Manufacturing Predictive Analytics Using Data Adaptors for Real-Time 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 adaptors like CAMx, to push data into 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 making it unsuitable for real-time monitoring
Solution Approach 1:
The patent introduces data adaptors as intermediary components that sit between machines and the analytics platform. These adaptors normalize and structure data from disparate machine sources into a unified format, making the data suitable for real-time monitoring and predictive analytics without requiring changes to the underlying machines or data collection methods
Solution Approach 2:
The system transforms unstructured machine data into structured formats by changing data parameters and organization. The adaptors convert various data formats, schemas, and structures into a standardized representation that the predictive analytics engine can process, effectively changing the parameter structure of the data while preserving the operational information
2Reliability
If traditional monitoring approaches are used, then issues are detected after process breakdowns or lower-than-expected yields, but this leads to increased downtime and reduced productivity
Solution Approach 1:
The patent implements predictive analytics that perform preliminary detection of potential issues before they cause process breakdowns or yield reductions. By analyzing operational data in real-time and predicting failures in advance, the system enables preventive actions that maintain reliability while avoiding the productivity losses associated with reactive troubleshooting and downtime
Solution Approach 2:
The system establishes a continuous feedback loop where operational data from machines is constantly monitored, analyzed by the predictive analytics engine, and used to generate alerts or recommendations. This real-time feedback mechanism enables dynamic adjustments to maintain optimal performance and prevent issues before they impact productivity
3Reliability
If real-time predictive analytics are implemented, then failures can be predicted before they occur improving machine availability, but this requires complex multi-layer software applications and data processing infrastructure
Solution Approach 1:
The patent divides the predictive analytics system into distinct modular layers: data collection layer, data adaptation layer, analytics engine layer, and visualization layer. Each layer performs a specific function and can be independently configured, maintained, and scaled. This segmentation reduces overall system complexity by creating manageable, loosely-coupled components that work together to achieve real-time predictive analytics and improve machine availability
Data Source
AI summary
A predictive analytics apparatus, engine, system and method capable of providing real time analytics in a manufacturing system. The apparatus, engine, system and method may include 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 capable of pushing the received raw data to one or more databases to processed data; an extractor capable of extracting the processed data from the one or more databases; predictive analytics capable of receiving the extracted processed data and applying thereto at least one predictive model including 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 at least a visualization of the feedback and of the performance.