A deep learning digitisation framework to mark up corrosion circuits in piping and instrumentation diagrams.

Corrosion circuit mark up in engineering drawings is one of the most crucial tasks performed by engineers. This process is currently done manually, which can result in errors and misinterpretations depending on the person assigned for the task. In this paper, we present a semi-automated framework which allows users to upload an undigitised Piping and Instrumentation Diagram, i.e. without any metadata, so that two key shapes, namely pipe specifications and connection points, can be localised using deep learning. Afterwards, a heuristic process is applied to obtain the text, orient it and read it with minimal error rates. Finally, a user interface allows the engineer to mark up the corrosion sections based on these findings. Experimental validation shows promising accuracy rates on finding the two shapes of interest and enhance the functionality of optical character recognition when reading the text of interest

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

Data Requirements to Enable PHM for Liquid Hydrogen Storage Systems from a Risk Assessment Perspective

Quantitative Risk Assessment (QRA) aids the development of risk-informed safety codes and standards which are employed to reduce risk in a variety of complex technologies, such as hydrogen systems. Currently, the lack of reliability data limits the use of QRAs for fueling stations equipped with bulk liquid hydrogen storage systems. In turn, this hinders the ability to develop the necessary rigorous safety codes and standards to allow worldwide deployment of these stations. Prognostics and Health Management (PHM) and the analysis of condition-monitoring data emerge as an alternative to support risk assessment methods. Through the QRA-based analysis of a liquid hydrogen storage system, the core elements for the design of a data-driven PHM framework are addressed from a risk perspective. This work focuses on identifying the data collection requirements to strengthen current risk analyses and enable data-driven approaches to improve the safety and risk assessment of a liquid hydrogen fueling infrastructure

Structural health monitoring of fatigue cracks using acoustic emission technique

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonIn the oil and gas industry, failure due to vibration-induced fatigue is considered as an important cause of loss of integrity. There have been increasing incidents of vibration-induced fatigue in pipelines both in offshore and onshore petrochemical plants. To mitigate this problem, a Structural Health Monitoring (SHM) method using Acoustic Emission (AE) was developed, using specific damage identification strategies. A number of contributions are highlighted in this research work, which includes the fundamental understanding of wave propagation in thick and thin structures. The application of Rayleigh waves and Lamb waves is important in damage identification, being demonstrated in experimental works and finite element analysis (FEA). The identification of damage by vibration-induced fatigue crack demands a dedicated instrumentation and signal processing techniques. A novel method using Bayesian estimation of the effective coefficient (EC) was introduced, which measure the uncertainties of the located events measured by AE technique, calibrated using the information derived from pencil lead break tests. The EC was formulated in such a way the AE signals from each sensor were cross-correlated and the calculated coefficients were corrected taking into account the minimum coefficients from each sensor combination. Cross-correlation was then used to improve the identification of Lamb waves generated by the crack development. The calculated time difference between the fundamental wave modes increases the accuracy of the located events using a Single Sensor Modal Analysis Localization technique (SSMAL). Using a similar approach, the ratio of symmetric (S0) and asymmetric waves (A0) amplitudes was determined and used for damage extent assessments. In summary the AE is shown to be a very effective method for SHM of vibration-induced fatigue damage.National Structure Integrity Research Centr

CLOI-NET: Class segmentation of industrial facilities' point cloud datasets

Shape segmentation from point cloud data is a core step of the digital twinning process for industrial facilities. However, it is also a very labor intensive step, which counteracts the perceived value of the resulting model. The state-of-the-art method for automating cylinder detection can detect cylinders with 62% precision and 70% recall, while other shapes must then be segmented manually and shape segmentation is not achieved. This performance is promising, but it is far from drastically eliminating the manual labor cost. We argue that the use of class segmentation deep learning algorithms has the theoretical potential to perform better in terms of per point accuracy and less manual segmentation time needed. However, such algorithms could not be used so far due to the lack of a pre-trained dataset of laser scanned industrial shapes as well as the lack of appropriate geometric features in order to learn these shapes. In this paper, we tackle both problems in three steps. First, we parse the industrial point cloud through a novel class segmentation solution (CLOI-NET) that consists of an optimized PointNET++ based deep learning network and post-processing algorithms that enforce stronger contextual relationships per point. We then allow the user to choose the optimal manual annotation of a test facility by means of active learning to further improve the results. We achieve the first step by clustering points in meaningful spatial 3D windows based on their location. Then, we apply a class segmentation deep network, and output a probability distribution of all label categories per point and improve the predicted labels by enforcing post-processing rules. We finally optimize the results by finding the optimal amount of data to be used for training experiments. We validate our method on the largest richly annotated dataset of the most important to model industrial shapes (CLOI) and yield 82% average accuracy per point, 95.6% average AUC among all classes and estimated 70% labor hour savings in class segmentation. This proves that it is the first to automatically segment industrial point cloud shapes with no prior knowledge at commercially viable performance and is the foundation for efficient industrial shape modeling in cluttered point clouds

