Transformer Time-Series Control for Rare Hardware Abnormal Events
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Solution Overview
Problem
Existing machine learning techniques struggle to effectively model and optimize hardware systems like manufacturing equipment, electric power systems, and HVAC due to the rarity of abnormal events, lack of integrated data sources, and difficulty in handling complex time series data with multiple inter-correlated variables and optimization goals, especially in environments with limited or no internet connectivity.
Innovation Solution
A time series-based machine learning framework utilizing transformer models and optical programming processors, combined with a blockchained quantum bit communication network, enables efficient prediction and optimization of hardware operations by integrating sensor data, optimization goals, and historical events, allowing for autonomous and human-in-the-loop control, even in environments without internet connectivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional machine learning techniques are used to model hardware systems, then the system complexity remains manageable, but the ability to accurately predict abnormal events and optimize multiple inter-correlated variables deteriorates due to the rarity of abnormal events and complexity of time series data
Solution Approach 1:
The patent segments the machine learning model into multiple specialized components: abnormal event detection models, optimization goal prediction models, and time series analysis models. Each segment handles specific aspects of the complex prediction task, allowing the system to achieve high prediction accuracy for rare abnormal events while managing overall model complexity through modular architecture.
Solution Approach 2:
The patent transforms the prediction problem by adding temporal dimensionality through time series analysis and by integrating multiple prediction dimensions (abnormal events, optimization goals, and their inter-correlations). This multi-dimensional approach enables accurate prediction of rare events by analyzing patterns across extended time horizons and multiple variable relationships simultaneously.
2Loss of information
If integrated data sources are combined to handle complex time series data with multiple variables, then the comprehensiveness of analysis improves, but the difficulty of detecting and measuring inter-correlated relationships increases
Solution Approach 1:
The patent introduces specialized intermediary components including time series analysis modules and correlation detection algorithms that mediate between raw integrated data and prediction models. These intermediaries preprocess and structure the complex inter-correlated data, making the relationships between multiple variables detectable and measurable while preserving the completeness of integrated data sources.
3Productivity
If autonomous control is implemented to reduce manual intervention, then productivity increases, but the reliability of control decisions may deteriorate without sufficient historical abnormal event data
Solution Approach 1:
The patent implements preliminary action by continuously training the machine learning model with historical data and performing predictive analysis before actual abnormal events occur. The system proactively identifies patterns and prepares control decisions in advance, enabling reliable autonomous control even when historical abnormal event data is limited, as the model learns from available data and simulates rare scenarios through predictive analytics.
Solution Approach 2:
The patent incorporates feedback mechanisms where prediction results and control outcomes are continuously fed back into the model for iterative improvement. This feedback loop enhances the reliability of autonomous control decisions by continuously refining the model's understanding of abnormal event patterns and optimization goals, allowing the system to become more reliable over time even with limited initial historical data.
Data Source
AI summary
A method includes: obtaining equipment sensor data from sensors in a time series; obtaining equipment optimization goal data from optimization goals in a time series; obtaining historical data on equipment abnormal events and intervention events; obtaining static equipment input parameters; applying time series model to the equipment sensor and optimization data, historical event data, and static equipment input parameters, to obtain predicted equipment sensor data; optimizing and controlling hardware operation based on the obtained predicted equipment sensor data; and providing predicted actions for abnormal event intervention based on the obtained predicted equipment sensor data. Hardware control, with optical mechanisms, into deep space, with a novel blockchained quantum bit communication network can be included, as changes to existing applications of transformer models, including ring-all reduced training on top of data and model parallelism, novel graph modalities, generic chart generations, generated image enabled search and drop ship, and multi-modal car foundation models.