Visionaize Inc. (20240241510). INTEGRATED SYSTEM FOR PREDICTING MAINTENANCE OF INDUSTRIAL ASSETS simplified abstract
Contents
- 1 INTEGRATED SYSTEM FOR PREDICTING MAINTENANCE OF INDUSTRIAL ASSETS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 INTEGRATED SYSTEM FOR PREDICTING MAINTENANCE OF INDUSTRIAL ASSETS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Predictive Maintenance Technology
- 1.13 Original Abstract Submitted
INTEGRATED SYSTEM FOR PREDICTING MAINTENANCE OF INDUSTRIAL ASSETS
Organization Name
Inventor(s)
Kannan Rameshkumar of Fremont CA (US)
Vikas Agrawal of Milpitas CA (US)
INTEGRATED SYSTEM FOR PREDICTING MAINTENANCE OF INDUSTRIAL ASSETS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240241510 titled 'INTEGRATED SYSTEM FOR PREDICTING MAINTENANCE OF INDUSTRIAL ASSETS
Simplified Explanation
The patent application describes a system and method for predicting maintenance and optimizing operational performance in industrial operations on a metaverse platform. The system uses AI/ML engines for anomaly detection and predictive analytics to prevent future failures, facilitate planned maintenance, and provide actionable recommendations. It combines data with AR/VR-based digital twin solutions for real-time troubleshooting and maintenance training.
- AI/ML engines for anomaly detection and predictive analytics
- AR/VR-based digital twin solutions for real-time troubleshooting and maintenance training
- Detection of anomalies in industrial assets using sensor and IIoT data
- Predictive analytics for capturing failures and providing actionable recommendations
- Improved overall equipment effectiveness and enhanced device utilization
- Simulation of processes using data and prescription uptime plans
Key Features and Innovation
- Implementation of AI/ML engines for anomaly detection and predictive analytics
- Integration of AR/VR-based digital twin solutions for real-time troubleshooting and maintenance training
- Utilization of sensor and IIoT data for anomaly detection in industrial assets
- Provision of predictive analytics for capturing failures and providing actionable recommendations
- Improvement of overall equipment effectiveness and device utilization
- Simulation of processes using data and prescription uptime plans
Potential Applications
The technology can be applied in various industrial operations such as manufacturing, energy production, and transportation for predictive maintenance and optimized operational performance.
Problems Solved
The system addresses the challenges of predicting maintenance needs, preventing future failures, and providing actionable recommendations for industrial assets.
Benefits
The technology offers improved overall equipment effectiveness, enhanced device utilization, and predictable uptime, leading to superior productivity gains in industrial operations.
Commercial Applications
Predictive maintenance solutions for industrial operations can be marketed to manufacturing companies, energy producers, and transportation providers to enhance operational efficiency and reduce downtime.
Prior Art
Readers interested in prior art related to this technology can explore research on AI/ML applications in predictive maintenance, digital twin solutions in industrial operations, and the use of sensor data for anomaly detection.
Frequently Updated Research
Stay updated on the latest advancements in AI/ML algorithms for predictive maintenance, AR/VR technologies for digital twin solutions, and sensor data analytics for industrial asset monitoring.
Questions about Predictive Maintenance Technology
How does predictive maintenance technology improve operational efficiency in industrial settings?
Predictive maintenance technology helps prevent unexpected equipment failures by analyzing data to predict maintenance needs, leading to reduced downtime and improved productivity.
What are the key components of a predictive maintenance system in industrial operations?
A predictive maintenance system typically includes AI/ML engines for anomaly detection, digital twin solutions for real-time troubleshooting, and sensor data analytics for predictive maintenance planning.
Original Abstract Submitted
a system and method for predicting maintenance and providing optimized operational performance in industrial operations on a metaverse platform, is described. in one aspect, the system implements ai/ml engines for anomaly detection and predictive analytics to control future failures, facilitate planned maintenance, and provide actionable recommendations to control future failures. the system combines data with ar/vr-based digital twin solutions for real-time troubleshooting and maintenance training. the system detects anomalies in industrial assets using sensor and iiot data and provides predictive analytics for capturing failures and actionable recommendations, provides improved overall equipment effectiveness, enhanced device and system utilization, simulation of processes using data, and prescription uptime plans, achieving superior productivity gains and predictable uptime.
