A Comparative Study of Deep Learning-Based Diagnostics for Automotive Safety Components Using a Raspberry Pi | IEEE Conference Publication | IEEE Xplore

A Comparative Study of Deep Learning-Based Diagnostics for Automotive Safety Components Using a Raspberry Pi


Abstract:

This paper presents a feasibility study to diagnose faults in automotive safety components that are subjected to abnormal vibrations. Diagnosis targets six faults from di...Show More

Abstract:

This paper presents a feasibility study to diagnose faults in automotive safety components that are subjected to abnormal vibrations. Diagnosis targets six faults from different components that generate abnormal vibrations and faults during operation. Four deep learning approaches were developed and evaluated in terms of their suitability for embedding inside a vehicle. As a result, all four architectures were trained and executed on a Raspberry Pi to replicate the expected computational power of the embedded system.
Date of Conference: 17-20 June 2019
Date Added to IEEE Xplore: 29 August 2019
ISBN Information:
Conference Location: San Francisco, CA, USA

References

References is not available for this document.