Proceedings Article10.1109/AERO.2008.4526623
A Framework for Prognostics and Health Management of Electronic Systems
Yogesh G. Bagul,Ibrahim Zeid,Sagar Kamarthi +2 more
- 01 Mar 2008
- pp 1-9
31
TL;DR: A general PHM framework for electronic systems is identified, which draws on the authors' work on monitoring, diagnostics, and holistic product lifecycle via embedded sensors and offers PHM guidelines to the research community in this area.
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Abstract: Prognostics and health management (PHM) of engineered systems have long been recognized as an important and integral part of reliable and safe performances of these systems, especially when their failures cause catastrophes. Blind system routine maintenance is no longer acceptable; it is both costly and inconvenient for system owners and/or users. Engineered systems, like humans, require health monitoring and management. We monitor the current health of these systems via diagnostics methods, and predict their future performance and remaining useful life via prognostics methods. Considerable research has been done on PHM with respect to mechanical systems, but the same is not true with regard to electronic systems. The contribution of this paper is two fold. First, it provides a review of PHM research with a focus on electronic systems. Second, it provides the context to identify a general PHM framework for electronic systems. The framework offers PHM guidelines to the research community in this area. The body of knowledge on PHM of electronic system fits well into the framework. The proposed framework draws on the authors' work on monitoring, diagnostics, and holistic product lifecycle via embedded sensors. The paper presents the extensions to the present research work.
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Citations
An Overview of Artificial Intelligence Applications for Power Electronics
TL;DR: The three distinctive life-cycle phases, design, control, and maintenance are correlated with one or more tasks to be addressed by AI, including optimization, classification, regression, and data structure exploration.
Prognostics Health Management of Electronic Systems Under Mechanical Shock and Vibration Using Kalman Filter Models and Metrics
TL;DR: The presented approach enables the estimation of residual life based on level of risk averseness in high reliability applications where the knowledge of impending failure is critical and the risks in terms of loss of functionality are too high to bear.
69
A Framework for Prognostics and Health Management Applications toward Smart Manufacturing Systems
Insun Shin,Junmin Lee,Jun Young Lee,Kyusung Jung,Daeil Kwon,Byeng D. Youn,Hyun Soo Jang,Joo-Ho Choi +7 more
TL;DR: A framework was developed for giving a guideline for PHM application based on common core modules across manufacturing systems and their kinds with respect to the amount of available data and domain knowledge.
63
Prognostics using Kalman-Filter models and metrics for risk assessment in BGAs under shock and vibration loads
Pradeep Lall,Ryan Lowe,Kai Goebel +2 more
- 01 Jun 2010
TL;DR: In this article, structural damage to BGA interconnects incurred during vibration testing has been monitored in the pre-failure space using resistance spectroscopy based state space vectors, rate of change of the state variable, and acceleration of state variable.
58
Extended Kalman Filter models and resistance spectroscopy for prognostication and health monitoring of leadfree electronics under vibration
Pradeep Lall,Ryan Lowe,Kai Goebel +2 more
- 20 Jun 2011
TL;DR: In this article, a technique has been developed for monitoring the structural damage accrued in BGA interconnects during operation in vibration environments, which can provide knowledge of impending failure in high reliability applications where the risks associated with loss-of-functionality are too high to bear.
47
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