FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2166351010> ?p ?o ?g. }

• W2166351010 abstract "Accurate fault prediction or remaining useful life (RUL) estimation can obviously reduce cost of maintenance and decrease the probability of accidents so as to improve the performance of the system test and maintenance. At the same time, it is difficult to apply the model-based methods for fault prediction in most applications for complex systems. Due to continuously improving of automation, increasing of sampling frequency and development of computing technology and memory capacity, it gradually promotes data-driven technology into practical methods. Therefore, data-driven fault prognostics based on the sensor or historical test data has become the primary prediction means of complex systems, such as Artificial Neural Networks (ANN), Support Vector Regression (SVR) and other computational intelligence methods. In the other hands, most of traditional forecasting methods are always off-line that are not suitable for on-line prediction and real-time processing. Furthermore, for some on-line prediction methods such as Online Support Vector Regression (Online SVR), there is conflicts and trade-offs between prediction efficiency and accuracy. In real prognostics and health management (PHM) systems, such as the operating status monitoring and forecasting of complex systems like airplane and aircraft, it requires that the algorithms are flexible and adaptive in realization of balance between prediction efficiency and accuracy to meet different complicated requirements. To solve the problem above, an on-line adaptive data-driven fault prognosis and prediction strategy is presented in this paper. Considering the complex characteristics of trends and neighborhood of time series data, the multi-scale reconstruction strategy is applied to effectively reduce the size of on-line data and preserve rich history knowledge of samples. Therefore, the prediction efficiency could be improved and faster forecasting can be achieved with adaptive multi-scale sub-models. To evaluate the proposed prediction strategy, we have executed experiments with Tennessee Eastman (TE) process data. Experimental results with TE process fault data prove its effectiveness. The experiments and tests confirm the algorithms can be effectively applied to the on-line status monitoring and prediction with excellent performance in both efficiency and precision. New on-line fault status prediction strategy shows better prospect in real-time and on-line application for complex system. It can be applied in industrial fields for system maintenance and prognostics and health management." @default.
• W2166351010 created "2016-06-24" @default.
• W2166351010 creator A5010356589 @default.
• W2166351010 creator A5019735827 @default.
• W2166351010 creator A5090076224 @default.
• W2166351010 date "2011-05-01" @default.
• W2166351010 modified "2023-10-13" @default.
• W2166351010 title "Online adaptive status prediction strategy for data-driven fault prognostics of complex systems" @default.
• W2166351010 cites W1964357740 @default.
• W2166351010 cites W1977777281 @default.
• W2166351010 cites W1978573847 @default.
• W2166351010 cites W2004186751 @default.
• W2166351010 cites W2017537474 @default.
• W2166351010 cites W2058196021 @default.
• W2166351010 cites W2097794234 @default.
• W2166351010 cites W2098263691 @default.
• W2166351010 cites W2100365983 @default.
• W2166351010 cites W2107818255 @default.
• W2166351010 cites W2125520373 @default.
• W2166351010 cites W2131616565 @default.
• W2166351010 cites W2152097420 @default.
• W2166351010 cites W591732527 @default.
• W2166351010 doi "https://doi.org/10.1109/phm.2011.5939530" @default.
• W2166351010 hasPublicationYear "2011" @default.
• W2166351010 type Work @default.
• W2166351010 sameAs 2166351010 @default.
• W2166351010 citedByCount "17" @default.
• W2166351010 countsByYear W21663510102012 @default.
• W2166351010 countsByYear W21663510102013 @default.
• W2166351010 countsByYear W21663510102014 @default.
• W2166351010 countsByYear W21663510102015 @default.
• W2166351010 countsByYear W21663510102016 @default.
• W2166351010 countsByYear W21663510102017 @default.
• W2166351010 countsByYear W21663510102018 @default.
• W2166351010 countsByYear W21663510102020 @default.
• W2166351010 countsByYear W21663510102021 @default.
• W2166351010 crossrefType "proceedings-article" @default.
• W2166351010 hasAuthorship W2166351010A5010356589 @default.
• W2166351010 hasAuthorship W2166351010A5019735827 @default.
• W2166351010 hasAuthorship W2166351010A5090076224 @default.
• W2166351010 hasConcept C119599485 @default.
• W2166351010 hasConcept C119857082 @default.
• W2166351010 hasConcept C12267149 @default.
• W2166351010 hasConcept C124101348 @default.
• W2166351010 hasConcept C127313418 @default.
• W2166351010 hasConcept C127413603 @default.
• W2166351010 hasConcept C129364497 @default.
• W2166351010 hasConcept C151406439 @default.
• W2166351010 hasConcept C154945302 @default.
• W2166351010 hasConcept C165205528 @default.
• W2166351010 hasConcept C175551986 @default.
• W2166351010 hasConcept C200601418 @default.
• W2166351010 hasConcept C2775846686 @default.
• W2166351010 hasConcept C41008148 @default.
• W2166351010 hasConcept C45804977 @default.
• W2166351010 hasConcept C50644808 @default.
• W2166351010 hasConcept C70452415 @default.
• W2166351010 hasConceptScore W2166351010C119599485 @default.
• W2166351010 hasConceptScore W2166351010C119857082 @default.
• W2166351010 hasConceptScore W2166351010C12267149 @default.
• W2166351010 hasConceptScore W2166351010C124101348 @default.
• W2166351010 hasConceptScore W2166351010C127313418 @default.
• W2166351010 hasConceptScore W2166351010C127413603 @default.
• W2166351010 hasConceptScore W2166351010C129364497 @default.
• W2166351010 hasConceptScore W2166351010C151406439 @default.
• W2166351010 hasConceptScore W2166351010C154945302 @default.
• W2166351010 hasConceptScore W2166351010C165205528 @default.
• W2166351010 hasConceptScore W2166351010C175551986 @default.
• W2166351010 hasConceptScore W2166351010C200601418 @default.
• W2166351010 hasConceptScore W2166351010C2775846686 @default.
• W2166351010 hasConceptScore W2166351010C41008148 @default.
• W2166351010 hasConceptScore W2166351010C45804977 @default.
• W2166351010 hasConceptScore W2166351010C50644808 @default.
• W2166351010 hasConceptScore W2166351010C70452415 @default.
• W2166351010 hasLocation W21663510101 @default.
• W2166351010 hasOpenAccess W2166351010 @default.
• W2166351010 hasPrimaryLocation W21663510101 @default.
• W2166351010 hasRelatedWork W1997521412 @default.
• W2166351010 hasRelatedWork W2110784315 @default.
• W2166351010 hasRelatedWork W2512344339 @default.
• W2166351010 hasRelatedWork W2515649918 @default.
• W2166351010 hasRelatedWork W2907568933 @default.
• W2166351010 hasRelatedWork W2922346517 @default.
• W2166351010 hasRelatedWork W3192899959 @default.
• W2166351010 hasRelatedWork W3216754999 @default.
• W2166351010 hasRelatedWork W4308095600 @default.
• W2166351010 hasRelatedWork W4320483443 @default.
• W2166351010 isParatext "false" @default.
• W2166351010 isRetracted "false" @default.
• W2166351010 magId "2166351010" @default.
• W2166351010 workType "article" @default.