3,200 research outputs found
Building robust prediction models for defective sensor data using Artificial Neural Networks
Predicting the health of components in complex dynamic systems such as an
automobile poses numerous challenges. The primary aim of such predictive
systems is to use the high-dimensional data acquired from different sensors and
predict the state-of-health of a particular component, e.g., brake pad. The
classical approach involves selecting a smaller set of relevant sensor signals
using feature selection and using them to train a machine learning algorithm.
However, this fails to address two prominent problems: (1) sensors are
susceptible to failure when exposed to extreme conditions over a long periods
of time; (2) sensors are electrical devices that can be affected by noise or
electrical interference. Using the failed and noisy sensor signals as inputs
largely reduce the prediction accuracy. To tackle this problem, it is
advantageous to use the information from all sensor signals, so that the
failure of one sensor can be compensated by another. In this work, we propose
an Artificial Neural Network (ANN) based framework to exploit the information
from a large number of signals. Secondly, our framework introduces a data
augmentation approach to perform accurate predictions in spite of noisy
signals. The plausibility of our framework is validated on real life industrial
application from Robert Bosch GmbH.Comment: 16 pages, 7 figures. Currently under review. This research has
obtained funding from the Electronic Components and Systems for European
Leadership (ECSEL) Joint Undertaking, the framework programme for research
and innovation Horizon 2020 (2014-2020) under grant agreement number
662189-MANTIS-2014-
Reduced order modeling of fluid flows: Machine learning, Kolmogorov barrier, closure modeling, and partitioning
In this paper, we put forth a long short-term memory (LSTM) nudging framework
for the enhancement of reduced order models (ROMs) of fluid flows utilizing
noisy measurements. We build on the fact that in a realistic application, there
are uncertainties in initial conditions, boundary conditions, model parameters,
and/or field measurements. Moreover, conventional nonlinear ROMs based on
Galerkin projection (GROMs) suffer from imperfection and solution instabilities
due to the modal truncation, especially for advection-dominated flows with slow
decay in the Kolmogorov width. In the presented LSTM-Nudge approach, we fuse
forecasts from a combination of imperfect GROM and uncertain state estimates,
with sparse Eulerian sensor measurements to provide more reliable predictions
in a dynamical data assimilation framework. We illustrate the idea with the
viscous Burgers problem, as a benchmark test bed with quadratic nonlinearity
and Laplacian dissipation. We investigate the effects of measurements noise and
state estimate uncertainty on the performance of the LSTM-Nudge behavior. We
also demonstrate that it can sufficiently handle different levels of temporal
and spatial measurement sparsity. This first step in our assessment of the
proposed model shows that the LSTM nudging could represent a viable realtime
predictive tool in emerging digital twin systems
Artificial intelligence for advanced manufacturing quality
100 p.This Thesis addresses the challenge of AI-based image quality control systems applied to manufacturing industry, aiming to improve this field through the use of advanced techniques for data acquisition and processing, in order to obtain robust, reliable and optimal systems. This Thesis presents contributions onthe use of complex data acquisition techniques, the application and design of specialised neural networks for the defect detection, and the integration and validation of these systems in production processes. It has been developed in the context of several applied research projects that provided a practical feedback of the usefulness of the proposed computational advances as well as real life data for experimental validation
One-Class Classification: Taxonomy of Study and Review of Techniques
One-class classification (OCC) algorithms aim to build classification models
when the negative class is either absent, poorly sampled or not well defined.
This unique situation constrains the learning of efficient classifiers by
defining class boundary just with the knowledge of positive class. The OCC
problem has been considered and applied under many research themes, such as
outlier/novelty detection and concept learning. In this paper we present a
unified view of the general problem of OCC by presenting a taxonomy of study
for OCC problems, which is based on the availability of training data,
algorithms used and the application domains applied. We further delve into each
of the categories of the proposed taxonomy and present a comprehensive
literature review of the OCC algorithms, techniques and methodologies with a
focus on their significance, limitations and applications. We conclude our
paper by discussing some open research problems in the field of OCC and present
our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure
Sensors Fault Diagnosis Trends and Applications
Fault diagnosis has always been a concern for industry. In general, diagnosis in complex systems requires the acquisition of information from sensors and the processing and extracting of required features for the classification or identification of faults. Therefore, fault diagnosis of sensors is clearly important as faulty information from a sensor may lead to misleading conclusions about the whole system. As engineering systems grow in size and complexity, it becomes more and more important to diagnose faulty behavior before it can lead to total failure. In the light of above issues, this book is dedicated to trends and applications in modern-sensor fault diagnosis
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