Building Automation Causal Chains for Faster Fault Diagnosis
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Solution Overview
Problem
Conventional building management systems (BMS) require manual intervention by facility managers to identify fault causes and determine responsive actions, leading to prolonged resolution times due to the cognitive analysis of fault patterns and causal chains.
Innovation Solution
A building automation system that automatically generates causal chains, identifies root causes, and determines responsive actions without human intervention, using input components, processors, and output components to collect, process, and provide facility data, suggested causes, and causal chains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional BMS manually analyze faults using facility manager experience, then fault identification accuracy is maintained, but resolution time increases significantly
Solution Approach 1:
The system enables self-service by automatically generating causal chains and identifying root causes without requiring facility manager intervention. The automated causal chain generation process analyzes facility data, identifies causal factors, and determines responsive actions independently, eliminating the need for manual cognitive analysis while maintaining accuracy through systematic data processing.
Solution Approach 2:
The patent replaces the mechanical cognitive process of facility managers with an automated computational system. Instead of relying on human experience and manual analysis, the system uses automated algorithms to generate causal chains, identify root causes, and determine responsive actions, substituting human cognitive mechanics with computational mechanics.
2Loss of time
If automated causal chain generation is implemented, then resolution time is reduced, but system complexity increases
Solution Approach 1:
The system segments the fault analysis process into distinct automated components: data collection from facility systems, causal chain generation, root cause identification, and responsive action determination. This segmentation allows each function to be handled by specialized automated modules, reducing overall system complexity while enabling rapid resolution through parallel processing of different analysis aspects.
Solution Approach 2:
The patent introduces an automated causal chain generation process as an intermediary between fault detection and responsive action. This intermediary systematically processes facility data, identifies causal factors, and generates structured causal chains that bridge the gap between raw data and actionable insights, reducing the complexity burden on facility managers while maintaining comprehensive analysis.
3Extent of automation
If manual fault analysis is performed, then system automation level is maintained at current level, but productivity decreases
Solution Approach 1:
The system performs preliminary actions by automatically generating causal chains and identifying root causes before facility managers need to intervene. The automated process continuously monitors facility data, pre-processes information, and prepares causal chain analyses in advance, enabling rapid responsive actions when faults occur and significantly improving fault resolution productivity.
Solution Approach 2:
The system enables self-service by automatically generating causal chains and identifying root causes without requiring facility manager intervention. The automated causal chain generation process analyzes facility data, identifies causal factors, and determines responsive actions independently, eliminating the need for manual cognitive analysis while maintaining accuracy through systematic data processing.
Data Source
AI summary
There is disclosed a building automation system, and a method thereof, for managing causal chain. Facility data of the building automation system is collected. One or more suggested causes and one or more causal chains are generated based on the facility data. One or more responsive actions are determined based on the suggested cause or causes, the causal chain or chains, and a cause-action mapping. A particular causal chain is provided based on the at least one responsive action and manager information.