Multivariate Event Prediction Using Latent Embeddings for Process Reliability
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Solution Overview
Problem
Existing solutions for predicting events in manufacturing and process industries with multiple event types are inadequate, as they often destroy relationships between event types, leading to loss of valuable information, and manual optimization model generation is time-consuming and impractical for large-scale operations.
Innovation Solution
A computing system that receives multivariate data from sensors, projects it to a lower stochastic latent embedding, learns temporal structures, and provides probabilistic predictions using a variational autoencoder (VAE) and recurrent neural network (RNN) to capture complex interactions and anomalies, enabling automated generation of optimization models for system-wide plant optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If multivariate event sequences are converted to univariate sequences for prediction, then the prediction process becomes simpler, but the relationship between event types is destroyed and valuable information is lost
Solution Approach 1:
The patent transforms the prediction problem from the original event space to a latent embedding space through dimensionality reduction. The VAE encoder projects high-dimensional multivariate event sequences into a lower-dimensional latent space, where temporal patterns are preserved but computational complexity is reduced. This resolves the contradiction by changing the dimensionality rather than simplifying the data structure.
Solution Approach 2:
The patent introduces a latent embedding space as an intermediary representation between the original multivariate event sequences and the prediction model. This intermediate latent space captures the essential temporal dependencies and relationships between events while being computationally more tractable, thus preserving information while reducing complexity.
2Measurement precision
If manual optimization model generation is used to capture complex system relationships, then model accuracy improves, but the process becomes time-consuming and impractical for large-scale operations
Solution Approach 1:
The patent implements automated model generation where the system learns temporal patterns and generates optimization models autonomously through the VAE-RNN framework. The model automatically captures complex relationships between multivariate event sequences without requiring manual specification, enabling scalable deployment across large-scale operations while maintaining high accuracy.
Solution Approach 2:
The patent replaces the manual mechanical process of optimization model generation with an automated learning-based system. The VAE-RNN framework automatically learns temporal dependencies and generates predictions, substituting the time-consuming manual modeling process with an efficient computational approach that scales to large systems.
3Measurement precision
If high-dimensional event sequences are analyzed in detail to capture all event relationships, then prediction accuracy improves, but computational complexity increases significantly
Solution Approach 1:
The patent extracts the essential temporal patterns and relationships from high-dimensional event sequences by projecting them into a compressed latent embedding space. The VAE encoder identifies and extracts the most significant temporal dependencies while discarding redundant information, achieving accurate predictions with reduced computational complexity.
Solution Approach 2:
The patent changes the parameter representation of event sequences by transforming them from high-dimensional raw event data into a lower-dimensional latent space representation. This parameter transformation preserves the essential predictive information while significantly reducing the computational burden of analyzing complex multivariate relationships.
Data Source
AI summary
A computer implemented method of administering a complex system includes receiving multivariate data from a plurality of sensors of the system in an ambient state. Event sequences in the received multivariate data are identified. The multivariate event sequences are projected to a lower stochastic latent embedding. A temporal structure of the sequences is learned in a lower latent space. A probabilistic prediction in the lower latent space is provided. The probabilistic prediction in the lower stochastic latent space is decoded to an event prediction in the ambient state.