IEEE International Conference on Prognostics and Health Management

Monday, June 17, 2019
10:00 a.m.
Hyatt Regency, San Francisco Airport

The IEEE Reliability Society is holding its tenth annual International Conference on Prognostics and Health Management (IEEE PHM 2019) on June 17-19 in San Francisco.

The 2019 IEEE PHM Conference is bringing together the expertise of relevant technical and management communities to facilitate cross-fertilization in this broad interdisciplinary technical area.

CALCE students Varun Khemani and Namkyoung Lee will attend the event along with Dr. Michael Azarian. Varun will present Electronic Circuit Diagnosis with No Data and Namkyoung will present  A Comparative Study of Deep Learning-based Diagnostics for Automotive Safety Components Using a Raspberry Pi.

Electronic Circuit Diagnosis with No Data by Varun Khemani.

Abstract: Operational data from the target system is widely considered a pre-requisite for implementation of PHM, as it used as training data. Often this data is not available to PHM practitioners because health monitoring capabilities may not be installed in legacy systems. This research presents an approach in which fault diagnosis can be implemented without any operational data and is generic enough to be applied to any electronic circuit provided a simulation model of the system with acceptable fidelity can be developed. The research also employs the Space-Filling Design, which can be used to generate the training data in a systematic, statistically valid framework, and is especially valuable for complex circuit with a large number of components. This design provides sufficient coverage of the parametric design space to be representative of the unavailable operational data, as well as incorporating the effects of parameter interaction on the simulated response of the system. Most PHM studies in the literature ignore the effect of the degradation of interacting components. We show, how such an assumption can lead to incorrect fault diagnosis/RUL estimation and propose methods to screen for two-way and higher order interactions. Finally, we use various deep learning approaches to diagnose circuit faults. This simulation-based fusion approach is a holistic framework for all types of analog electronic circuits

Deep Learning-based Diagnostics for Automotive Safety Components Using a Raspberry Pi by Namkyoung Lee.

Abstract: This presentation 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.

More information on this year's event can be found at the IEEE PHM 2019 website.


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