Benefits and Challenges of System Prognostics

Sun, Bo; Zeng, Shengkui; Kang, Rui; Pecht, Michael

doi:10.1109/tr.2012.2194173

Cited by 155 publications

(119 citation statements)

References 70 publications

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“…2 illustrates the relationship between maintenance cost and reliability of machines [160]. Preventive maintenance can specifically [194]:…”

Section: Benefits Of Prognosis For Manufacturingmentioning

confidence: 99%

“…Baraldi [16] investigated the capabilities of prognostic approaches to deal with various sources of uncertainty in RUL prediction, focusing on particle filtering (PF) and bootstrap-centered techniques. Heng et al [72] and Sun et al [194] discussed the potential benefits, challenges, and opportunities associated with rotating machinery prognosis.…”

Section: Benefits Of Prognosis For Manufacturingmentioning

confidence: 99%

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Cloud-enabled prognosis for manufacturing

et al. 2015

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Advanced manufacturing depends on the timely acquisition, distribution, and utilization of information from machines and processes across spatial boundaries. These activities can improve accuracy and reliability in predicting resource needs and allocation, maintenance scheduling, and remaining service life of equipment. As an emerging infrastructure, cloud computing provides new opportunities to achieve the goals of advanced manufacturing. This paper reviews the historical development of prognosis theories and techniques and projects their future growth enabled by the emerging cloud infrastructure. Techniques for cloud computing are highlighted, as well as the influence of these techniques on the paradigm of cloud-enabled prognosis for manufacturing. Finally, this paper discusses the envisioned architecture and associated challenges of cloud-enabled prognosis for manufacturing

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“…2 illustrates the relationship between maintenance cost and reliability of machines [160]. Preventive maintenance can specifically [194]:…”

Section: Benefits Of Prognosis For Manufacturingmentioning

confidence: 99%

Section: Benefits Of Prognosis For Manufacturingmentioning

confidence: 99%

Cloud-enabled prognosis for manufacturing

et al. 2015

View full text Add to dashboard Cite

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“…Prognosis refers to the prediction of remaining useful life (RUL) of a component, or estimates the probability that a component can still function before failure occurs [1,9]. It can improve system reliability as well as effectiveness of maintenance and logistics planning [10][11][12][13]. Industrial rotating machines and components typically operate under conditions such as high load, high temperature, high moisture or dusty areas.…”

Section: Introductionmentioning

confidence: 99%

A review on prognostic techniques for non-stationary and non-linear rotating systems

Kan

Tan

Mathew

2015

Mechanical Systems and Signal Processing

318

153

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a b s t r a c tThe field of prognostics has attracted significant interest from the research community in recent times. Prognostics enables the prediction of failures in machines resulting in benefits to plant operators such as shorter downtimes, higher operation reliability, reduced operations and maintenance cost, and more effective maintenance and logistics planning. Prognostic systems have been successfully deployed for the monitoring of relatively simple rotating machines. However, machines and associated systems today are increasingly complex. As such, there is an urgent need to develop prognostic techniques for such complex systems operating in the real world. This review paper focuses on prognostic techniques that can be applied to rotating machinery operating under nonlinear and non-stationary conditions. The general concept of these techniques, the pros and cons of applying these methods, as well as their applications in the research field are discussed. Finally, the opportunities and challenges in implementing prognostic systems and developing effective techniques for monitoring machines operating under nonstationary and non-linear conditions are also discussed.

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“…When failures occur within power plants there results a loss of operation, unplanned downtime, higher maintenance costs, and a lack of supply to the electrical grid [5]. Therefore it is essential to implement predictive maintenance to reduce unplanned downtime utilizing prognostic maintenance policies in place of time based approaches [6] [7]. However, building physical test systems from which to generate and gather run-to-failure data within power generation is prohibitively expensive.…”

mentioning

confidence: 99%

Prognostic Modeling Utilizing a High-Fidelity Pressurized Water Reactor Simulator

McGhee

Catterson

Brown³

2018

IEEE Trans. Syst. Man Cybern, Syst.

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Abstract-Within power generation, ageing assets and an emphasis on more efficient operation of power systems and improved maintenance decision methods has led to a growing focus on asset prognostics. The main challenge facing the implementation of successful asset prognostics in power generation is the lack of available run-to-failure data. This paper proposes to overcome this issue by use of full scope high fidelity simulators to generate the run-to-failure data required. From this simulated failure data a similarity based prognostic approach is developed for estimating the Remaining Useful Life of a valve asset. Case study data is generated by initializing prebuilt industrial failure models within a 970MW Pressurized Water Reactor simulation. Such full scope high fidelity simulators are mainly operated for training purposes, allowing personnel to gain experience of standard operation as well as failures within a safe, simulated operating environment. This research repurposes such a high fidelity simulator to generate the type of data and affects that would be produced in the event of a fault. The fault scenario is then run multiple times to generate a library of failure events. This library of events was then split into training and test batches for building the prognostic model. Results are presented and conclusions drawn about the success of the technique and the use of high fidelity simulators in this manner.Index Terms-Model-based prognostics, remaining useful life, high fidelity simulation, power generation,

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Benefits and Challenges of System Prognostics

Cited by 155 publications

References 70 publications

Cloud-enabled prognosis for manufacturing

Cloud-enabled prognosis for manufacturing

A review on prognostic techniques for non-stationary and non-linear rotating systems

Prognostic Modeling Utilizing a High-Fidelity Pressurized Water Reactor Simulator

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