21,483 research outputs found
Degradation modeling applied to residual lifetime prediction using functional data analysis
Sensor-based degradation signals measure the accumulation of damage of an
engineering system using sensor technology. Degradation signals can be used to
estimate, for example, the distribution of the remaining life of partially
degraded systems and/or their components. In this paper we present a
nonparametric degradation modeling framework for making inference on the
evolution of degradation signals that are observed sparsely or over short
intervals of times. Furthermore, an empirical Bayes approach is used to update
the stochastic parameters of the degradation model in real-time using training
degradation signals for online monitoring of components operating in the field.
The primary application of this Bayesian framework is updating the residual
lifetime up to a degradation threshold of partially degraded components. We
validate our degradation modeling approach using a real-world crack growth data
set as well as a case study of simulated degradation signals.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS448 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Spatiotemporal Sparse Bayesian Learning with Applications to Compressed Sensing of Multichannel Physiological Signals
Energy consumption is an important issue in continuous wireless
telemonitoring of physiological signals. Compressed sensing (CS) is a promising
framework to address it, due to its energy-efficient data compression
procedure. However, most CS algorithms have difficulty in data recovery due to
non-sparsity characteristic of many physiological signals. Block sparse
Bayesian learning (BSBL) is an effective approach to recover such signals with
satisfactory recovery quality. However, it is time-consuming in recovering
multichannel signals, since its computational load almost linearly increases
with the number of channels.
This work proposes a spatiotemporal sparse Bayesian learning algorithm to
recover multichannel signals simultaneously. It not only exploits temporal
correlation within each channel signal, but also exploits inter-channel
correlation among different channel signals. Furthermore, its computational
load is not significantly affected by the number of channels. The proposed
algorithm was applied to brain computer interface (BCI) and EEG-based driver's
drowsiness estimation. Results showed that the algorithm had both better
recovery performance and much higher speed than BSBL. Particularly, the
proposed algorithm ensured that the BCI classification and the drowsiness
estimation had little degradation even when data were compressed by 80%, making
it very suitable for continuous wireless telemonitoring of multichannel
signals.Comment: Codes are available at:
https://sites.google.com/site/researchbyzhang/stsb
An Online Parallel and Distributed Algorithm for Recursive Estimation of Sparse Signals
In this paper, we consider a recursive estimation problem for linear
regression where the signal to be estimated admits a sparse representation and
measurement samples are only sequentially available. We propose a convergent
parallel estimation scheme that consists in solving a sequence of
-regularized least-square problems approximately. The proposed scheme
is novel in three aspects: i) all elements of the unknown vector variable are
updated in parallel at each time instance, and convergence speed is much faster
than state-of-the-art schemes which update the elements sequentially; ii) both
the update direction and stepsize of each element have simple closed-form
expressions, so the algorithm is suitable for online (real-time)
implementation; and iii) the stepsize is designed to accelerate the convergence
but it does not suffer from the common trouble of parameter tuning in
literature. Both centralized and distributed implementation schemes are
discussed. The attractive features of the proposed algorithm are also
numerically consolidated.Comment: Part of this work has been presented at The Asilomar Conference on
Signals, Systems, and Computers, Nov. 201
4D Seismic History Matching Incorporating Unsupervised Learning
The work discussed and presented in this paper focuses on the history
matching of reservoirs by integrating 4D seismic data into the inversion
process using machine learning techniques. A new integrated scheme for the
reconstruction of petrophysical properties with a modified Ensemble Smoother
with Multiple Data Assimilation (ES-MDA) in a synthetic reservoir is proposed.
The permeability field inside the reservoir is parametrised with an
unsupervised learning approach, namely K-means with Singular Value
Decomposition (K-SVD). This is combined with the Orthogonal Matching Pursuit
(OMP) technique which is very typical for sparsity promoting regularisation
schemes. Moreover, seismic attributes, in particular, acoustic impedance, are
parametrised with the Discrete Cosine Transform (DCT). This novel combination
of techniques from machine learning, sparsity regularisation, seismic imaging
and history matching aims to address the ill-posedness of the inversion of
historical production data efficiently using ES-MDA. In the numerical
experiments provided, I demonstrate that these sparse representations of the
petrophysical properties and the seismic attributes enables to obtain better
production data matches to the true production data and to quantify the
propagating waterfront better compared to more traditional methods that do not
use comparable parametrisation techniques
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