Contextual Workflow Encoding for Time-Series Event Detection
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Solution Overview
Problem
Current systems for analyzing time series data in industrial settings, such as chemical plants and wellbore environments, require expert intervention and are not capable of providing real-time insights or automatically identifying anomalies, relying on initial expert-defined models that do not adapt or learn over time.
Innovation Solution
A system utilizing machine learning models and feedback from user interactions to identify features, correlations, and events in time series data, allowing for self-learning and self-labeling, which correlates user queries to generate knowledge graphs and contextual workflows for anomaly detection and event identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If expert-defined models are used for initial analysis, then the system can provide basic anomaly detection capability, but the system cannot adapt or learn over time and requires continuous expert intervention
Solution Approach 1:
The system enables self-learning through automated machine learning model generation. Users provide examples of normal and abnormal data patterns, and the system automatically generates and refines detection models without requiring expert intervention in model creation or adaptation
Solution Approach 2:
The system implements feedback loops where detection results are continuously evaluated and used to refine models. The automated learning process incorporates feedback from detected anomalies and user corrections to improve detection accuracy over time
2Productivity
If manual expert analysis is used for each anomaly, then detection accuracy can be maintained, but real-time insights and automated identification are not achieved
Solution Approach 1:
The system replaces manual expert analysis with automated machine learning models. These models process data in real-time, automatically identifying anomalies without requiring human intervention while maintaining detection accuracy through continuous learning and refinement
Solution Approach 2:
The system creates digital representations of expert knowledge through machine learning models. By training on expert-labeled data, the models capture and replicate expert detection patterns, enabling automated analysis that mirrors expert accuracy at scale
3Adaptability or versatility
If multiple specialized models are created for different anomalies, then comprehensive coverage is achieved, but the complexity of model creation and maintenance increases significantly
Solution Approach 1:
The system employs a universal framework for anomaly detection that can handle multiple types of anomalies through a single platform. The machine learning models are designed to be multi-functional, adapting to different data patterns and anomaly types without requiring separate specialized systems
Solution Approach 2:
The system segments the anomaly detection process into modular components: data ingestion, pattern recognition, model generation, and validation. This modular architecture allows comprehensive coverage through composition of simpler, manageable modules rather than requiring monolithic complex models
Data Source
AI summary
A method of workflow selection and event detection comprises: receiving, on a user interface, a plurality of selections from a user, wherein the plurality of selections identifies a plurality of time series data elements; correlating the plurality of selections with a plurality of workflows, wherein each workflow of the plurality of workflows defines a set of associated time series data elements; identifying a first workflow of the plurality of workflows associated with the plurality of selections; retrieving a first set of time series data elements associated with the first workflow; and identifying an event using the first set of time series data elements.