Manufacturing Event Sequence Embeddings for Fast Pattern Clustering
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Solution Overview
Problem
Analyzing event sequences in manufacturing plants is challenging due to high volumes of data, variable sequence lengths, and the need for fast and accurate decision-making, as existing methods are either computationally expensive or fail to capture implicit information, making it difficult to identify patterns and anomalies in real-time.
Innovation Solution
The method involves receiving event data, determining event sequences, and using novel embedding pipelines (ESE-E and ESE-L) to convert these sequences into fixed-length embeddings, which can be clustered using alignment-free and efficient algorithms like K-means, preserving the order of values and capturing implicit patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning-based models (e.g., DeepCluster) are used to cluster event sequences, then clustering performance is improved, but computational cost and training time increase significantly
Solution Approach 1:
The patent introduces alignment-free embedding pipelines (ESE-E and ESE-L) as intermediary components that transform event sequences into fixed-length numerical representations without requiring expensive deep learning training. These embeddings serve as a bridge between raw event sequences and clustering algorithms, enabling efficient processing while preserving sequential information through techniques like k-mer counting and latent space projection.
2Productivity
If feature engineering methods (e.g., one-hot encoding) are used to represent event sequences, then computational efficiency is improved, but ability to capture implicit information and patterns is reduced
Solution Approach 1:
The patent transforms event sequences from variable-length symbolic data into fixed-dimensional numerical embeddings through dimensionality transformation. The ESE-E pipeline uses k-mer frequency counting to project sequences into fixed-length vectors, while ESE-L employs latent space projection to capture sequential patterns in a compressed numerical form, enabling both efficiency and information preservation.
Solution Approach 2:
The patent changes the parameter representation of event sequences from discrete symbolic events to continuous numerical embeddings. By transforming event types, timestamps, and sequence positions into numerical features with appropriate scaling and encoding, the system enables clustering algorithms to operate efficiently while capturing subtle patterns that would be lost in simple one-hot encoding.
3Measurement precision
If manual review of event sequences is performed, then analysis accuracy is improved, but time consumption increases significantly
Solution Approach 1:
The patent implements an automated system that performs event sequence analysis without human intervention. The embedding pipelines automatically transform raw event logs into clustered patterns, identifying anomalies and frequent sequences through unsupervised learning. This self-service approach eliminates manual review time while maintaining analysis accuracy through sophisticated numerical representations that capture sequential dependencies.
4Loss of information
If clustering algorithms are applied to raw event sequences, then pattern identification capability is improved, but computational complexity increases due to variable lengths and misalignment
Solution Approach 1:
The patent applies preliminary transformation steps before clustering to convert variable-length event sequences into fixed-length embeddings. The ESE-E and ESE-L pipelines perform preprocessing including k-mer extraction, frequency counting, and latent space projection, which standardize the input data structure and enable efficient clustering without requiring complex alignment algorithms or handling of variable sequence lengths.
Data Source
AI summary
A system and methods for event analysis are disclosed. The system and methods can be employed analyze at least one event data stream from a monitored system. The system and methods advantageously leverage two novel embedding pipelines to enable event sequences extracted from the event data stream to be more effectively clustered and mined for patterns, thereby enabling a better understanding of the event sequences. As a result, the system and methods better assist operators and engineers in studying the cause-and-effect relationships between events so that they can prevent undesirable events from occurring in the monitored system.