Manufacturing Chamber Diagnostics Using Summary Sensor Data
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Solution Overview
Problem
Conventional manufacturing equipment diagnostic systems face inefficiencies in isolating informative sensor data from large datasets, leading to delayed corrective actions, increased waste, and unscheduled downtime due to sub-optimal performance caused by drifting, aging, or failing components.
Innovation Solution
The system generates summary data from trace sensor data using statistical and machine learning methods, enabling quick analysis and identification of problematic components, and provides a quality index score to trigger corrective actions before sub-optimal products are produced, thereby reducing waste and downtime.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional diagnostic systems analyze large sensor datasets, then comprehensive monitoring is achieved, but analysis time increases and corrective actions are delayed
Solution Approach 1:
The patent extracts only the most informative sensor data from large datasets using criteria-based filtering and outlier detection. By identifying and isolating critical data points that indicate component degradation, the system achieves accurate diagnostics without analyzing every sensor reading, thus reducing analysis time while maintaining reliability.
Solution Approach 2:
The patent segments the analysis process into distinct stages: data collection, summary generation, outlier detection, and diagnostic evaluation. This segmentation allows the system to process large datasets systematically, focusing computational resources on identifying critical anomalies rather than uniformly processing all data, thereby reducing overall analysis time.
2Measurement precision
If comprehensive sensor data is collected and analyzed, then accurate fault detection is achieved, but processing complexity increases
Solution Approach 1:
The patent introduces summary data as an intermediary between raw sensor data and diagnostic conclusions. Summary statistics and aggregated metrics serve as a intermediate representation that simplifies the data structure while preserving essential information about component performance, reducing processing complexity without sacrificing detection accuracy.
Solution Approach 2:
The patent applies partial action by focusing analysis only on sensor data that exhibits outliers or deviations from normal patterns. Rather than uniformly processing all sensor data with equal computational intensity, the system applies enhanced analysis only where needed—to detect anomalies—thereby reducing overall processing complexity while maintaining accurate fault detection.
3Reliability
If real-time monitoring of all manufacturing parameters is implemented, then sub-optimal performance is detected early, but energy consumption and computational resources increase
Solution Approach 1:
The patent implements partial monitoring by continuously collecting all sensor data but applying intensive analysis only to identify outliers and anomalies. Normal operating parameters are monitored with minimal processing, while deviations from expected patterns trigger more detailed analysis. This approach maintains early detection capability while significantly reducing computational energy consumption compared to uniform real-time analysis of all parameters.
Data Source
AI summary
A method includes receiving trace sensor data associated with a first manufacturing process of a manufacturing chamber. The method further includes processing the trace sensor data by a processing device to generate summary data associated with the trace sensor data. The method further includes generating a quality index score based on the summary data. The method further includes providing an alert to a user based on the quality index score. The alert includes an indication that the manufacturing chamber performance does not meet a first threshold.