Building Automation Fault Detection With Rule-Based Data Modeling
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Solution Overview
Problem
Existing building automation systems lack user-friendly methods for fault detection and optimization, requiring programming expertise and being cumbersome to operate, which hinders efficient energy management and equipment performance.
Innovation Solution
A computer-facilitated method and system using a hierarchical tree-structured database and user-configurable rules for fault detection and optimization, allowing non-expert users to analyze asset and performance data, generate executable code, and output faults or optimization measures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If statistical analysis methods and statistical process control methods are used for fault detection, then measurement precision and reliability are improved, but device complexity and ease of operation deteriorate due to cumbersome operation and programming requirements
Solution Approach 1:
The patent introduces an intermediary processing layer that automatically transforms raw building automation data into meaningful fault indicators. This intermediary system handles the complex statistical analysis in the background while presenting simplified results to users, thereby maintaining high measurement precision while dramatically improving ease of operation.
Solution Approach 2:
The system performs self-service by automatically executing statistical analysis and fault detection algorithms without requiring user programming. The built-in engine autonomously processes data, applies statistical methods, and generates fault reports, allowing non-expert users to benefit from sophisticated analysis capabilities.
2Reliability
If sophisticated statistical analysis methods are implemented, then reliability of fault detection is improved, but device complexity increases requiring programming expertise
Solution Approach 1:
The patent extracts the complex statistical analysis functionality into a separate, autonomous engine module. This extraction allows the core reliability-enhancing statistical methods to be implemented independently, while the main user-facing system remains simple and requires no programming expertise.
Solution Approach 2:
The system replaces manual programming and configuration with an automated rule-based engine. Instead of requiring users to program statistical analysis, the system uses pre-configured rules and algorithms that automatically process data, thereby maintaining high reliability while reducing system complexity from the user perspective.
3Productivity
If automated code generation is implemented, then productivity is improved by enabling non-experts to configure rules, but device complexity increases requiring runtime code generation capabilities
Solution Approach 1:
The system performs preliminary action by pre-compiling and validating rule templates and code modules during system setup. This allows runtime code generation to simply assemble and execute pre-validated components rather than creating complex code from scratch, thereby improving productivity while managing system complexity through advance preparation.
Solution Approach 2:
The system uses parameter changes to control code generation behavior. By adjusting configuration parameters and runtime settings, the system can generate appropriate code modules without requiring users to understand programming. This parameter-driven approach improves productivity while keeping the underlying complexity hidden through standardized parameter interfaces.
Data Source
AI summary
Method and system is provided for detecting faults and/or providing optimization measures to enhance the performance of one or more buildings, especially building automation equipment of the one or more buildings. The system may include a database for storing a data model comprising asset and performance data of the one or more buildings. The data model may be represented in one or more hierarchical tree-structures, in which the nodes of the tree-structures represent asset data and the leaves of the tree-structures represent performance data of the one or more buildings. The performance data may be represented in each case by data points. The data points may include time series of measured or derived field values and meta information.