Sensor-Based Recipe Generation for Accurate Process Documentation
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Solution Overview
Problem
Existing industrial process documentation methods, such as manual lab notebooks and Process Knowledge Administration (PKA) tools, are inaccurate, time-consuming, and difficult to replicate, leading to potential safety risks and compliance issues due to incomplete or incorrect process recordings.
Innovation Solution
A computer-implemented method that captures sensor data in real-time to automatically generate a process definition, including materials, equipment, operations, and timing, using machine learning to analyze and identify the process steps and hierarchy levels, with visualizations for improved accuracy and real-time documentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual documentation methods (lab notebooks, PKA tools) are used to record process operations, then the documentation can be created with minimal equipment, but the accuracy and completeness of process recording deteriorates leading to errors and compliance issues
Solution Approach 1:
The patent replaces manual mechanical documentation methods (writing in lab notebooks, using PKA tools) with an automated sensor-based system. Sensors capture process data directly from the environment, eliminating manual recording and reducing errors. The system substitutes human-operated mechanical documentation with automated electronic sensing and processing.
Solution Approach 2:
The documentation system performs self-service by automatically capturing, analyzing, and recording process operations without requiring manual intervention. The sensors continuously monitor process parameters and the system autonomously generates documentation, freeing operators from manual recording tasks while ensuring consistent and accurate documentation.
2Productivity
If manual process recording methods are used, then the implementation is simple and quick to deploy, but the time required to complete documentation and the risk of errors increases
Solution Approach 1:
The system replaces slow manual documentation processes with automated sensor-based capture and machine learning analysis. This substitution dramatically increases documentation speed while simultaneously improving reliability through consistent, error-free automated recording of process operations.
Solution Approach 2:
The system implements continuous feedback loops where sensors monitor process operations in real-time, the machine learning model analyzes the captured data, and the system automatically adjusts and refines documentation. This feedback mechanism ensures high compliance and eliminates errors by continuously verifying process accuracy.
3Loss of information
If detailed sensor data capture and machine learning analysis are implemented to improve documentation accuracy, then the completeness and replicability of process definitions improve, but the device complexity and computational requirements increase
Solution Approach 1:
The system performs preliminary actions by pre-configuring sensors to capture relevant process data before operations begin. The machine learning model is pre-trained with domain knowledge to efficiently analyze sensor data. This preliminary preparation ensures complete information capture without requiring complex real-time processing during operations.
Solution Approach 2:
The documentation system is segmented into modular components: sensor modules for data capture, processing modules for analysis, and output modules for documentation generation. This segmentation allows the system to manage complexity through modular architecture while maintaining complete and accurate process information through coordinated operation of specialized components.
Data Source
AI summary
Techniques for automatically generating a process definition for an industrial process to create a product in an industrial plant are provided, including capturing sensor data associated with an individual performing a set of process operations to a set of process materials to make a product, analyzing the sensor data associated with the individual performing the set of process operations to the set of process materials to make the product, and identifying, based on analyzing the sensor data associated with the individual performing the set of process operations to the set of process materials to make the product, a process definition including the set of process materials, the equipment used to make the product, the set of process operations applied to the materials to make the product, a sequence of the process operations, a timing of the process operations, and/or a quantity of materials used in the process.