scholarly journals The Limpet: A ROS-Enabled Multi-Sensing Platform for the ORCA Hub

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3487 ◽  
Author(s):  
Mohammed Sayed ◽  
Markus Nemitz ◽  
Simona Aracri ◽  
Alistair McConnell ◽  
Ross McKenzie ◽  
...  
Keyword(s):  
Signal Processing ◽  
Condition Monitoring ◽  
Wind Turbines ◽  
Communication Systems ◽  
Offshore Structures ◽  
Classification Approach ◽  
Communication Techniques ◽  
Sensing Platform ◽  
Online Classification ◽  
Monitoring Process

The oil and gas industry faces increasing pressure to remove people from dangerous offshore environments. Robots present a cost-effective and safe method for inspection, repair, and maintenance of topside and marine offshore infrastructure. In this work, we introduce a new multi-sensing platform, the Limpet, which is designed to be low-cost and highly manufacturable, and thus can be deployed in huge collectives for monitoring offshore platforms. The Limpet can be considered an instrument, where in abstract terms, an instrument is a device that transforms a physical variable of interest (measurand) into a form that is suitable for recording (measurement). The Limpet is designed to be part of the ORCA (Offshore Robotics for Certification of Assets) Hub System, which consists of the offshore assets and all the robots (Underwater Autonomous Vehicles, drones, mobile legged robots etc.) interacting with them. The Limpet comprises the sensing aspect of the ORCA Hub System. We integrated the Limpet with Robot Operating System (ROS), which allows it to interact with other robots in the ORCA Hub System. In this work, we demonstrate how the Limpet can be used to achieve real-time condition monitoring for offshore structures, by combining remote sensing with signal-processing techniques. We show an example of this approach for monitoring offshore wind turbines, by designing an experimental setup to mimic a wind turbine using a stepper motor and custom-designed acrylic fan blades. We use the distance sensor, which is a Time-of-Flight sensor, to achieve the monitoring process. We use two different approaches for the condition monitoring process: offline and online classification. We tested the offline classification approach using two different communication techniques: serial and Wi-Fi. We performed the online classification approach using two different communication techniques: LoRa and optical. We train our classifier offline and transfer its parameters to the Limpet for online classification. We simulated and classified four different faults in the operation of wind turbines. We tailored a data processing procedure for the gathered data and trained the Limpet to distinguish among each of the functioning states. The results show successful classification using the online approach, where the processing and analysis of the data is done on-board by the microcontroller. By using online classification, we reduce the information density of our transmissions, which allows us to substitute short-range high-bandwidth communication systems with low-bandwidth long-range communication systems. This work shines light on how robots can perform on-board signal processing and analysis to gain multi-functional sensing capabilities, improve their communication requirements, and monitor the structural health of equipment.

scholarly journals Maintenance Planning of Offshore Wind Turbine Using Condition Monitoring Information

2009 ◽  
Author(s):  
Jose´ G. Rangel-Rami´rez ◽  
John D. So̸rensen
Keyword(s):  
Condition Monitoring ◽  
Wind Turbines ◽  
Wind Farm ◽  
Offshore Structures ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Maintenance Planning ◽  
Offshore Wind Turbines ◽  
Inspection And Maintenance ◽  
Monitoring Information

Deterioration processes such as fatigue and corrosion are typically affecting offshore structures. To “control” this deterioration, inspection and maintenance activities are developed. Probabilistic methodologies represent an important tool to identify the suitable strategy to inspect and control the deterioration in structures such as offshore wind turbines (OWT). Besides these methods, the integration of condition monitoring information (CMI) can optimize the mitigation activities as an updating tool. In this paper, a framework for risk-based inspection and maintenance planning (RBI) is applied for OWT incorporating CMI, addressing this analysis to fatigue prone details in welded steel joints at jacket or tripod steel support structures for offshore wind turbines. The increase of turbulence in wind farms is taken into account by using a code-based turbulence model. Further, additional modes t integrate CMI in the RBI approach for optimal planning of inspection and maintenance. As part of the results, the life cycle reliabilities and inspection times are calculated, showing that earlier inspections are needed at in-wind farm sites. This is expected due to the wake turbulence increasing the wind load. With the integration of CMI by means Bayesian inference, a slightly change of first inspection times are coming up, influenced by the reduction of the uncertainty and harsher or milder external agents.

