C4D

PhDC4D® is a predictive digital twin developed by PhDsoft, aimed at enhancing the integrity and maintenance management of critical assets. This solution is tailored for asset managers, maintenance engineers, and inspection professionals in the oil and gas and maritime industries. It offers a range of features that aim to optimize asset management, automate human inspections, enable predictive maintenance, integrate drones and robots, and provide real-time monitoring and analysis capabilities.

Key Features

Build/Convert 3D Model into Digital Twin: According to the vendor, PhDC4D® Smart Digital Twin facilitates the digital transformation of the maintenance process by converting existing 3D models into digital twins. This provides a comprehensive and interactive representation of critical assets.

Automation of Human Inspections: PhDC4D® automates and integrates data ingestion from human inspections, as well as data collected by drones, robots, and sensors. The system seamlessly integrates data from various sources into the same smart digital twin, reducing the need for manual data entry.

Predictive Maintenance: PhDC4D® utilizes advanced analytics and machine learning algorithms to enable predictive maintenance. By analyzing historical data and real-time sensor data, the system can detect patterns and anomalies that may indicate potential asset failures.

Integration of Drones, Robots, and Sensors: PhDC4D® seamlessly integrates data collected by drones, robots, and sensors into the digital twin environment. The system can ingest data from various sensors, such as temperature, pressure, vibration, and corrosion sensors, to monitor asset health.

Real-Time Monitoring and Analysis: PhDC4D® offers real-time monitoring and analysis capabilities to support proactive maintenance. The system continuously collects and analyzes data from sensors, inspections, and other sources to provide up-to-date asset information.

Optimize Asset Management: According to the vendor, PhDC4D® allows companies to optimize asset management and make data-driven decisions by leveraging the digital twin. The digital twin serves as a virtual replica that can be used for analysis, simulation, and predictive maintenance purposes.

Streamline Maintenance Process: According to the vendor, PhDC4D® streamlines the maintenance process by automating human inspections and data ingestion. This reduces the risk of human error and ensures the accuracy and consistency of data.

Early Detection of Anomalies: Real-time monitoring in PhDC4D® enables early detection of anomalies and immediate response to critical issues. This capability allows companies to optimize maintenance strategies and minimize unplanned downtime.

Extend Asset Lifespan: The predictive maintenance capabilities of PhDC4D® help companies identify maintenance needs before they escalate into costly breakdowns. This proactive approach to maintenance aims to minimize downtime and extend asset lifespan.