338 research outputs found
Fault diagnosis method for rolling bearings based on the interval support vector domain description
Aiming at the fault classification problem of the rolling bearing under the uncertain structure parameters work condition, this paper proposes a fault diagnosis method based on the interval support vector domain description (ISVDD). Firstly, intrinsic time scale decomposition is performed for vibration signals of the rolling bearing to get the time-frequency spectrum samples. These samples are divided into a training set and a test set. Then, the training set is used to train the ISVDD. Meanwhile, the dynamic decreasing inertia weight particle swarm optimization is applied to improve the training accuracy of ISVDD model. Finally, the performance of the four interval classifiers is calculated in rolling bearing fault test set. The experimental results show the advantages of the ISVDD model: (1) ISVDD can extend the support vector domain description to solve the uncertain interval rolling bearing fault classification problem effectively; (2) The proposed ISVDD has the highest classification accuracy in four interval classification methods for the different rolling bearing fault types
Geometric margin domain description with instance-specific margins
Support vector domain description (SVDD) is a useful tool in data mining, used
for analysing the within-class distribution of multi-class data and to ascertain
membership of a class with known training distribution. An important property
of the method is its inner-product based formulation, resulting in its applicability
to reproductive kernel Hilbert spaces using the “kernel trick”. This practice relies
on full knowledge of feature values in the training set, requiring data exhibiting
incompleteness to be pre-processed via imputation, sometimes adding unnecessary
or incorrect data into the classifier. Based on an existing study of support
vector machine (SVM) classification with structurally missing data, we present a
method of domain description of incomplete data without imputation, and generalise
to some times of kernel space. We review statistical techniques of dealing
with missing data, and explore the properties and limitations of the SVM procedure.
We present two methods to achieve this aim: the first provides an input
space solution, and the second uses a given imputation of a dataset to calculate an
improved solution. We apply our methods first to synthetic and commonly-used
datasets, then to non-destructive assay (NDA) data provided by a third party. We
compare our classification machines to the use of a standard SVDD boundary, and
highlight where performance improves upon the use of imputation
Exploration of the High Entropy Alloy Space as a Constraint Satisfaction Problem
High Entropy Alloys (HEAs), Multi-principal Component Alloys (MCA), or
Compositionally Complex Alloys (CCAs) are alloys that contain multiple
principal alloying elements. While many HEAs have been shown to have unique
properties, their discovery has been largely done through costly and
time-consuming trial-and-error approaches, with only an infinitesimally small
fraction of the entire possible composition space having been explored. In this
work, the exploration of the HEA composition space is framed as a Continuous
Constraint Satisfaction Problem (CCSP) and solved using a novel Constraint
Satisfaction Algorithm (CSA) for the rapid and robust exploration of alloy
thermodynamic spaces. The algorithm is used to discover regions in the HEA
Composition-Temperature space that satisfy desired phase constitution
requirements. The algorithm is demonstrated against a new (TCHEA1) CALPHAD HEA
thermodynamic database. The database is first validated by comparing phase
stability predictions against experiments and then the CSA is deployed and
tested against design tasks consisting of identifying not only single phase
solid solution regions in ternary, quaternary and quinary composition spaces
but also the identification of regions that are likely to yield
precipitation-strengthened HEAs.Comment: 14 pages, 13 figure
Pareto-Path Multi-Task Multiple Kernel Learning
A traditional and intuitively appealing Multi-Task Multiple Kernel Learning
(MT-MKL) method is to optimize the sum (thus, the average) of objective
functions with (partially) shared kernel function, which allows information
sharing amongst tasks. We point out that the obtained solution corresponds to a
single point on the Pareto Front (PF) of a Multi-Objective Optimization (MOO)
problem, which considers the concurrent optimization of all task objectives
involved in the Multi-Task Learning (MTL) problem. Motivated by this last
observation and arguing that the former approach is heuristic, we propose a
novel Support Vector Machine (SVM) MT-MKL framework, that considers an
implicitly-defined set of conic combinations of task objectives. We show that
solving our framework produces solutions along a path on the aforementioned PF
and that it subsumes the optimization of the average of objective functions as
a special case. Using algorithms we derived, we demonstrate through a series of
experimental results that the framework is capable of achieving better
classification performance, when compared to other similar MTL approaches.Comment: Accepted by IEEE Transactions on Neural Networks and Learning System
Modeling and Recognition of Smart Grid Faults by a Combined Approach of Dissimilarity Learning and One-Class Classification
Detecting faults in electrical power grids is of paramount importance, either
from the electricity operator and consumer viewpoints. Modern electric power
grids (smart grids) are equipped with smart sensors that allow to gather
real-time information regarding the physical status of all the component
elements belonging to the whole infrastructure (e.g., cables and related
insulation, transformers, breakers and so on). In real-world smart grid
systems, usually, additional information that are related to the operational
status of the grid itself are collected such as meteorological information.
Designing a suitable recognition (discrimination) model of faults in a
real-world smart grid system is hence a challenging task. This follows from the
heterogeneity of the information that actually determine a typical fault
condition. The second point is that, for synthesizing a recognition model, in
practice only the conditions of observed faults are usually meaningful.
Therefore, a suitable recognition model should be synthesized by making use of
the observed fault conditions only. In this paper, we deal with the problem of
modeling and recognizing faults in a real-world smart grid system, which
supplies the entire city of Rome, Italy. Recognition of faults is addressed by
following a combined approach of multiple dissimilarity measures customization
and one-class classification techniques. We provide here an in-depth study
related to the available data and to the models synthesized by the proposed
one-class classifier. We offer also a comprehensive analysis of the fault
recognition results by exploiting a fuzzy set based reliability decision rule
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