Event Horizon Forecasting With Condition-Specific ML Models
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Solution Overview
Problem
Rule-based alerting systems in complex systems, such as power plants and industrial machines, struggle to accurately predict equipment failures and consumable exhaustion, often resulting in false-positive alerts and inadequate early warning times for maintenance.
Innovation Solution
A machine learning-based approach that aggregates signal data from sensors into feature vectors, generates label data, and trains separate models for distinct conditions to predict event horizons, providing more accurate and reliable forecasts for timely interventions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If rule-based alerting systems are used to predict equipment failures, then the system complexity is low and ease of operation is maintained, but prediction accuracy deteriorates and false-positive alerts increase
Solution Approach 1:
The patent segments the prediction system into multiple specialized machine learning models, each trained on specific feature vectors derived from different sensor data sources. This segmentation allows each model to focus on particular failure modes or equipment types, improving prediction accuracy while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The patent introduces machine learning algorithms as intermediary components between raw sensor data and failure predictions. These ML models act as mediators that transform complex sensor signals into meaningful predictive insights, bridging the gap between simple rule-based systems and the need for accurate predictions without requiring direct complex rule formulations.
2Loss of time
If rule-based alerting systems are used, then the system is easy to operate, but early warning time is insufficient and alert fatigue occurs
Solution Approach 1:
The patent implements preliminary action by training machine learning models on historical sensor data and failure patterns before deployment. This pre-training phase allows the system to learn from past failures and establish predictive capabilities in advance, enabling earlier and more accurate warnings without requiring complex real-time rule formulations during operation.
Solution Approach 2:
The patent incorporates feedback mechanisms where prediction outcomes and actual failure data are fed back into the machine learning models for continuous improvement. This feedback loop enables the system to refine its predictions over time, reducing false positives and alert fatigue while maintaining ease of operation through automated model retraining and validation.
3Reliability
If machine learning models are trained on all conditions together, then the system is simpler to implement, but prediction reliability for distinct conditions deteriorates
Solution Approach 1:
The patent segments the training process into distinct condition-specific datasets, where separate machine learning models are trained on feature vectors corresponding to different operational conditions, equipment types, or failure modes. This segmentation improves prediction reliability for each specific condition by allowing models to specialize, while the modular approach keeps implementation complexity manageable through systematic training procedures.
Data Source
AI summary
A method for generating forecast predictions that indicate an event horizon of an entity or remaining useful life of a consumable using machine learning techniques is provided. Using a server computer system, feature data comprising features vectors that represent a set of signal data over a range of time is stored. Condition data comprising conditions occurring on the entity at particular moments in time is stored. Label data that comprises a plurality of time values that each indicate a difference in time between one condition and another condition is stored. A training dataset is created by combining the feature data, the condition data, and the label data into a single dataset. The training dataset is partitioned by condition. A machine learning model is trained on each target condition training dataset. The trained machine learning models are used to generate forecast values that each indicate an amount of time to an occurrence of a target condition associated with an entity.