Building Equipment Predictive Maintenance Using Generative AI
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing building management systems face challenges in generating precise data for identifying proper response actions or sequences for servicing building equipment, due to technical issues and variability in equipment characteristics.
Innovation Solution
A building management system utilizing machine learning models trained with structured and unstructured data from building equipment, capable of generating responses for issue detection, service operations, and user guidance, incorporating various machine learning architectures such as language models and neural networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional building management systems are used to service equipment, then the system structure is simple, but the precision of identifying proper response actions and service operations is insufficient
Solution Approach 1:
A generative AI model serves as an intermediary between building equipment data and service operations. The model receives inputs including equipment data, service requests, and technical documentation, then generates precise response actions, diagnostic conclusions, and service recommendations. This intermediary layer transforms unstructured data into actionable service guidance without requiring direct complex rule-based systems.
Solution Approach 2:
The patent replaces traditional mechanical rule-based decision systems with an AI-based cognitive system. Instead of using predefined if-then rules and manual troubleshooting procedures, the system employs machine learning models that can process natural language inputs, understand equipment contexts, and generate appropriate service responses dynamically based on trained patterns from historical service data.
2Measurement precision
If more data is collected from building equipment to improve service precision, then the accuracy of service operations improves, but the difficulty of processing and analyzing the data increases
Solution Approach 1:
The generative AI model performs self-service by automatically processing and analyzing multiple data sources including equipment sensor data, service logs, technical manuals, and warranty information. The system autonomously synthesizes this unstructured data to generate service recommendations without requiring manual data processing or complex data engineering pipelines, effectively serving itself in data analysis.
Solution Approach 2:
The AI model is designed with multi-functionality to handle diverse data types and service scenarios simultaneously. It can process structured sensor data, unstructured technical documentation, natural language service requests, and generate multiple types of outputs including diagnostics, service procedures, and recommendations, replacing multiple specialized tools with a single universal system.
3Productivity
If traditional service methods are used, then the implementation is straightforward, but the productivity and efficiency of service operations are low
Solution Approach 1:
The system performs preliminary actions by pre-training the generative AI model on extensive historical service data, technical documentation, and equipment information before actual service operations. This pre-training enables the model to quickly generate accurate service recommendations during actual operations without requiring complex real-time analysis, thereby improving productivity while maintaining ease of operation.
Solution Approach 2:
The system implements feedback mechanisms where service outcomes are fed back into the training data for continuous model improvement. The AI model learns from actual service results, successful repairs, and technician feedback to refine its recommendations over time, progressively improving service efficiency while the interface remains user-friendly for technicians.
Data Source
AI summary
A method including training, by one or more processors, a generative AI model using first operating data from building equipment and a plurality of first service reports indicating a plurality of first problems associated with the building equipment. The method may include predicting, by the one or more processors using the generative AI model, one or more future problems likely to occur with the building equipment based on second operating data from the building equipment. The method may include automatically initiating, by the one or more processors, one or more actions to prevent the one or more future problems from occurring or mitigate an effect of the one or more future problems.