Work

AI-Based Prediction and Safety Measures for Electromechanical Brake Three-Phase Motor Faults

Year: 2024

Type: article

Abstract: In the development of future automotive systems, safety and performance are crucial considerations. The reliable operation of Drum-type Electromechanical Brakes (D-EMBs), key components responsible for vehicle braking, is essential. Previous research has predominantly focused on post-fault response strategies, emphasizing fault detection and diagnosis. However, this study aimed to enhance vehicle safety by predicting motor faults in the D-EMB system and developing corresponding measures. Utilizing AI-based FFT (Fast Fourier Transform) analysis, in this research, we successfully developed a technology for the early detection of motor faults, achieving an accuracy of over 80%. This study contributes to improving the safety of future automobiles and the development of innovative safety technologies.

Source: World Electric Vehicle Journal

DOI: 10.3390/wevj15120550

Author: Hyun Jee

Institution: Mando Corporation (South Korea)

Language: English

Topic: Machine Fault Diagnosis Techniques

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,591