A hybrid prognostics and health management approach for condition-based maintenance

Zhang, Huiguo; Kang, Rui; Pecht, Michael

doi:10.1109/ieem.2009.5372976

Cited by 38 publications

(20 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The hybrid approaches are proposed in consideration of the pros and cons of the previous two groups (Lee 2004), in which prognostics results are claimed to be more reliable. The hybrid approaches have been used for the RUL prediction and maintenance of systems, such as (Kumar et al 2008, García et al 2010, Skima et al 2015, Zhang et al 2009.…”

Section: Phm Methodologiesmentioning

confidence: 99%

Prognostic and health management for safety barriers in infrastructures: Opportunities and challenges

Zhang¹,

Liu²,

Barros³

et al. 2018

Safety and Reliability – Safe Societies in a Changing World

View full text Add to dashboard Cite

Different types of safety barriers are deployed in many infrastructures to reduce the occurrences of hazards, and protect people, environment and other assets in case the unexpected events have occurred and the capacity of these barriers against hazards can be weakened by degradations or the failures related to changes over time. It is natural to adapt the approaches of Prognostic and Health Management (PHM) to monitor the conditions and measurable parameters of safety barriers, and predict their future performance by assessing the extent of degradations. This study aims to identify the uniqueness and possible challenges when implementing PHM on safety barriers. Definitions and classifications of safety barriers will be discussed with considering their installation environment in infrastructures, in order to reveal what kind of characteristics of barriers can lead to higher demand on prognosis and heath monitoring. Another objective of this paper is to review the qualitative and quantitative measures for the capacity and performance of safety barriers, and to explore the possible methods and research gaps in the assessments for different PHM strategies, taking account their effects on safety barriers, and effects on the infrastructures being protected by the barriers.

show abstract

Section: Phm Methodologiesmentioning

confidence: 99%

Prognostic and health management for safety barriers in infrastructures: Opportunities and challenges

Zhang¹,

Liu²,

Barros³

et al. 2018

Safety and Reliability – Safe Societies in a Changing World

View full text Add to dashboard Cite

show abstract

“…In contrast, modelbased or physics-of-failure approaches calculate the cumulative damage due to various failure mechanisms based on the knowledge about the physics of the degradation of individual components [2]. Hybrid approaches combine both strategies, whereas data-driven approaches are used for calibration of the physical models while model-based techniques define failure criteria and thresholds for the data-driven methods [14].…”

Section: Prognostics and Health Managementmentioning

confidence: 99%

Clever

Maeda

Torres

2014

Proceedings of the 27th Symposium on Integrated Circuits and Systems Design

View full text Add to dashboard Cite

The continuously progressing technology scaling is not only the source for increasing performance and complexity of embedded systems, but also the reason for rising reliability concerns of the underlying hardware. Consequently, numerous techniques emerged in recent years to mitigate wide range of error causes. Single layer methods, though, proved to be limited in providing an effective and efficient way to address errors in the system perspective. This paper proposes the new technology Cross-Layer Error Verification, Evaluation and Reporting (CLEVER), which is capable of gathering errors and system information on different levels of abstraction to evaluate the remaining useful lifetime of the system and its current state. Thus, CLEVER permits to overcome errors due to time-dependable and abrupt deterioration effects in complex embedded systems with a reasonable overhead. The presented system architecture was designed using a system description language and integrated in a simulation platform. Simulation results indicate the feasibility of this technology.

show abstract

“…Data-driven approaches (Fig. 4) are underpinned by machine learning or statistical methods [20][21][22][23]. A machine learning approach uses measured or sensor data to estimate and calculate the current status of a system's health as well as predict the future health condition.…”

Section: Data Driven Prognostics In Automotive Electronicsmentioning

confidence: 99%

Prognostics of automotive electronics with data driven approach: A review

Ahsan

Stoyanov

Bailey

2016

2016 39th International Spring Seminar on Electronics Technology (ISSE)

View full text Add to dashboard Cite

Prognostics and Health Management (PHM) is a comprehensive framework that can deal with solutions for predicting and maintaining electronic system's health. The emerging concept of PHM is increasingly considered for adoption in many engineering fields such as automotive, mechanical, electrical, industrial, aerospace and railway. PHM of electronic components and systems can offer competitive advantages by improving performance, reliability, safety, maintainability and availability. In this paper, a brief description of PHM concept, current PHM approaches, key prognostics components and corresponding monitored/sensed parameters in automotive PHM applications are presented. Software tools developed for PHM applications are also reviewed. Particular focus is given on data driven approaches for prognostics of performance and reliability of automotive electronic systems. Based on the undertaken review of state-of-art in this area, key requirements and attributes of prognostic frameworks for automotive electronics are formulated and future prognostics challenges for the sector are discussed.

show abstract

A hybrid prognostics and health management approach for condition-based maintenance

Cited by 38 publications

References 5 publications

Prognostic and health management for safety barriers in infrastructures: Opportunities and challenges

Prognostic and health management for safety barriers in infrastructures: Opportunities and challenges

Clever

Prognostics of automotive electronics with data driven approach: A review

Contact Info

Product

Resources

About