Plants usually no longer fully correspond with the planning status when they are commissioned, and they are subject to further changes during operation. Modernization, however, requires up-to-date as-is documentation. Together with a partner, PROSTEP offers plant engineering companies an AI-based service that allows an automated digitization of existing plants.
The documentation of the state of a plant is not only crucial when it comes to modernization measures. It also enables the use of 3D models for applications such as virtual operation or training purposes. Identified plant components also facilitate the use of augmented reality to support maintenance activities. For these purposes, the 3D models must correspond to the as-is-status, which in practice is often only the case with new plants.
Plant engineering companies today have to go to considerable lengths to digitize existing plants. A manually remodeling on the basis of 3D scan data is necessary. The piping systems of large plants alone can consist of several thousand pipe sections. Accordingly, digitization becomes time-consuming and cost-intensive. A particular challenge in this context is the correct identification and assignment of the piping and other equipment, such as pumps. The logic of the plant structure and the meta-information are stored in the corresponding P&ID-diagrams and must be linked to the 3D models.
PROSTEP’s new 3DigitalTwin solution offers an elegant, faster and economical alternative to the manual digitization of plants. The core of the solution is an AI-based software that generates a digital twin from a point cloud in a three-stage process. It is capable to automatically recognize object structures, such as piping and equipment, and convert them into 3D models. The model of the plant is then enriched with the process knowledge from the P&ID.
PROSTEP developed the 3DigitalTwin solution together with the Digital Asset Experts from SCHULLER & Company, who support companies in a wide range of industries in managing their plant data. Based on the knowledge gained in the DigiTwin research project about the AI-based evaluation of scan data from production systems, PROSTEP and SCHULLER further developed the solution to meet plant engineering specific-requirements.
By Johannes Lützenberger