Building analysis system with machine learning based interpretations
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Solution Overview
Problem
Traditional HVAC data analysis systems rely on manual modification of criteria and often fail to accurately predict equipment performance or maintenance needs, leading to potential malfunctions and inefficiencies.
Innovation Solution
A vibration analysis system that processes data from building equipment to generate performance predictions and explanations, using a prediction model to identify maintenance requirements and display results through a user interface, allowing for feedback incorporation to improve accuracy over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional data analysis systems use manual modification of criteria, then the system is easy to operate and understand, but the measurement precision and reliability of performance prediction deteriorate
Solution Approach 1:
The patent replaces manual mechanical modification of analysis criteria with an automated machine learning model that processes vibration data. The system uses algorithms to automatically generate performance predictions and explanations, eliminating the need for manual criterion adjustment while improving prediction accuracy through data-driven insights.
Solution Approach 2:
The machine learning model performs self-learning and self-adjustment by automatically analyzing vibration data patterns and improving its predictions over time. The system generates its own performance criteria through training on historical data, eliminating dependency on manual expert intervention while maintaining high precision.
2Device complexity
If traditional systems rely on fixed analysis criteria, then the device complexity is low, but the adaptability to different equipment conditions deteriorates
Solution Approach 1:
The system transitions from static fixed criteria to dynamic adaptive criteria through machine learning. The model continuously learns from new vibration data and adjusts its analysis parameters automatically, enabling it to adapt to different equipment conditions and failure modes without increasing operational complexity for users.
Solution Approach 2:
The machine learning model dynamically changes analysis parameters based on the specific equipment being monitored. It automatically adjusts vibration thresholds, frequency ranges, and prediction criteria according to the unique characteristics of each piece of equipment, providing high adaptability while maintaining a simple user interface.
3Ease of manufacture
If manual analysis is used, then the system is simpler to implement, but the productivity and speed of diagnosis deteriorate
Solution Approach 1:
The patent replaces manual diagnostic processes with automated machine learning analysis. The system processes vibration data and generates performance predictions instantly, dramatically increasing diagnostic productivity while keeping the system implementation straightforward through standardized hardware and software integration.
Solution Approach 2:
The machine learning model acts as an intermediary between raw vibration data and human operators. It automatically processes and interprets complex vibration patterns, providing actionable insights that accelerate diagnostic productivity while allowing the system to be implemented using standard computing infrastructure.
4Device complexity
If traditional systems provide only predictions without explanations, then the device complexity is low, but the loss of information deteriorates
Solution Approach 1:
The system implements feedback by providing explanations that show users what vibration patterns led to specific predictions. This feedback loop helps users understand the reasoning behind predictions, retain more information about equipment conditions, and potentially provide corrective feedback to improve the model, all while maintaining manageable system complexity.
Solution Approach 2:
The explanation generation module serves as an intermediary that translates complex machine learning predictions into understandable information for users. It preserves and communicates critical information about vibration patterns and prediction confidence without requiring the system to become significantly more complex.
Data Source
AI summary
A vibration analysis system for predicting performance of a building system includes one or more memory devices configured to store instructions that, when executed on one or more processor, cause the one or more processors to receive vibration data from the building equipment, generate a performance prediction for the building equipment, generate a performance explanation for the performance prediction, and cause a user interface to display the performance prediction and the performance explanation.