Advanced Integration of Discrete Line Segments in Digitized P&ID for Continuous Instrument Connectivity
- URL: http://arxiv.org/abs/2505.11976v1
- Date: Sat, 17 May 2025 12:16:54 GMT
- Title: Advanced Integration of Discrete Line Segments in Digitized P&ID for Continuous Instrument Connectivity
- Authors: Soumya Swarup Prusty, Astha Agarwal, Srinivasan Iyenger,
- Abstract summary: Piping and Instrumentation Diagrams (P&IDs) constitute the foundational blueprint of a plant.<n>The manual mapping of information from P&ID sheets holds a significant challenge.<n>The digitization of P&IDs entails merging detected line segments.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Piping and Instrumentation Diagrams (P&IDs) constitute the foundational blueprint of a plant, depicting the interconnections among process equipment, instrumentation for process control, and the flow of fluids and control signals. In their existing setup, the manual mapping of information from P&ID sheets holds a significant challenge. This is a time-consuming process, taking around 3-6 months, and is susceptible to errors. It also depends on the expertise of the domain experts and often requires multiple rounds of review. The digitization of P&IDs entails merging detected line segments, which is essential for linking various detected instruments, thereby creating a comprehensive digitized P&ID. This paper focuses on explaining how line segments which are detected using a computer vision model are merged and eventually building the connection between equipment and merged lines. Hence presenting a digitized form of information stating the interconnection between process equipment, instrumentation, flow of fluids and control signals. Eventually, which can be stored in a knowledge graph and that information along with the help of advanced algorithms can be leveraged for tasks like finding optimal routes, detecting system cycles, computing transitive closures, and more.
Related papers
- Continuous Wavelet Transform and Siamese Network-Based Anomaly Detection in Multi-variate Semiconductor Process Time Series [0.11184789007828977]
anomaly prediction in semiconductor fabrication presents several critical challenges.<n>The paper presents a novel and generic approach for anomaly detection in MTS data using machine learning.<n>Our approach demonstrates high accuracy in identifying anomalies on a real FAB process time-series dataset.
arXiv Detail & Related papers (2025-07-01T11:10:19Z) - A Multi-Level, Multi-Scale Visual Analytics Approach to Assessment of
Multifidelity HPC Systems [17.246865176910045]
Hardware system events and behaviors are crucial to improving the robustness and reliability of these systems.
In this work, we aim to build a holistic analytical system that helps make sense of such massive data.
This end-to-end log analysis system, coupled with visual analytics support, allows users to glean and promptly extract supercomputer usage and error patterns.
arXiv Detail & Related papers (2023-06-15T19:23:50Z) - The Wyner Variational Autoencoder for Unsupervised Multi-Layer Wireless
Fingerprinting [6.632671046812309]
We propose a multi-layer fingerprinting framework that jointly considers the multi-layer signatures for improved identification performance.
In contrast to previous works, by leveraging the recent multi-view machine learning paradigm, our method can cluster the device information shared among the multi-layer features without supervision.
Our empirical results show that the proposed method outperforms the state-of-the-art baselines in both supervised and unsupervised settings.
arXiv Detail & Related papers (2023-03-28T10:05:06Z) - 3DMODT: Attention-Guided Affinities for Joint Detection & Tracking in 3D
Point Clouds [95.54285993019843]
We propose a method for joint detection and tracking of multiple objects in 3D point clouds.
Our model exploits temporal information employing multiple frames to detect objects and track them in a single network.
arXiv Detail & Related papers (2022-11-01T20:59:38Z) - MD-CSDNetwork: Multi-Domain Cross Stitched Network for Deepfake
Detection [80.83725644958633]
Current deepfake generation methods leave discriminative artifacts in the frequency spectrum of fake images and videos.
We present a novel approach, termed as MD-CSDNetwork, for combining the features in the spatial and frequency domains to mine a shared discriminative representation.
arXiv Detail & Related papers (2021-09-15T14:11:53Z) - Digitize-PID: Automatic Digitization of Piping and Instrumentation
Diagrams [21.298283130966148]
Digitize-PID comprises of an end-to-end pipeline for detection of core components from P&IDs like pipes, symbols and textual information.
A novel and efficient kernel-based line detection and a two-step method for detection of complex symbols based on a fine-grained deep recognition technique is presented.
Results show that Digitize-PID outperforms the existing state-of-the-art for P&ID digitization.
arXiv Detail & Related papers (2021-09-08T17:32:49Z) - Automatic digital twin data model generation of building energy systems
from piping and instrumentation diagrams [58.720142291102135]
We present an approach to recognize symbols and connections of P&ID from buildings in a completely automated way.
We apply algorithms for symbol recognition, line recognition and derivation of connections to the data sets.
The approach can be used in further processes like control generation, (distributed) model predictive control or fault detection.
arXiv Detail & Related papers (2021-08-31T15:09:39Z) - Prototypical Cross-Attention Networks for Multiple Object Tracking and
Segmentation [95.74244714914052]
Multiple object tracking and segmentation requires detecting, tracking, and segmenting objects belonging to a set of given classes.
We propose Prototypical Cross-Attention Network (PCAN), capable of leveraging rich-temporal information online.
PCAN outperforms current video instance tracking and segmentation competition winners on Youtube-VIS and BDD100K datasets.
arXiv Detail & Related papers (2021-06-22T17:57:24Z) - Federated Learning for Intrusion Detection System: Concepts, Challenges
and Future Directions [0.20236506875465865]
Intrusion detection systems play a significant role in ensuring security and privacy of smart devices.
The present paper aims to present an extensive and exhaustive review on the use of FL in intrusion detection system.
arXiv Detail & Related papers (2021-06-16T13:13:04Z) - SOLD2: Self-supervised Occlusion-aware Line Description and Detection [95.8719432775724]
We introduce the first joint detection and description of line segments in a single deep network.
Our method does not require any annotated line labels and can therefore generalize to any dataset.
We evaluate our approach against previous line detection and description methods on several multi-view datasets.
arXiv Detail & Related papers (2021-04-07T19:27:17Z) - Graph signal processing for machine learning: A review and new
perspectives [57.285378618394624]
We review a few important contributions made by GSP concepts and tools, such as graph filters and transforms, to the development of novel machine learning algorithms.
We discuss exploiting data structure and relational priors, improving data and computational efficiency, and enhancing model interpretability.
We provide new perspectives on future development of GSP techniques that may serve as a bridge between applied mathematics and signal processing on one side, and machine learning and network science on the other.
arXiv Detail & Related papers (2020-07-31T13:21:33Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.