Asset Status Prediction With Bitemporal Modeling for Downtime Reduction
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Solution Overview
Problem
Existing asset management systems are inefficient and reactive, lacking real-time updates and proactive maintenance, leading to equipment downtime and increased costs due to manual updates and lack of manufacturer visibility.
Innovation Solution
A centralized service that uses a machine learning model to track asset status through bitemporal modeling, predict anomalies, and generate real-time updates, integrating with asset management platforms to facilitate dynamic and flexible management of hardware assets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional reactive asset management methods are used, then implementation simplicity is maintained, but asset uptime and reliability deteriorate due to lack of real-time monitoring and predictive capabilities
Solution Approach 1:
The system performs preliminary actions by training ML models on historical asset data before deployment. The models learn patterns of asset behavior, failure modes, and maintenance requirements in advance, enabling predictive analytics before actual asset issues occur. This allows the system to predict future asset states and schedule maintenance proactively rather than reactively.
Solution Approach 2:
The system implements continuous feedback loops where asset performance data is collected in real-time, compared against ML model predictions, and used to update and refine the models. Maintenance outcomes and asset status changes feed back into the system to improve future predictions. This closed-loop feedback mechanism enhances reliability while managing system complexity through iterative learning.
2Loss of time
If real-time asset monitoring and predictive modeling are implemented, then maintenance precision and downtime reduction are improved, but data processing requirements and computational resources increase
Solution Approach 1:
The system applies partial action by focusing computational resources on the most critical asset parameters and high-risk assets rather than processing all data uniformly. ML models prioritize analyzing data points that have the highest predictive value for asset failure, reducing unnecessary computational overhead while maintaining effective downtime prediction for critical assets.
Solution Approach 2:
The system replaces traditional mechanical monitoring approaches with intelligent software-based ML models. Instead of relying on complex hardware monitoring systems and manual analysis, the patent uses software algorithms to process sensor data, predict failures, and generate maintenance recommendations. This substitution reduces physical infrastructure requirements while enabling more sophisticated analytics.
3Measurement precision
If ML models are continuously trained and updated with actual data, then prediction accuracy is improved, but processing time and computational overhead increase
Solution Approach 1:
The system implements periodic training cycles rather than continuous training. ML models are trained at scheduled intervals using accumulated data batches, allowing the system to balance accuracy improvements with operational continuity. Between training cycles, the models operate in inference mode with minimal computational overhead, predicting asset states based on their learned patterns while new data is collected for the next training iteration.
Solution Approach 2:
The system performs preliminary data preprocessing and feature engineering before training sessions. Historical data is cleaned, normalized, and transformed into appropriate formats in advance, reducing the actual training time. Feature selection and model architecture optimization are done beforehand based on domain knowledge, enabling faster convergence during training while maintaining high prediction accuracy.
Data Source
AI summary
Techniques for using an LLM agent to predict a state of an asset are disclosed. The LLM agent uses bitemporal modeling to track status information of an asset. The status information includes an uptime or a downtime of the asset. The LLM agent is trained to detect anomalies with respect to at least one of the uptime or the downtime of the asset and to predict uptimes and downtimes. The LLM agent generates a prediction regarding a future uptime or a future downtime of the asset. Subsequently, the LLM agent collects data reflecting an actual uptime or an actual downtime of the asset during the given range of time. The LLM agent makes a comparison between the prediction and the actual data and then further trains or instructs the LLM agent based on this comparison.