Brake Fault Identification and Fault-Tolerant Directional Stability Control of Heavy Road Vehicles

Abstract

Accurate fault diagnosis in air brake is crucial to reduce frequent brake inspection and maintenance in heavy commercial road vehicles. Existing model-based fault diagnostic schemes work well under limited vehicle operating conditions, which is insufficient for developing an on-board monitoring device. In this context, a learning-based fault identification scheme using the Random Forest technique, which accommodates the vehicle's wide operating conditions, is proposed. This scheme identifies the brake's fault levels with a better classification accuracy of 92% compared to techniques such as Naïve Bayes, k-Nearest Neighbors, Support Vector Machine, and Decision Tree. Further, a fault-tolerant controller is proposed to overcome the vehicle's directional instability arising due to the brake fault. Two sliding mode controllers, namely differential brake control and steering angle control, were developed to control the yaw angle. These have been implemented in a Hardware in Loop experimental platform with the vehicle dynamic simulation software TruckMaker ® .

Actuator Fault-Tolerant Vehicle Motion Control: A Survey

“…In general, Fig. 3 reveals that most publications involve topologies that consist of two actuator types, namely steering actuators combined with either drive [29, 33, 36, 41, 42, 44, 45, 47, 50-52, 56-63, 67, 68, 75, 82, 83, 85-87, 89-91, 96-98, 100, 101, 110, 116, 118, 120, 127, 129-132, 135, 146, 148-154, 158, 159, 162, 163, 165-170, 172-177, 187, 190, 199] or brake actuators [35,46,48,54,55,71,73,76,78,79,81,95,108,111,114,115,117,121,164,171,182,189,193,[195][196][197].…”

“…Still, those considering actuator degradations form the majority in this group (27 publications) [29, 35, 37, 40-42, 44, 47, 52, 53, 56, 60, 63, 73, 98, 110, 125, 153, 154, 162, 163, 166-168, 172, 174, 176]. Publications that target yaw angle [102,117] control and pose tracking [126] are comparably rare.…”

“…Researchers use model-in-the-loop experiments in the vast majority of publications [29-44, 46-73, 75-78, 80-86, 88-90, 92-115, 118-141, 143, 145-157, 159-175, 177, 179-181, 183-190, 193-200]. Rare exceptions are publications showing hardware-in-the-loop [74,117,176,182] or even vehicle-inthe-loop experiments [45,79,87,91,116,142,144,158,178,191,192].…”

“…For model-in-the-loop experiments, commercial simulation environments are quite frequently employed. Among these, CarSim is most often used [30-32, 38-42, 44, 49-51, 56, 57, 60-64, 66-68, 74, 78, 80, 92-94, 97, 98, 100, 108, 109, 122, 132, 135-141, 143, 145-149, 155, 159, 164, 165, 170-176, 181, 184-187, 200], followed by veDYNA [150-154, 180, 182, 193-198], CarMaker [73,117,179], ADAMS [52,53], Cruise [43], Dyna4 [119], and AMESim [76]. Altogether, the vehicle models' parametrizations are selected more or less individually in each publication and also depend strongly on the actuator topology as well as on the considered degradations.…”

A data-driven approach to fault modeling and diagnosis of brake-by-wire system

“…In previous studies on fault diagnosis for hydraulic systems, researchers often focused on machine learning methods such as support vector machines (SVMs) [4], Bayesian network classifiers [5], and random forests [6]. Although these methods were also effective, they were not universal for more complex and stochastic dynamic processes.…”