  The pipelines transmission system is one of the growing aspects, which has
existed for a long time in the energy industry. The cost of in-pipe exploration
for maintaining service always draws lots of attention in this industry.
Normally exploration methods (e.g. Magnetic flux leakage and eddy current) will
establish the sensors stationary for each pipe milestone or carry sensors to
travel inside the pipe. It makes the maintenance process very difficult due to
the massive amount of sensors. One of the solutions is to implement machine
learning techniques for the analysis of sensory data. Although SVMs can resolve
this issue with kernel trick, the problem is that computing the kernel depends
on the data size too. It is because the process can be exaggerated quickly if
the number of support vectors becomes really large. Particularly LiDAR spins
with an extremely rapid rate and the flow of input data might eventually lead
to massive expansion. In our proposed approach, each sample is learned in an
instant way and the supported kernel is computed simultaneously. In this
research, incremental learning approach with online support vector machines
(SVMs) is presented, which aims to deal with LiDAR sensory data only.
