Residual life of technical systems; diagnosis, prediction and life extension

Reinertsen, Rune

doi:10.1016/s0951-8320(96)00092-0

Cited by 31 publications

(16 citation statements)

References 32 publications

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“…Once a conditional reliability function is determined, the remaining useful life function is defined as the conditional expected time to failure, given the current state, which may or may not be time dependant [40][41][42][43][44]. Modelling variants differ not only in how they calculate the conditional reliability function, but also in what information is used to define the current state.…”

Section: Conditional Probability Modelsmentioning

confidence: 99%

Prognostic modelling options for remaining useful life estimation by industry

Sikorska¹,

Hodkiewicz

2011

Mechanical Systems and Signal Processing

782

513

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Section: Conditional Probability Modelsmentioning

confidence: 99%

Prognostic modelling options for remaining useful life estimation by industry

Sikorska¹,

Hodkiewicz

2011

Mechanical Systems and Signal Processing

782

513

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“…Reinertsen [6] provided a discussion on the published literature with respect to life extension practices. In a series of research studies, Ray et al attempted to connect the dynamics of material degradation with the current active control technologies to come up with a damage-mitigating control system (i.e., life extending control system) [7][8][9].…”

Section: Life Extensionmentioning

confidence: 99%

Operation and maintenance decision-making using prognostic information

Niknam

Kobza

Hines

2015

2015 Annual Reliability and Maintainability Symposium (RAMS)

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& CO CLUSIO SPracticable life extension of engineering systems would be a remarkable application of prognostics. Although considerable research has been devoted to developing prognostics algorithms, rather less attention has been paid to post-prognostic issues such as maintenance decision making. This research investigates the use of prognostic data to mobilize the potential residual life. In this respect, a multiobjective optimization model is presented for a typical power generation unit. This model proves the ability of prognostic models to balance between power generation and life extension. The results of the optimization models quantitatively indicated that maximizing the service life of bearings requires lower shaft speed and longer maintenance time. I TRODUCTIOAn effective prognostic system should play a significant role in improving the durability of engineering systems. In essence, limited investment capitals and the need for Returnon-investment (ROI) are the motivations for life extension in engineering systems. Power generation systems are an excellent example of capital-intensive engineering systems that are expected to operate safely and efficiently beyond their nominal design life. It is to be noted that applying prognosis for extending the operable life of power generating systems has been noticeably lacking in published literature.A critical aspect in the evaluation of economic viability of power generation system is the difference between the market value of energy and the energy generation costs. In effect, the operations and maintenance (O&M) costs are a major contributor to the energy generation costs. For instance, the O&M costs of offshore wind farms contribute about 25-30% to the energy generation costs [1]. It should be emphasized that the maintenance strategy is the main contributor to the O&M costs. As indicated by Lu et al. [2], if the maintenance strategy does not work appropriately, the cost of replacement related to a failed bearing in wind turbines would jump from $5000 to $250,000. In essence, O&M costs can be minimized through improved reliability of the turbine's components, condition-based maintenance (CBM) and optimal management of maintenance requirements. Diagnostics and prognostics are two important aspects of a comprehensive CBM program. In essence, decision-making and planning for O&M need to take diagnostic and prognostic systems into account. The primary objective of this research is to develop a prognostic-based lifeextending methodology in order to improve the current PHM practices for a typical wind-based power generation unit (PGU).The first and foremost element of an efficient lifeextending methodology is effective and robust diagnosis. Next, real-time estimates of remaining useful life (RUL) need to be used in order to characterize and quantify the life extension practices. Useful life can be defined as the ability for power generation to fully or partially meet demands. RUL is the time that a component or system is able to operate with the same predefined specifi...

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“…It is supposed to use following solution: methods of genetic modeling [7,8] and methods on the basis of subjective estimate of risk factors [9].…”

Section: B Innovationmentioning

confidence: 99%

Conceptual design and engineering strategies to increase energy efficiency at enterprises: Research, technologies and personnel

Lyakhomskii

Perfilieva

Петроченков

et al. 2015

2015 IV Forum Strategic Partnership of Universities and Enterprises of Hi-Tech Branches (Science. Education. Innovations)

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One of the main directions of changeover from the inertial scenario to innovative scenario of increasing of the energy efficiency by system management of energy resources is considered. Semantic aspects, concepts and conceptual models of system of management of energy resources are defined. A method for assessment of energy management using a multiplicative model the impact of technological and managerial factors with the use of pattern recognition theory is developed. Guidelines and engineering facilities to increasing of the energy efficiency in the framework of the system of management of energy resources on the basis of the results obtained are given. Keywords-energy efficiency scenarios, energy consumption process, system management of energy resources, conceptual design, engineering of increasing energy efficiency

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Residual life of technical systems; diagnosis, prediction and life extension

Cited by 31 publications

References 32 publications

Prognostic modelling options for remaining useful life estimation by industry

Prognostic modelling options for remaining useful life estimation by industry

Operation and maintenance decision-making using prognostic information

Conceptual design and engineering strategies to increase energy efficiency at enterprises: Research, technologies and personnel

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