Uniform Equipment Characterization via Data Normalization
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Solution Overview
Problem
Existing solutions for data generalization from equipment in buildings are manual, time-consuming, and often lead to loss of important information, failing to provide a correct and effective representation of equipment characteristics, and are reactive rather than preemptive.
Innovation Solution
A system and method that uniformly characterizes equipment categories by extracting data points, normalizing frequencies, handling variances, and mapping data to aggregation methods and algorithms, using modules like information extraction, frequency decoupling, variance handling, and user reference to generate uniform equipment characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data generalization is used to identify equipment behavior, then equipment characteristics can be represented, but the process is time-consuming and requires considerable manual effort
Solution Approach 1:
The system enables equipment to automatically generate and transmit their own operational data to the server. The server then automatically processes, normalizes, and generalizes this data without requiring manual intervention. This self-service approach eliminates time-consuming manual data collection and processing while maintaining accurate equipment characteristic representation.
Solution Approach 2:
The patent replaces manual mechanical processes with automated computational systems. Instead of manual data collection and analysis, the system uses servers with processors that automatically extract, normalize, and generalize equipment data through software algorithms, dramatically reducing the time required while maintaining or improving accuracy.
2Ease of operation
If existing data generalization solutions are used, then some equipment behavior can be identified, but important information is lost leading to incorrect representation
Solution Approach 1:
The system extracts specific relevant data points from equipment data streams while filtering out irrelevant information. The server identifies and extracts key operational parameters that define equipment characteristics, ensuring that only meaningful information is processed and generalized. This selective extraction prevents information loss by focusing on critical data elements.
Solution Approach 2:
The system transforms raw equipment data into standardized parameters through normalization processes. By converting diverse equipment data into uniform parameter formats, the system preserves essential information while enabling meaningful comparisons and generalizations across different equipment types without losing critical characteristics.
3Adaptability or versatility
If data from multiple equipment with different manufacturers and models is collected, then comprehensive equipment coverage is achieved, but data variance increases making generalization difficult
Solution Approach 1:
The system creates a universal data processing framework that can handle multiple equipment types from different manufacturers and models. The server applies standardized normalization and generalization algorithms that work across diverse equipment categories, enabling comprehensive coverage without proportionally increasing processing complexity. The universal approach treats different equipment types through a common methodology.
Solution Approach 2:
The system standardizes diverse equipment parameters by transforming them into unified formats. Through parameter normalization, the system converts varying data structures from different manufacturers into consistent parameter sets, reducing processing complexity while maintaining comprehensive equipment category coverage. This parameter transformation enables uniform handling of heterogeneous equipment data.
4Reliability
If existing solutions work based on reactive action, then equipment behavior can be analyzed after occurrence, but preemptive actions cannot be taken
Solution Approach 1:
The system performs preliminary analysis of equipment data to identify trends and patterns before problems occur. By continuously monitoring and generalizing equipment operational data, the system can detect early signs of equipment degradation or abnormal behavior, enabling preemptive maintenance actions before actual failures occur. This preliminary action extends the useful operational duration of equipment.
Solution Approach 2:
The system establishes continuous feedback loops where equipment data is constantly collected, analyzed, and used to generate insights that feed back into equipment operation and maintenance decisions. This real-time feedback mechanism enables both reliable behavior analysis and timely preemptive actions by immediately responding to detected patterns and trends in equipment performance.
Data Source
AI summary
Aspects of the invention are directed towards a system and a method for uniformly characterizing equipment category. One or more embodiments of the invention describe the method comprising steps of extracting a plurality of data points from data stream received from a plurality of equipment within an equipment category. The method also comprises steps of normalizing the varying frequency of the extracted data points to generate time normalized information corresponding to each of the plurality of equipment. The method comprises steps of manipulating a plurality of variances in the translated uniform data stream received from each of the plurality of equipment within the equipment category. The method comprises steps of generically mapping the normalized information to one or more aggregation methods and for mapping the output of aggregation methods to one or more reusable data processing algorithms to generate a plurality of uniform equipment characteristics corresponding to the equipment category.