THESIS
2020
xiii, 36 pages : illustrations ; 30 cm
Abstract
Pipeline systems take a significant role in the transportation of important resources for
urban cities. However, these vital pipeline systems are vulnerable to damage. Corrosion and
cracks can lead to blockages and leakages in pipeline systems. It is desirable to have a proactive
and preventive monitoring system for assessing pipe wall thickness, pipeline radius and
pipeline material which can all degrade and be the forerunner to pipeline faults. Recent advances
in wireless sensor networks have made it possible to develop proactive pipeline health
monitoring systems. Due to limited communication resources, data storage and centralized
processing capability of wireless sensor networks, it is highly desirable for each sensor node
to extract low dimension features before further da...[
Read more ]
Pipeline systems take a significant role in the transportation of important resources for
urban cities. However, these vital pipeline systems are vulnerable to damage. Corrosion and
cracks can lead to blockages and leakages in pipeline systems. It is desirable to have a proactive
and preventive monitoring system for assessing pipe wall thickness, pipeline radius and
pipeline material which can all degrade and be the forerunner to pipeline faults. Recent advances
in wireless sensor networks have made it possible to develop proactive pipeline health
monitoring systems. Due to limited communication resources, data storage and centralized
processing capability of wireless sensor networks, it is highly desirable for each sensor node
to extract low dimension features before further data analysis. To address such a feature
extraction problem, we exploit the hidden sparsity of acoustic sensor readings in the space
of dispersion modes and transform the parameter estimation problem into a sparse signal
recovery problem.
We propose an off-grid SBL approach combined with inexact MM algorithm to extract
the acoustic mode information from the raw measurements of the sensors. The proposed
algorithm has superior performance than standard sparse recovery approaches due to the adjustable
off-grid parameter and the inherit data-driven learning capability (no prior knowledge
about the parameter, e.g., noise level, of the system model is required) of SBL. It is proved
that our proposed algorithm generates sequences of estimates converge to stationary points.
Simulation results reveal that our proposed method has substantial performance gain over
baseline methods with respect to the recovery of sparse mode information. Moreover, we
design a neural network to predict pipeline condition from the extracted information. Simulation
results reveal that pipeline health learning based on the recovered mode information
extracted by our proposed method can achieve high accuracy.
Post a Comment