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Unmanned Inspection of Large Industrial Environments - Insights into Research Project RoboGasInspector
Abstract
Industrial plants are a vital and common asset of modern society in a various number of ways. Safety of large industrial complexes that handle hazardous chemical materials is of utter importance to prevent harm to employees, general population, our natural environment and valuable infrastructure. Therefore, besides the plant owner's own financial interest to guarantee faultless and safe operation, legal regulations have to be adhered as well. An important measure to ensure safety is the implementation of regular inspection tours by maintenance personnel, who examine the often widely-stretched process infrastructure on foot and locally search for signs of beginning leakage. The research project RoboGas Inspector was started to develop new means of aiding with the fulfillment of this essential task. This article gives an overview of this ongoing research project, where autonomous mobile robotics is combined with laser-based remote gas detection technology in order to create a proof-of-concept inspection system prototype, which can relieve humans of this monotonous and highly repetitive work.
Nowadays, an increasing amount of literature acknowledges that smart cities’ core aspect is not technology per se but how organizational characteristics and capabilities influence the technology enactment. However, the extant studies primarily focus on business-led smart city projects. Little attention is paid to the causal relations between organizational conditions and technology enactment in government-led projects. Therefore, there is a research gap concerning what conditions of governmental organizations, singly or jointly, drive technology enactment in smart city projects. To fill the gap, this article establishes three increasing levels of smartness and formulates four government conditions. Based on this conceptual framework, this article explores different configurations of organizational conditions that lead to varying levels of smartness. It uses the Qualitative Comparative Analysis (QCA) method to study 11 smart city projects in Hangzhou, China. The results generate three configurations of organizational conditions that lead to decisional intelligence, indicating that multiple pathways exist to achieve the highest level of smartness. These configurations often emphasize the significant role of human resource pressure in driving decisional intelligence. But no single condition is necessary because when human resource pressure is absent, strong financial capacity, good information-sharing, and facilitative leadership can work together as a substitution.
Gas detection may become a critical task in dangerous environments, which involve hazardous or contaminant gases, and the use of imaging sensors provides an important tool for leakage location. This paper presents a new design for remote sensing of gas leaks based on infrared (IR) imaging techniques.
The inspection system uses an uncooled microbolometer detector, operating over a wide spectral bandwidth, that features both low size and low power consumption. This equipment is boarded on a robotic platform, so that wide objects or areas can be scanned.
The detection principle is based on the use of active imaging techniques, where the use of external IR
illumination enhances the detection limit and allows the proposed system to operate in most cases independently
from environmental conditions, unlike passive commercial approaches. To illustrate this concept, a
fully radiometric description of the detection problem has been developed; CO2 detection has been demonstrated; and simulations of typical gas detection scenarios have been performed, showing that typical industrial leaks of CH4 are well within the detection limits.
The mobile platform, where the gas sensing system is going to be implemented, is a robot called TurtleBot. The
control of the mobile base and of the inspection device is integrated in ROS architecture. The exploration system is based on the technique of Simultaneous Localization And Mapping (SLAM) that allows to locate the gas leak in the map.
Tunable Diode Laser Absorption Spectroscopy (TDLAS) has evolved over the past decade from a laboratory specialty to an accepted, robust, and reliable technology for trace gas sensing. Some applications include improving efficiency of gas leak detection surveying, monitoring and controlling trace gases in chemical and pharmaceutical processing, and monitoring emissions in energy production plants. The recent advent of lightweight battery-powered standoff TDLAS sensors is enabling novel applications for remote gas sensing and non-contact process monitoring. This paper provides an overview of these next-generation TDLAS tools.
From the Publisher:Human factors, also known as human engineering or human factors engineering, is the application of behavioral and biological sciences to the design of machines and human-machine systems. Automation refers to the mechanization and integration of the sensing of environmental variables, data processing and decision making and mechanical action. This book deals with all the issues involved in human-automation systems from design to control and performance of both humans and machines.
Lots of industrial plants are regularly inspected for irregularities such as gas leaks. This is done by plant personnel via visual, aural, olfactory, and gas detector based search. Mobile robots equipped with several sensors are an opportunity to free personnel from this monotone repetitive task. A task- and user-centered design approach was chosen to assure an efficient and effective human-robot interaction and to enhance the acceptance of the developed human-machine system. In the first step, current practices were surveyed and analyzed. Then, system requirements were derived and technically realized, to iteratively evaluate their usability. This article illustrates this design approach by exemplarily present-ing requirements and their realization.
Detection and localization of escaped hazardous gases is of great industrial and public interest in order to prevent harm to humans, nature and assets or just to prevent financial losses. The development of novel leak-detection technologies will yield better coverage of inspected objects while helping to lower plant operation costs at the same time. Moreover, inspection personnel can be relieved from repetitive work and focus on value-adding supervisory control and optimization tasks. The proposed system consists of autonomous mobile inspection robots that are equipped with several remote gas sensing devices and local intelligence. All-terrain robots with caterpillar tracks are used that can handle slopes, unpaved routes and offer maneuverability in restricted spaces as required for inspecting plants such as petroleum refineries, tank farms or chemical sites as well as sealed landfills. The robots can detect and locate gas leaks autonomously to a great extent using infrared optical spectroscopic and thermal remote sensing techniques and data processing. This article gives an overview of the components of the robotic system prototype, i.e. the robotic platform and the remote sensing and evaluation module.The software architecture, including the robot middleware and the measurement routines, are described. Results from testing autonomous mobility and object inspection functions in a large test course are presented.
While other robots use in-situ measurements for gas leak detection and localization, we propose to apply remote sensing. It is easier and safer to conduct, permits rapid scans and is applicable to leak sources high up. An IR-optical sensor is used, exploiting spectral absorption effects of gases. Tailored leak detection and localization strategies are proposed. A simulation environment with a 3D model of the gas concentration field is used for developing and testing the detection and localization strategies. The system performance is demonstrated in a case study with a chemical plant.