Time-Correlated Reliability Streams for Building Sensor Faults
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Solution Overview
Problem
Building data collected by equipment is often unreliable due to temporal faults, configuration issues, measurement uncertainty, and improper commissioning, which hinders the performance of artificial intelligence systems in building management.
Innovation Solution
A building system that generates time-correlated data streams and reliability streams, including virtual streams, to improve data quality by filtering out unreliable data and providing network traffic analysis for enhanced decision-making, and identifies performance issues through entity relationships and anomaly detection models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If building equipment collects and transmits data continuously, then data availability for AI systems is improved, but data reliability deteriorates due to temporal faults, configuration issues, and network traffic variations
Solution Approach 1:
The patent introduces a data quality assessment system that acts as an intermediary between building equipment and AI platforms. This intermediary evaluates data reliability using multiple indicators including network traffic analysis, equipment operational status, and data anomaly detection, then filters or flags data accordingly before transmission to AI systems.
Solution Approach 2:
The system performs preliminary data quality assessment and reliability evaluation before data is transmitted to AI platforms. By pre-evaluating data quality metrics and identifying potential issues such as temporal faults or configuration problems, the system prevents unreliable data from reaching the AI system.
2Productivity
If AI systems use all available building data for decision-making, then processing efficiency is improved, but decision accuracy deteriorates due to corrupt or unreliable data
Solution Approach 1:
The patent applies different quality assessment criteria and filtering levels to different data sources and data types within the building system. Rather than uniformly processing all data, the system evaluates each data stream's reliability based on its specific characteristics, equipment source, and operational context, allowing AI systems to weigh different data sources appropriately.
3Reliability
If the system filters out unreliable data points, then data quality for AI systems is improved, but information loss increases due to removal of potentially valid data
Solution Approach 1:
The system implements feedback mechanisms where data quality assessments and filtering decisions are continuously monitored and adjusted. The AI platform receives feedback about data reliability metrics and can adjust its processing accordingly, while the data quality system learns from AI system performance to refine its filtering criteria and reduce false positives.
Data Source
AI summary
A building system including one or more memory devices configured to store instructions that, when executed on one or more processors, cause the one or more processors to collect building device data of a building device, the building device data comprising a plurality of data samples of a data point and generate a time correlated data stream for the data point, the time correlated data stream comprising values of the plurality of data samples of the data point. The instructions cause the one or more processors to generate a time correlated reliability data stream for the data point, the time correlated reliability data stream comprising a plurality of reliability values indicating reliability of the values of the plurality of data samples of the data point.