Machine Learning Model for Time Series Workflow Analysis
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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 cannot provide real-time feedback or automatically identify insights beyond initial expert-identified correlations, limiting their ability to adapt and improve over time.
Innovation Solution
A system utilizing machine learning models that track user queries, correlate them to determine workflow neighbors, and classify features from time series data, allowing for self-learning and self-labeling by using user feedback to improve model accuracy and adapt to new events and anomalies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If expert intervention is used to identify correlations in time series data, then measurement precision is improved, but device complexity and loss of time increase
Solution Approach 1:
The system enables self-service by implementing automated machine learning models that independently identify correlations and anomalies in time series data without requiring expert intervention. The model continuously learns from data patterns and automatically updates its understanding of system behavior, eliminating the need for human experts to manually analyze correlations while maintaining high measurement precision.
Solution Approach 2:
The system implements feedback mechanisms where the machine learning model continuously receives input from time series data, processes it, and uses the results to refine its understanding. The model learns from past patterns and feedback loops, automatically improving its correlation identification accuracy over time without requiring external expert input, thus reducing device complexity while maintaining precision.
2Measurement precision
If expert-identified correlations are used as initial insights, then measurement precision is improved, but adaptability deteriorates because the system cannot identify insights beyond initial expert identification
Solution Approach 1:
The system applies dynamics by implementing a machine learning model that continuously adapts and evolves its understanding of correlations in time series data. Rather than relying on static expert-identified correlations, the model dynamically learns new patterns as data arrives, automatically updating its internal representations to reflect emerging insights and changing system behavior, thereby achieving both precision and adaptability.
Solution Approach 2:
The machine learning model performs self-service by independently discovering and identifying new correlations and insights from time series data without requiring ongoing expert intervention. The model autonomously learns beyond initial expert knowledge, continuously expanding its understanding of system relationships and adapting to new patterns, thus achieving both high initial precision and continuous adaptability.
3Productivity
If real-time analysis is implemented, then productivity is improved, but device complexity increases due to the need for automated learning systems
Solution Approach 1:
The system replaces complex mechanical or manual analysis systems with an automated machine learning model that processes time series data in real-time. The model uses computational algorithms to automatically identify correlations, events, and anomalies, substituting what would otherwise require complex human expert systems or manual analysis procedures, thereby achieving real-time productivity with manageable complexity.
Solution Approach 2:
The machine learning model provides self-service by automatically performing real-time analysis of time series data without requiring external control or complex orchestration. The model independently processes incoming data, identifies patterns, and generates insights in real-time, simplifying the overall system architecture while maintaining high productivity through autonomous operation.
Data Source
AI summary
A method for capturing user workflows can include tracking user queries for a plurality of users, correlating the user queries between two or more users of the plurality of users, determining that the user queries of the two or more users of the plurality of users are correlated, and classifying the user queries of the at least two users as a workflow neighbor. The workflow neighbor defines a set of time series data or features.