sets theories (RST) is the core of most recent approximations based mathematical model to investigate the imprecision and uncertainty present in knowledge [13][14][15][16][17], as well as extract decision rules which act as classification scheme for prediction. We can say that it is a tool for data mining or knowledge discovery in relational databases.…”

Grey Relational Analysis-Based Fault Prediction for Watercraft Equipment

“…Therefore, fault prediction is the key to realizing the transformation of support mode from planned maintenance to predictive maintenance, which can perform the warning before the failure of equipment occurs. Also, more and more attention has been focused on fault prediction for equipment [11][12][13][14][15][16][17][18][19][20][21][22], such as fault prediction for the vehicle [11,12], the watercraft [13][14][15], the aircraft engine [16][17][18], the power supply system [19,20], and the track circuit [21][22][23]. Due to the complexity of watercraft structure and the diversity of the marine environment, it is challenging and difficult to study the fault prediction for watercraft equipment.…”