Temporal State Monitoring for Automated Sensor Insight Generation
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Solution Overview
Problem
Existing SCADA systems require significant human intervention for data analysis and decision-making, leading to potential delays and inefficiencies in industrial processes due to the lack of automated insights from timeseries data.
Innovation Solution
A temporal state monitoring system that processes timeseries data using AI/ML modeling to automatically generate actionable insights and control signals, enabling autonomous process control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human monitors analyze data from sensors in decision-making processes, then insights can be obtained, but time consumption increases and serious problems may occur if data is not timely analyzed
Solution Approach 1:
The system enables self-service automation where the monitoring system automatically generates actionable insights from sensor data without requiring human analysis. The temporal state monitoring system processes timeseries data, identifies state changes, and generates insights autonomously, allowing the system to serve itself rather than relying on human operators for data interpretation.
Solution Approach 2:
The patent replaces the mechanical human decision-making process with an automated computational system. Instead of human operators manually analyzing sensor data and making decisions, the system uses temporal state monitoring algorithms and machine learning models to automatically process data, identify patterns, and generate actionable insights, substituting human cognitive processes with automated computational mechanisms.
2Ease of operation
If human intervention is used for data analysis and decision-making, then flexibility and judgment can be applied, but the process becomes time-consuming and less efficient
Solution Approach 1:
The system performs self-service by automatically monitoring temporal states, detecting changes, and generating actionable insights without human intervention. The temporal state monitoring system autonomously processes timeseries data, applies state specifications, and delivers insights to relevant entities, eliminating the need for human operators to manually analyze data while maintaining operational flexibility through configurable state definitions.
Solution Approach 2:
The system introduces an intermediary automated analysis layer between sensor data collection and decision-making execution. This intermediary temporal state monitoring system processes raw sensor data, identifies meaningful state changes, and generates structured actionable insights that can be directly executed or reviewed, bridging the gap between data collection and decision-making without requiring direct human involvement in the analysis process.
3Speed
If automated systems are implemented for real-time data processing, then response time improves, but system complexity increases
Solution Approach 1:
The system segments the data processing task into distinct components: temporal state determination, state change detection, and actionable insight generation. Each component handles a specific aspect of the analysis, allowing for modular implementation and independent optimization. The temporal state monitoring system divides complex timeseries analysis into manageable state evaluations, reducing overall system complexity while maintaining high processing speed.
Solution Approach 2:
The system implements dynamic temporal state specifications that can adapt to different devices, processes, and operational conditions. State specifications are configurable and can be modified based on changing requirements, allowing the system to maintain high processing speed while adapting to various complexity levels. The dynamic nature of state definitions enables the system to handle different scenarios without requiring complete system redesign.
Data Source
AI summary
A method for monitoring a temporal state of a device includes collecting a set of timeseries data within a time window, the timeseries data being collected from a sensor associated with the device, generating temporal states of the device within the time window based on the set of timeseries data, generating an actionable insight based on the temporal states of the device within the time window, and forwarding the actionable insight to a corresponding entity. The temporal states of the device are generated by running a state specification that includes a set of state definitions for defining different states of the device.