Predictive Analytics Engine for Real-Time Manufacturing Feedback
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Solution Overview
Problem
Manufacturing systems face challenges in detecting issues in real-time due to disparate and unstructured machine data, leading to problems being identified only after process breakdowns or lower-than-expected yields.
Innovation Solution
The implementation of a multi-layer software application that utilizes real-time machine learning data, structured through an adaptor like CAMx, to push data to databases for analytics, enabling predictive modeling and visualization, which drives actions to prevent failures and improve production efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional machine data collection methods are used, then machines can provide operational data, but the data is in disparate and unstructured formats that make monitoring difficult
Solution Approach 1:
The patent introduces an intermediary layer (data collection apparatus and processing system) that sits between the machines and the monitoring system. This intermediary standardizes and structures the disparate machine data formats into a unified structure, making the data suitable for monitoring without requiring changes to the machines themselves or creating excessive complexity in the data collection process
2Reliability
If traditional monitoring approaches are used, then issues are detected after process breakdowns or lower-than-expected yields, but real-time predictive capability is lost
Solution Approach 1:
The system performs preliminary actions by continuously analyzing machine data in real-time to predict potential issues before they occur. The data collection and analysis apparatus identifies trends and patterns that indicate future failures, allowing the system to take preventive actions before actual breakdowns happen, thus improving reliability while reducing response time
Solution Approach 2:
The patent implements a feedback mechanism where machine operational data is continuously collected, analyzed, and used to generate predictions about future issues. This feedback loop enables real-time monitoring and predictive capabilities, allowing the system to detect potential problems early and respond before they become actual breakdowns, resolving the contradiction between detection timing and response time
3Productivity
If real-time data collection and analysis is implemented, then predictive analytics and failure prevention are achieved, but system complexity increases
Solution Approach 1:
The patent segments the data collection and analysis system into distinct functional components: data collection apparatus, data processing system, and predictive analytics engine. This segmentation allows each component to perform its specific function efficiently, reducing overall system complexity while enabling real-time predictive analytics that improve production efficiency
Data Source
AI summary
A predictive analytics apparatus, engine, system and method capable of providing real time analytics in a manufacturing system. Included are a data input capable of receiving raw data output from at least one machine operable to effect the manufacturing system embodiments, and a processor associated with a computing memory and suitable for executing code from the computing memory. The code may include an adaptor; an extractor; predictive analytics capable of receiving the extracted processed data and applying thereto at least one predictive model comprised of target data for the at least one machine, and capable of providing feedback to the at least one machine to modify performance of the at least one machine based on the application of the at least one predictive model; and a visualizer capable of providing a visualization of the feedback.