Development of an inline inspection robot for the detection of pipeline defects

Abstract

Purpose The purpose of this study is to develop a robot for non-destructive testing of the pipelines to improve its reliability and reduce the loss of products due to cracks, corrosions, etc. Design/methodology/approach In this study, an inline inspection robot was developed for crack and corrosion detection in the pipeline. The developed robot consists of ultrasonic sensors to avoid obstacles, a visual aid with high resolution to view real time images and colour sensors for corrosion detection. The Autodesk inventor software was used for the drafting and solid modelling of the robot. A dummy pipe of 500 mm diameter and 2,000 mm length with induced cracks and corrosion was fabricated to test the robot. The colour sensors placed at each side of the robot were used to detect corrosion in the dummy pipe whilst the image processing was done to analyse the crack, as well as the type and depth of corrosion present in the dummy pipe. Findings The results obtained show the ability of the developed robot to detect cracks and determine the crack growth in the pipeline in addition to its ability to determine corrosion. Practical implications Hence, the study provides a diagnostic tool for detecting pipeline defects and analysing the extent of defects to determine the fatigue rate and the useful life of the pipeline. Originality/value The novelties of this study is based on the fact that it was designed to avoid obstacles and check for cracks, leakage and corrosion in pipelines autonomously. It has visual aid that makes it possible to see the interior of the pipe. This makes it easier to identify the defect and the location of the defects before a catastrophic failure. The device is also equipped with sensors, which can detect defects and send the signal to a control system, as well as a Bluetooth device so the operator can have real time information about the state and integrity of the pipelines. The system is also integrated with a Bluetooth device, which permits its compatibility with Android and other mobile applications. Thus, the enabled user can send a command to query the state of the pipeline at any location with the feedback received in the form of short message service. Hence, this study offers contribution in the development of an independent (self-governing) system with the capability to autonomously detect defects in pipe walls and effectively communicate feedback to the authorised users. The prototype model for the evaluation of pipeline integrity will bring about a more proactive way to detect pipeline defects so that effort can be geared towards its restoration before it becomes a major problem, which will subsequently affect productivity and incur losses.

Risk analysis of Ex-spool 16” mol: an insight of machine learning and experimental result

“…On one side, the RCA is an attractive method for many researchers and practitioners. A recent study by [21] equips the research by developing the ultrasonic sensor to internally overcome the obstacles of crack visualization and generate the anomaly's historical datasets.At the same time, ML retains its validity in gathering and processing the ILI data to provide comprehensive studies of the failed materials.The work of [22]reviews several methods of collecting the potential datasets from the field inspection results and their historical data.…”