Optical character recognition on engineering drawings to achieve automation in production quality control

Introduction: Digitization is a crucial step towards achieving automation in production quality control for mechanical products. Engineering drawings are essential carriers of information for production, but their complexity poses a challenge for computer vision. To enable automated quality control, seamless data transfer between analog drawings and CAD/CAM software is necessary.Methods: This paper focuses on autonomous text detection and recognition in engineering drawings. The methodology is divided into five stages. First, image processing techniques are used to classify and identify key elements in the drawing. The output is divided into three elements: information blocks and tables, feature control frames, and the rest of the image. For each element, an OCR pipeline is proposed. The last stage is output generation of the information in table format.Results: The proposed tool, called eDOCr, achieved a precision and recall of 90% in detection, an F1-score of 94% in recognition, and a character error rate of 8%. The tool enables seamless integration between engineering drawings and quality control.Discussion: Most OCR algorithms have limitations when applied to mechanical drawings due to their inherent complexity, including measurements, orientation, tolerances, and special symbols such as geometric dimensioning and tolerancing (GD&T). The eDOCr tool overcomes these limitations and provides a solution for automated quality control.Conclusion: The eDOCr tool provides an effective solution for automated text detection and recognition in engineering drawings. The tool's success demonstrates that automated quality control for mechanical products can be achieved through digitization. The tool is shared with the research community through Github

Surface characterization of biomass by imaging mass spectrometry

Lignocellulosic biomass (e.g., non food-based agricultural resides and forestry wastes) has recently been promoted for use as a source of bioethanol instead of food-based materials (e.g., corn and sugar cane), however to fully realize these benefits an improved understanding of lignocellulosic recalcitrance must be developed. The primary goal of this thesis is to gain fundamental knowledge about the surface of the plant cell wall, which is to be integrated into understanding biomass recalcitrance. Imaging mass spectrometry by TOF-SIMS and MALDI-IMS is applied to understand detailed spatial and lateral changes of major components in the surface of biomass under submicron scale. Using TOF-SIMS analysis, we have demonstrated a dilute acid pretreated poplar stem represented chemical differences between surface and bulk compositions. Especially, abundance of xylan was observed on the surface while sugar profile data showed most xylan (ca. 90%) removed from the bulk composition. Water only flowthrough pretreated poplar also represented difference chemistry between surface and bulk, which more cellulose revealed on the surface compared to bulk composition. In order to gain the spatial chemical distribution of biomass, 3-dimensional (3D) analysis of biomass using TOF-SIMS has been firstly introduced in the specific application of understanding recalcitrance. MALDI-IMS was also applied to visualize different molecular weight (e.g., DP) of cellulose oligomers on the surface of biomass.PhDCommittee Chair: Art J. Ragauskas, Advisor; Committee Member: Charles L. Liotta; Committee Member: David M. Collard; Committee Member: Stefan France; Committee Member: Yulin Den

A Proposal for a Three Detector Short-Baseline Neutrino Oscillation Program in the Fermilab Booster Neutrino Beam

A Short-Baseline Neutrino (SBN) physics program of three LAr-TPC detectors located along the Booster Neutrino Beam (BNB) at Fermilab is presented. This new SBN Program will deliver a rich and compelling physics opportunity, including the ability to resolve a class of experimental anomalies in neutrino physics and to perform the most sensitive search to date for sterile neutrinos at the eV mass-scale through both appearance and disappearance oscillation channels. Using data sets of 6.6e20 protons on target (P.O.T.) in the LAr1-ND and ICARUS T600 detectors plus 13.2e20 P.O.T. in the MicroBooNE detector, we estimate that a search for muon neutrino to electron neutrino appearance can be performed with ~5 sigma sensitivity for the LSND allowed (99% C.L.) parameter region. In this proposal for the SBN Program, we describe the physics analysis, the conceptual design of the LAr1-ND detector, the design and refurbishment of the T600 detector, the necessary infrastructure required to execute the program, and a possible reconfiguration of the BNB target and horn system to improve its performance for oscillation searches.Comment: 209 pages, 129 figure