Fault Detection and Fault Tolerant Control for Anti-lock Braking Systems )ABS) Speed Sensors by Using Neural Networks

Abdulkareem, Ayad Q.; Humod, Abdulrahim Th.; Ahmed, Oday A.

doi:10.30684/etj.2022.135106.1259

	Fault Detection and Fault Tolerant Control for Anti-lock Braking Systems )ABS) Speed Sensors by Using Neural Networks
Engineering and Technology Journal
Article 6, Volume 41, Issue 2, 2023, Pages 333-344 PDF (1.16 M)
Document Type: Research Paper
DOI: 10.30684/etj.2022.135106.1259
Authors
Ayad Q. Abdulkareem^* ¹; Abdulrahim Th. Humod²; Oday A. Ahmed^* ³
¹Department of Electrical Engineering/ University of Technology-Iraq., Al-Sinaha Street, Baghdad, Iraq
²Department of Electrical Engineering, University of Technology, Baghdad - Iraq
³Oday A. Ahmed received his MSc degree in Electrical and Electronic Engineering from University of Technology, Baghdad-Iraq, in 2002. He was awarded a PhD degree from University of Leicester in 2012. Since 2002, he has been a Lecturer
Abstract
This paper proposed neural networks to continuously provide alternative constructed signals for vehicle and wheel speed sensors utilized for the Anti-Lock Braking System (ABS), which serves as the fault tolerant control method. These alternative constructed signals are used for two purposes. The first is to generate residual signals, and the second is to be adopted instead of isolated faulty signals. The residual signal is generated by extracting the difference between the alternative constructed signals and the corresponding actual signals. These residual signals serve as an indication of fault occurrence and to express that fault severity. Whenever a fault occurrence is detected and diagnosed in one of the sensor’s signals, the faulty signal is isolated and replaced by the corresponding constructed signal to maintain the system's normal behavior under a faulty condition. The range of data covered under the proposed estimating neural networks is huge, continuous in time, and not sampled. In this work, the range of the data lies between [50 to 120 km/h] when the braking is started. That cannot be performed by any available method. These models' training process is based on the Levenberg-Marquardt (LM) algorithm, implemented and tested by MATLAB/Simulink. The results show that these models can accurately map the measured data into the desired output through the best-fit functions. The fast response of the trained models makes them suitable for real-time alternative signals for fault-tolerant purposes for speed sensors during hard or panic braking.
Highlights
An active Fault Detection and Fault Tolerant Control FD-FTC method were implemented for the speed sensors fault utilized in the anti-lock braking system. Proposed Method FD-FTC is a Data-Based method implemented with a neural network model. The data required for training neural network models are obtained from the Quarter Car Model implemented with the MATLAB environment. During the applied test, the responses were accurate, and the implemented method served its design purpose. The proposed method will increase the reliability and safety of the anti-lock braking system used in modern vehicle braking systems.
Keywords
Anti-lock Braking System; Data Construction; Fault Tolerant Control; Neural Network, Residual Generation; Sensor Fault

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