Using artificial intelligence to find design errors in the engineering drawings

Artificial intelligence is increasingly becoming important to businesses because many companies have realized the benefits of applying machine learning (ML) and deep learning (DL) in their operations. ML and DL have become attractive technologies for organizations looking to automate repetitive tasks to reduce manual work and free up resources for innovation. Unlike rule-based automation, typically used for standardized and predictable processes, machine learning, especially deep learning, can handle more complex tasks and learn over time, leading to greater accuracy and efficiency improvements. One of such promising applications is to use AI to reduce manual engineering work. This paper discusses a particular case within McDermott where the research team developed a DL model to do a quality check of complex blueprints. We describe the development and the final product of this case—AI-based software for the engineering, procurement, and construction (EPC) industry that helps to find the design mistakes buried inside very complex engineering drawings called piping and instrumentation diagrams (P&IDs). We also present a cost-benefit analysis and potential scale-up of the developed software. Our goal is to share the successful experience of AI-based product development that can substantially reduce the engineering hours and, therefore, reduce the project\u27s overall costs. The developed solution can also be potentially applied to other EPC companies doing a similar design for complex installations with high safety standards like oil and gas or petrochemical plants because the design errors it captures are common within this industry. It also could motivate practitioners and researchers to create similar products for the various fields within engineering industry

A framework for capturing and representing the decision-making processes to classify nuclear waste

In this paper, we present a new framework for triaging nuclear waste classification inside a nuclear cell as part of the decommissioning process of nuclear facilities. The process of decommissioning includes a large amount of human involvement for decision making, physical inspections and even lifting and relocating radioactive waste items. The current process accounts for risks like close human contact with radioactive material for extended periods of time, and errors based on operator knowledge rather than automated detection systems. Effective, optimized waste management solutions are essential for the safe and secure decommissioning of nuclear power plants and in this paper, we introduce an approach for integrating knowledge-based systems (KBS) with nuclear decommissioning activities, bringing benefits of efficiency and responsiveness to activities performed on daily basis by operators at nuclear facilities. We propose a framework using the CommonKADS methodology, a well-established approach for knowledge management systems, to identify the main decisions in the process for decommissioning a nuclear cell in a nuclear facility. We capture the sources of knowledge required to support and justify decisions made, and the resulting models are reviewed to assess where decisions can be automated, or supported using AI tools, to ensure of robust, reliable, and rapid decisions. The aims of this framework are to provide the first step, and help to support innovation, towards a system able to produce tangible benefits for enhancing safety, economy and reliability of nuclear cell waste classification and decommissioning management. We illustrate the use of the framework with a case study application which demonstrates how a semi-automated decision support system could be built based on the framework

A framework for capturing and representing the process to classify nuclear waste and informing where processes can be automated

Decommissioning and dismantling of nuclear facilities are complex processes, where an accurate triage of visual and radiological characterisation is an important driver of how this process is executed. In-situ measurements before dismantling are essential for effective, optimized waste management solutions to ensure the safe and secure decommissioning of nuclear installations. Characterising nuclear structures includes a large amount of human involvement in decision making, physical inspections and even lifting and relocating radioactive waste items. The current process accounts for risks like close human contact with radioactive material for extended periods, and errors based on operator knowledge rather than automated detection systems. In this paper, we present a framework to explicitly outline the steps required to classify nuclear waste remotely, in-situ and non-destructively, and the subsequent evaluation of these steps to determine where they can be automated. This framework uses the CommonKADS methodology, a well-established approach for knowledge modelling systems, to identify the main decisions in the process of characterising a nuclear reprocessing cell in a nuclear facility. We capture the sources of knowledge required to support and justify decisions made, and the resulting models are reviewed to assess where decisions can be automated, or supported using AI tools, to ensure robust, reliable, and rapid decisions. This framework aims to provide the first step and help to support innovation, toward a system able to produce tangible benefits for enhancing the safety, economy and reliability of nuclear cell waste classification and decommissioning management. We illustrate the use of the framework with a case study application which demonstrates how a semi-automated decision support system could be built based on the framework