scholarly journals The Limpet: A ROS-Enabled Multi-Sensing Platform for the ORCA Hub

2018 ◽  
Author(s):  
Mohammed E. Sayed ◽  
Markus P. Nemitz ◽  
Simona Aracri ◽  
Alistair C. McConnell ◽  
Ross M. McKenzie ◽  
...  
Keyword(s):  
Condition Monitoring ◽  
Communication Systems ◽  
Oil And Gas ◽  
Low Cost ◽  
Cost Effective ◽  
Oil And Gas Industry ◽  
Offshore Structures ◽  
Offshore Wind ◽  
Sensor Data ◽  
Offshore Platforms

The oil and gas industry faces increasing pressure to remove people from dangerous offshore environments. Robots present a cost-effective and safe method for inspection, repair and maintenance of topside and marine offshore infrastructure. In this work, we introduce a new immobile multi-sensing robot, the Limpet, which is designed to be low-cost and highly manufacturable, and thus can be deployed in huge collectives for monitoring offshore platforms. The Limpet can be considered an instrument, where in abstract terms, an instrument is a device that transforms a physical variable of interest (measurand) into a form that is suitable for recording (measurement). The Limpet is designed to be part of the ORCA (Offshore Robotics for Certification of Assets) Hub System, which consists of the offshore assets and all the robots (UAVs, drones, mobile legged robots etc.) interacting with them. The Limpet comprises the sensing aspect of the ORCA Hub System. We integrated the Limpet with Robot Operating System (ROS), which allows it to interact with other robots in the ORCA Hub System. In this work, we demonstrate how the Limpet can be used to achieve real-time condition monitoring for offshore structures, by combining remote sensing with signal processing techniques. We show an example of this approach for monitoring offshore wind turbines. We demonstrate the use of four different communication systems (WiFi, serial, LoRa and optical communication) for the condition monitoring process. By processing the sensor data on-board, we reduce the information density of our transmissions, which allows us to substitute short-range high-bandwidth communication systems with low-bandwidth long-range communication systems. We train our classifier offline and transfer its parameters to the Limpet for online classification, where it makes an autonomous decision based on the condition of the monitored structure.

scholarly journals Aspects of structural health and condition monitoring of offshore wind turbines

2015 ◽  
Vol 373 (2035) ◽  
pp. 20140075 ◽  
Author(s):  
I. Antoniadou ◽  
N. Dervilis ◽  
E. Papatheou ◽  
A. E. Maguire ◽  
K. Worden
Keyword(s):  
Signal Processing ◽  
Damage Detection ◽  
Wind Turbine ◽  
Condition Monitoring ◽  
Wind Turbines ◽  
Offshore Wind ◽  
Successful Implementation ◽  
Offshore Wind Turbines ◽  
Structural Health ◽  
The Past

Wind power has expanded significantly over the past years, although reliability of wind turbine systems, especially of offshore wind turbines, has been many times unsatisfactory in the past. Wind turbine failures are equivalent to crucial financial losses. Therefore, creating and applying strategies that improve the reliability of their components is important for a successful implementation of such systems. Structural health monitoring (SHM) addresses these problems through the monitoring of parameters indicative of the state of the structure examined. Condition monitoring (CM), on the other hand, can be seen as a specialized area of the SHM community that aims at damage detection of, particularly, rotating machinery. The paper is divided into two parts: in the first part, advanced signal processing and machine learning methods are discussed for SHM and CM on wind turbine gearbox and blade damage detection examples. In the second part, an initial exploration of supervisor control and data acquisition systems data of an offshore wind farm is presented, and data-driven approaches are proposed for detecting abnormal behaviour of wind turbines. It is shown that the advanced signal processing methods discussed are effective and that it is important to adopt these SHM strategies in the wind energy sector.

scholarly journals Cost-Effective Condition Monitoring for Wind Turbines

2010 ◽  
Vol 57 (1) ◽  
pp. 263-271 ◽  
Author(s):  
Wenxian Yang ◽  
P.J. Tavner ◽  
C.J. Crabtree ◽  
M. Wilkinson
Keyword(s):  
Condition Monitoring ◽  
Wind Turbines ◽  
Cost Effective ◽  
Effective Condition
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