Prognostics and health management of electronics

Vichare, Nikhil; Pecht, Michael

doi:10.1109/tcapt.2006.870387

Cited by 379 publications

(183 citation statements)

References 42 publications

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“…A failure precursor is an event or series of events that is indicative of an impending failure [5]. A failure can be predicted by correlating changes in monitored precursor parameters.…”

Section: B Failure Precursormentioning

confidence: 99%

Big Data Preventive Maintenance for Hard Disk Failure Detection

Su¹,

Yon²

2018

IJIET

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Abstract-In recent years, with the rapid advancement of cloud technology, data centers have always been considered one of cloud services most important aspect to evaluate, and its reliability and availability have been the focus of every IT engineer's attention. However, service interruption is the most important factor to consider for every data center, affecting the user experience, or causing loss in a business. Therefore, automated fault prevention and monitoring of data center will effectively improve the reliability of cloud services. Predictive maintenance differs from traditional maintenance process (i.e. routine maintenance and corrective maintenance), it evaluates the state by performing device condition monitoring, and according to the state, it predicts when maintenance should be performed.This research focuses on hard drive failure prediction, with big data analysis and machine learning technology, we have developed a Preventive Monitoring System (PMS). Utilizing Prognostics and Health Management (PHM) to identify the failure mechanism, and combining Self-Monitoring, Analysis and Reporting Technology (SMART) to identify early signs of abnormalities before the device fail. Finally, we use random forest algorithm to construct the predictive model. This research aims to develop a predictive monitoring system to provide device condition monitoring and fault diagnosis, thereby identifying the device malfunction and resolving it as soon as possible, keeping the system maintained at optimal condition.Index Terms-Big data, preventive maintenance, failure prediction, hard drive, random forest.

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“…A failure precursor is an event or series of events that is indicative of an impending failure [5]. A failure can be predicted by correlating changes in monitored precursor parameters.…”

Section: B Failure Precursormentioning

confidence: 99%

Big Data Preventive Maintenance for Hard Disk Failure Detection

Su¹,

Yon²

2018

IJIET

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“…A number of approaches reported in the literature [26][27][28] have been used for products reliability assessment such as prognosis and health management of power devices (PHM) [9,29]. Such methods can be considered as a practical alternative way of looking at product reliability and life cycles conditions where the remaining useful life of the product can be predicted by assessing the extent of degradation from the product's original state of health and its expected usage conditions [30][31][32].…”

Section: Mission Profile Based Reliability Design Andmentioning

confidence: 99%

Mission Profile-Based Reliability Design and Real-Time Life Consumption Estimation in Power Electronics

Musallam

Yin

Bailey

et al. 2015

IEEE Trans. Power Electron.

139

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A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription.For more information, please contact eprints@nottingham.ac.ukManuscript ID TPEL-Reg-2014-04-0475.R2 1  Abstract-Power electronics are efficient for conversion and conditioning of the electrical energy through a wide range of applications. Proper life consumption estimation methods applied for power electronics that can operate in real-time under inservice mission profile conditions will not only provide an effective assessment of the products life expectancy but also they can deliver reliability design information. This is important to aid in manufacturing and thus help in reducing costs and maximizing through-life availability. In this paper, a mission profile based approach for real-time life consumption estimation which can be used for reliability design of power electronics is presented. The paper presents the use of electro-thermal models coupled with physics-of-failure analysis by means of real-time counting algorithm to provide accurate life consumption estimations for power modules operating under in-service conditions. These models, when driven by the actual mission profiles, can be utilized to provide advanced warning of failures and thus deliver information that can be useful to meet particular application requirements for reliability at the design stage. To implement this approach, an example of two case studies using mission profiles of a metro-system and wind-turbines applications are presented.

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“…The added capability to make appropriate decisions about maintenance actions based on diagnostics/prognostics information, available resources, and operational demand is referred to as health management. One approach to prognostics is through monitoring and reasoning of parameters that are precursors to impending failures [8,9]. In a precursor monitoring approach, a failure can be predicted by correlating the change in the monitored precursor parameter with the impending failure.…”

Section: Diagnostics Prognostics and Health Managementmentioning

confidence: 99%

A hierarchical control architecture for a PEBB-based ILC Marx modulator

Macken

Burkhart

Larsen

et al. 2009

2009 IEEE Pulsed Power Conference

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The idea of building power conversion systems around Power Electronic Building Blocks (PEBBs) was initiated by the U.S. Office of Naval Research in the mid 1990s. A PEBB-based design approach is advantageous in terms of power density, modularity, reliability, and serviceability. It is obvious that this approach has much appeal for pulsed power conversion including the International Linear Collider (ILC) klystron modulator application. A hierarchical control architecture has the inherent capability to support the integration of PEBBs. This has already been successfully demonstrated in a number of industrial applications in the recent past. This paper outlines the underlying concepts of a hierarchical control architecture for a PEBB-based Marx-topology ILC klystron modulator. The control in PEBB-based power conversion systems can be functionally partitioned into (three) hierarchical layers; system layer, application layer, and PEBB layer. This has been adopted here. Based on such a hierarchical partition, the interfaces are clearly identified and defined and, consequently, are easily characterised. A conceptual design of the hardware manager, executing low-level hardware oriented tasks, is detailed. In addition, the idea of prognostics is briefly discussed.

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Prognostics and health management of electronics

Cited by 379 publications

References 42 publications

Big Data Preventive Maintenance for Hard Disk Failure Detection

Big Data Preventive Maintenance for Hard Disk Failure Detection

Mission Profile-Based Reliability Design and Real-Time Life Consumption Estimation in Power Electronics

A hierarchical control architecture for a PEBB-based ILC Marx modulator

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