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Imperfections in process models, if ignored, may affect reliability of the diagnostic system, for example through excessive false alarm rates. The problem tackled in this paper is how to handle unmodelled effects, caused by imperfect nominal models. We propose a covariance model, which builds on the idea that bias in nominal model can be described by a stochastic process. The Gaussian process model...
This paper presents a comprehensive solution for condition monitoring (CM) of proton exchange membrane (PEM) fuel cell systems. It comprises a modular dc–dc converter, a 90-channel fuel cell voltage monitor, and embedded diagnostics algorithms. Besides providing its basic functionality, the dc–dc converter is designed to perform diagnostic probing of fuel cells by injecting current excitation waveforms...
For efficient maintenance of critical industrial asset one requires an estimate of the current condition as well as an estimate of the remaining useful life. One can hence guarantee timely fault accommodation and avoid unanticipated breakdowns and equipment failures. The paper presents a concept of a system for on-line diagnostics and prognostics of asset condition whose aim is to make implementation...
Machine failure prognostic is concerned with the generation of long term predictions and the estimation of the probability density function of the remaining useful life. Nowadays, a commonly used approach for this task is to make the prediction using a dynamical state-space model of the fault evolution. However, the main limitation of this approach is that it requires the values of the model parameters...
The use of data-driven soft sensors instead of expensive or even non-existent hardware sensors has become increasingly popular in the last two decades. Among others, they allow to reliably substitute the direct measurements with their predictions based on several related measured variables. The reason for such an indirect solution lies mostly in the absence or technical unsuitability of a reliable...
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