1 research outputs found
Structured Sparsity Modeling for Improved Multivariate Statistical Analysis based Fault Isolation
In order to improve the fault diagnosis capability of multivariate
statistical methods, this article introduces a fault isolation framework based
on structured sparsity modeling. The developed method relies on the
reconstruction based contribution analysis and the process structure
information can be incorporated into the reconstruction objective function in
the form of structured sparsity regularization terms. The structured sparsity
terms allow selection of fault variables over structures like blocks or
networks of process variables, hence more accurate fault isolation can be
achieved. Four structured sparsity terms corresponding to different kinds of
process information are considered, namely, partially known sparse support,
block sparsity, clustered sparsity and tree-structured sparsity. The
optimization problems involving the structured sparsity terms can be solved
using the Alternating Direction Method of Multipliers (ADMM) algorithm, which
is fast and efficient. Through a simulation example and an application study to
a coal-fired power plant, it is verified that the proposed method can better
isolate faulty variables by incorporating process structure information.Comment: 36 pages, 12 figure