Research on Online Condition monitoring for Complex System based on
Modified Broad Learning Systems
Abstract
Monitoring the condition of complex systems with implemented sensors in
an online manner is of great importance to their safety and
availability. Broad learning system (BLS), which expands the
single-hidden-layer neural network by enriching the number of
hidden-layer nodes, can greatly improve the model training efficiency.
But, the randomly generated hidden-layer nodes make BLSs performing
poorly in some high-dimensional data classification tasks. This paper
focuses on providing some ideas to tackle this problem by optimizing the
generation of initial nodes to compact the BLS hidden-layer structure.
Specifically, logistic regression (LR) and structural causal model (SCM)
are considered to obtain rough predictions of system fault state to
replace the randomly generated hidden-layer nodes with no practical
significance. Thus, the outputs of the initial node groups are more
closely related to the system health status. The proposed methods are
expected to improve the feature extraction effectiveness, to simplify
the network structure, and to reduce the computational burden. Various
simulation datasets are considered to prove the universality of the
proposed method in complex system fault diagnosis. And, with real data
from a high-speed train brake control system, the effectiveness of the
proposed online monitoring framework is further verified. It is also
shown that the proposed methods are convenient to migrate to new
operation environments.