Failure Detection Forecasting from Work-Order Text in Facility Maintenance

The patent creates a virtual copy of the physical facility by generating a digital twin model that replicates the behavior and characteristics of the actual building systems. This digital model allows failure detection and analysis without requiring physical sensors to be installed throughout the facility, thereby maintaining reliability while reducing implementation costs.

The patent replaces the mechanical sensor-based detection system with an information-processing system that uses natural language processing and machine learning algorithms. Instead of using physical sensors to detect failures, the system processes text data from work orders and facility management systems to identify failure patterns, substituting mechanical detection with computational analysis.

The patent extracts the essential failure detection functionality from complex sensor networks and concentrates it in a centralized natural language processing system. By taking out the core analytical function and separating it from the data collection infrastructure, the system achieves high detection accuracy without requiring complex distributed sensor installations across all facilities.

The patent creates a universal natural language processing system that can analyze failure data across multiple different facility types and systems using a single platform. This multi-functional approach allows the same system to handle diverse failure scenarios without requiring facility-specific sensor configurations, thereby reducing overall system complexity while maintaining detection accuracy.