Tyco Fire & Security GmbH (20240345914). BUILDING SYSTEM WITH GENERATIVE AI-BASED FAULT DETECTION AND DIAGNOSTICS USING MULTI-MODAL DATA simplified abstract
Contents
BUILDING SYSTEM WITH GENERATIVE AI-BASED FAULT DETECTION AND DIAGNOSTICS USING MULTI-MODAL DATA
Organization Name
Inventor(s)
Krishnamurthy Selvaraj of Buchen (DE)
Rajiv Ramanasankaran of San Jose CA (US)
Wenwen Zhao of Santa Clara CA (US)
BUILDING SYSTEM WITH GENERATIVE AI-BASED FAULT DETECTION AND DIAGNOSTICS USING MULTI-MODAL DATA - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240345914 titled 'BUILDING SYSTEM WITH GENERATIVE AI-BASED FAULT DETECTION AND DIAGNOSTICS USING MULTI-MODAL DATA
The abstract describes a method for servicing building equipment using generative artificial intelligence models. This method involves receiving multi-modal data input, associating related data portions from each mode to form original analysis packages, training data generators to create artificial analysis packages, adjusting an output model using the artificial and original analysis packages, and generating a service-relevant multi-modal data output for servicing the building equipment.
- Receiving multi-modal data input characterizing operation of building equipment
- Associating related data portions from each mode to form original analysis packages
- Training data generators to create artificial analysis packages
- Adjusting an output model using artificial and original analysis packages
- Generating a service-relevant multi-modal data output for servicing building equipment
Potential Applications: - Predictive maintenance for building equipment - Optimization of building equipment performance - Automated troubleshooting of building equipment issues
Problems Solved: - Efficient servicing of building equipment - Improved maintenance scheduling - Enhanced equipment performance and longevity
Benefits: - Cost savings through predictive maintenance - Reduced downtime of building equipment - Enhanced operational efficiency
Commercial Applications: Title: AI-Powered Building Equipment Servicing Technology This technology can be utilized in various industries such as facility management, real estate, and construction to streamline maintenance processes, improve equipment performance, and reduce operational costs.
Questions about AI-Powered Building Equipment Servicing Technology: 1. How does this technology impact the overall efficiency of building equipment maintenance? - This technology improves efficiency by enabling predictive maintenance and automated troubleshooting, reducing downtime and optimizing equipment performance.
2. What are the potential cost savings associated with implementing this AI-powered servicing method? - Implementing this technology can lead to significant cost savings through reduced maintenance costs, minimized downtime, and enhanced equipment longevity.
Original Abstract Submitted
a method for servicing building equipment using generative artificial intelligence models includes receiving a multi-modal data input characterizing operation of the building equipment using multiple modes of data, associating related data portions from each mode of the multi-modal data input to form a set of original analysis packages, training at least one data generator to generate artificial analysis packages using the original analysis packages, using the at least one data generator to generate a set of artificial analysis packages, and adjusting an output model using the set of artificial analysis packages and the set of original analysis packages. the output model is configured to generate a service relevant multi-modal data output for use in servicing the building equipment.
