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Smart Robotic System Tracks Buried Pipelines, Inspects for External Damage

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The complete paper describes the development of a smart robotic inspection system for noncontact condition monitoring and fault detection in buried pipelines. Steered by a pipe locator, the smart robot, called an autonomous ground vehicle (AGV), can autonomously track the buried pipeline and simultaneously inspect it externally with a metal magnetic memory (MMM) sensor. The smart robotic system is designed to overcome the shortcomings of both manual external inspection and noninvasive magnetometric diagnosis (NIMD), making pipeline inspection safer, more efficient, and less expensive.

Introduction

Condition monitoring and defect inspection of buried pipelines has been a constant challenge for all oil and gas operations. Maintaining safety and prolonging the service life of ferrous metal pipelines that are exposed to harsh operating environments and damage from corrosion, erosion, and cracking requires regular inspection to diagnose existing or potential defects.

Pipelines can be inspected in two ways: internally and externally. Internal, or inline, inspection primarily uses an intelligent pipeline inspection gauge equipped with sensors to measure the size, location, and orientation of defects inside the pipeline. In external inspection, which is the subject of the paper, workers drive a vehicle along the pipeline to visually inspect for detection of leakage or any other kind of visible damage. Such manual external inspection is highly inefficient, expensive, and hazardous. It is also difficult to obtain any important information about anomalies brewing in the buried pipes or cathodic protection layers using this method.

Much work has been undertaken to develop nondestructive testing (NDT) technologies to inspect pipelines. However, most of these NDT sensors work only in close vicinity to the pipeline surface, so this method requires excavating the pipeline and exposing the structure. This shortcoming has instigated research toward other NDT techniques such as NIMD, which allows noncontact detection of anomalies from a distance in the core metal of pipelines buried deeply underground. NIMD sensors work on the principle of measuring distortions of residual magnetic fields caused by the variation in the pipeline’s metal magnetic permeability in a stress concentration zone (SCZ). The SCZ, and the potential changes in metal magnetic permeability, result from the combined influence of residual stress, vibration, bending and loading of pipelines, installation stress, temperature fluctuations, and other factors. These handheld magnetic sensors are used by field operators, making inspection of long pipelines in extreme environmental conditions unfeasible.

Efforts to develop more-intelligent and -efficient methods of external inspection led to the design of various types of in-pipe inspection robots. Overall, all types of in-pipe robots are designed for solving specific problems relating to the pipeline’s interior environment, which is complex, invisible, and unpredictable. The technology presented in this paper resulted from the idea of using a robot that can simultaneously track and externally inspect the pipeline.

Robot Design

To enable the AGV to find buried pipeline and inspect it from a distance, the robot is equipped with two kinds of sensors: a pipe locator to navigate to the pipeline, and a noncontact MMM sensor to inspect the defects in the pipeline. The two sensors are housed on an unmanned ground vehicle. Accurate autonomous tracking of the pipeline is achieved by fusion of three navigation mechanisms based on visual data, the Global Positioning System, and the pipe locator. The pipe locator and MMM sensor are presented in detail in the paper.

Sensor-Based Navigation

The pipe locator consists of a transmitter and receiver. It works in combination with a cathodic protection post sensor, which is used extensively in the industry for tracking buried pipelines, to enable fully autonomous tracking of the buried pipelines by the AGV. The paper describes the pipe locator, pipe-locator calibration, and controller design.

Metal Magnetic Memory

The MMM method has been applied to ferromagnetic fault diagnosis in the power, oil and gas, and manufacturing industries. It is based on measurement of the self-magnetization leakage field (SMLF) that arises on the SCZs of the ferromagnetic component as a result of being subjected to working loads in the Earth’s magnetic field. The SCZ is the area where the ferromagnetic component easily propagates defects and the defects can distort the magnetic-field distribution. The authors report that 80% of engineering accidents result from the fatigue failure caused by local stress concentrations. The existence of SMLF is the result of high-stress-induced magnetization (Fig. 1). The SMLF has three vector quantities: two tangential components, and one vertical component. By detecting these features in the magnetic field, it is possible to detect defects in the pipeline.

Fig. 1—The ferromagnetic component propagates defects in the SCZ. The MMM method detects the SMLF arising on the SCZ.

 

One of the merits of MMM inspection is that special magnetizing devices or special preparation of the pipeline before inspection are not required, a benefit that can enhance the use of this method to monitor pipeline safety frequently and in a time-saving manner.

The operational requirement of MMM is to maintain the sensor on the top center of the pipeline and keep it stable. The MMM sensor is a specialized, highly sensitive six-channel scanning device with an analog-digital analyzer. The scanning device is manufactured as a telescopic bar with a changeable length. It is designed for noncontact inspection of the SMLF of oil and gas pipelines buried at depths of up to 3 m. This probe can be used with a measuring wheel or independently. The wheel is installed with an encoder to transfer the wheel’s displacement into an electrical-impulse signal. When working with the measuring wheel (wheel mode), the probe records the data at the same time as the encoder transmits an impulse signal to it. The wheel works not only as an odometer, but also as a generator. The probes also can work in time mode with the analyzer to obtain an impulse signal at a specified time interval. Additionally, the probe contains two transducers, upper and lower. The lower transducer is on the downside to inspect the SMLF of the pipeline while the upper one inspects the environmental magnetic field for reference.

Experiments and Results

The complete paper describes experiments to evaluate the AGV’s autonomous tracking, validate the MMM manual inspection, and examine the practicality of using the AGV for inspection. Results were as follows:

  • The trajectory controller is sufficient and applicable for quick navigation and smooth tracking of the pipeline.
  • MMM inspection can provide a reliable way inspect defects on the pipeline.
  • With the trajectory controller, an AGV can autonomously track buried pipeline.

Conclusions

  • A smart robotic system has been built for noncontact condition monitoring and fault detection in buried pipelines.
  • The robot, steered by a pipeline locator, can autonomously track a buried pipeline and simultaneously inspect it with an MMM sensor.
  • MMM results were verified in a real-case study.
  • The robot was also tested in a practical application.
This article, written by JPT Technology Editor Judy Feder, contains highlights of paper SPE 192773, “A Smart Robotic System for Noncontact Condition Monitoring and Fault Detection in Buried Pipelines,” by Xiaoxiong Zhang and Amit Shukla, Khalifa University; Abdulla Al Ali, ADNOC; and Hamad Karki, Khalifa University, prepared for the 2018 Abu Dhabi International Petroleum Exhibition and Conference, Abu Dhabi, 12–14 November. The paper has not been peer reviewed.

Smart Robotic System Tracks Buried Pipelines, Inspects for External Damage

01 December 2019

Volume: 71 | Issue: 12

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