Add to ORKGInternational audienceWe consider the problem of missing data in the context of on-line condition monitoring of industrial components by empirical, data-driven models. We propose a novel method for missing data reconstruction based on three main steps: (1) computing a fuzzy similarity measure between a segment of the time series containing the missing data and segments of reference time series; (2) assigning a weight to each reference segment; (3) reconstructing the missing values as a weighted average of the reference segments. The performance of the proposed method is verified on a real industrial application regarding shut-down transients of a Nuclear Power Plant (NPP) turbine
Missing data are a prevalent problem in many domains of pattern recognition and signal processing. M...
International audienceWe consider a real industrial case concerning 148 shutdown multidimensional tr...
International audienceThe development of empirical classification models for fault diagnosis usually...
We consider the problem of missing data in the context of on-line condition monitoring of industrial...
International audienceThe present work addresses the problem of missing data in multidimensional tim...
International audienceIn this work, an extension of a data-driven approach for estimation of the ava...
We analyze signal data collected during 148 shut-down transients of a nuclear power plant (NPP) turb...
International audienceEmpirical methods for fault diagnosis usually entail a process of supervised t...
In this work, an extension of a data-driven approach for estimation of the available Recovery Time (...
International audienceThis paper presents a data-driven approach for predicting the available Recove...
This paper presents a similarity-based approach for prognostics of the Remaining Useful Life (RUL) o...
International audienceThe application of the Auto Associative Kernel Regression (AAKR) method to the...
Missing data are a prevalent problem in many domains of pattern recognition and signal processing. M...
International audienceWe consider a real industrial case concerning 148 shutdown multidimensional tr...
International audienceThe development of empirical classification models for fault diagnosis usually...
We consider the problem of missing data in the context of on-line condition monitoring of industrial...
International audienceThe present work addresses the problem of missing data in multidimensional tim...
International audienceIn this work, an extension of a data-driven approach for estimation of the ava...
We analyze signal data collected during 148 shut-down transients of a nuclear power plant (NPP) turb...
International audienceEmpirical methods for fault diagnosis usually entail a process of supervised t...
In this work, an extension of a data-driven approach for estimation of the available Recovery Time (...
International audienceThis paper presents a data-driven approach for predicting the available Recove...
This paper presents a similarity-based approach for prognostics of the Remaining Useful Life (RUL) o...
International audienceThe application of the Auto Associative Kernel Regression (AAKR) method to the...
Missing data are a prevalent problem in many domains of pattern recognition and signal processing. M...
International audienceWe consider a real industrial case concerning 148 shutdown multidimensional tr...
International audienceThe development of empirical classification models for fault diagnosis usually...