Pipeline systems are critical infrastructure for modern economies, which serve as the essential means for transporting oil, gas, water, and other fluids. These pipelines are mostly buried underground, making their integrity highly crucial. Because they are buried, these pipelines are subject to stress and are prone to material degradation due to corrosion. Corrosion not only reduces the wall thickness of the pipes but also poses severe safety risks and can lead to catastrophic failures and substantial financial losses. Hence, there is an urgent need to develop accurate predictive models for evaluating pipe wall thickness. This paper aims to address this need by exploring machine learning-based algorithms to monitor the corrosion rates so that preventive measures can be taken to ensure pipeline integrity. Thus, four state-of-the-art machine-learning algorithms, namely, Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU), Bidirectional Gated Recurrent Unit (Bi-GRU), and Long Short-Term Memory (LSTM) are employed to predict accurate wall thickness of pipelines. The empirical results show that the LSTM algorithm outperforms its counterparts, achieving a low root mean squared error (RMSE) of 0.0721 mm. Therefore, incorporating LSTM-based models into pipeline integrity programs can be a significant step forward to safeguard these critical infrastructures.
KEYWORDS: Data modeling, Analytic models, Computing systems, Sensors, Data fusion, Computer simulations, Analytics, Internet of things, Industry, Ecosystems
Digital twin engineering is a disruptive technology that creates a living data model of industrial assets. The living model will continually adapt to changes in the environment or operations using real-time sensory data as well as forecast the future of the corresponding infrastructure. A digital twin can be used to proactively identify potential issues with its real physical counterpart, allowing the prediction of the remaining useful life of the physical twin by leveraging a combination of physics-based models and data-driven analytics. The digital twin ecosystem comprises sensor and measurement technologies, industrial Internet of Things, simulation and modeling, and machine learning. This paper will review the digital twin technology and highlight its application in predictive maintenance applications.
This paper presents the Airframe Digital Twin (ADT) framework and key technologies for aircraft structural life-cycle management, developed by the National Research Council (NRC) of Canada, with the aim of significantly reducing maintenance cost and extending the remaining useful life of aircraft components. The NRC ADT technologies include high-fidelity structural modelling, probabilistic usage/loads forecasting, probabilistic crack growth modelling, Bayesian updating based on non-destructive inspection (NDI) results, and advanced risk/reliability analysis. To demonstrate the NRC ADT framework, a CF-188 full-scale life-extension test was used as a physical platform to simulate the remaining lifespan of an aircraft component. A series of eddy-current NDI results, obtained during the CF-188 full-scale test, were processed using a Bayesian inference algorithm to update the ADT model. The updated ADT model was then used to predict the remaining service life of the component and to determine the next inspection interval based on the acceptable probability of failure defined by risk-based airworthiness management policies. The ADT-based methods and results were compared with the existing CF-188 lifing approach, which revealed advantages and gaps of the ADT framework for the future aircraft structural life-cycle management in the digital age. This work demonstrated the unique capability of the ADT framework to quantify the effect of NDI capability and reliability, which is crucial to update the ADT model and achieve its benefits for structural life assessment and maintenance scheduling.
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