Abstract: To explore pipelines is one of various bio-mimetic
robot applications. The robot may work in common buildings such as
between ceilings and ducts, in addition to complicated and massive
pipeline systems of large industrial plants. The bio-mimetic robot finds
any troubled area or malfunction and then reports its data. Importantly,
it can not only prepare for but also react to any abnormal routes in the
pipeline. The pipeline monitoring tasks require special types of mobile
robots. For an effective movement along a pipeline, the movement of
the robot will be similar to that of insects or crawling animals. During
its movement along the pipelines, a pipeline monitoring robot has an
important task of finding the shapes of the approaching path on the
pipes. In this paper we propose an effective solution to the pipeline
pattern recognition, based on the fuzzy classification rules for the
measured IR distance data.
Abstract: This paper proposes a novel system for monitoring the
health of underground pipelines. Some of these pipelines transport
dangerous contents and any damage incurred might have catastrophic
consequences. However, most of these damage are unintentional and
usually a result of surrounding construction activities. In order to
prevent these potential damages, monitoring systems are
indispensable. This paper focuses on acoustically recognizing road
cutters since they prelude most construction activities in modern
cities. Acoustic recognition can be easily achieved by installing a
distributed computing sensor network along the pipelines and using
smart sensors to “listen" for potential threat; if there is a real threat,
raise some form of alarm. For efficient pipeline monitoring, a novel
monitoring approach is proposed. Principal Component Analysis
(PCA) was studied and applied. Eigenvalues were regarded as the
special signature that could characterize a sound sample, and were
thus used for the feature vector for sound recognition. The denoising
ability of PCA could make it robust to noise interference. One class
SVM was used for classifier. On-site experiment results show that the
proposed PCA and SVM based acoustic recognition system will be
very effective with a low tendency for raising false alarms.