Sensor Network Abnormality Detection for Complex Equipment
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Solution Overview
Problem
Existing equipment diagnostic techniques struggle to accurately diagnose abnormalities in complex semiconductor equipment due to the difficulty in quantifying time-series data from thousands of sensors, and they fail to effectively analyze changes in equipment status caused by hardware aging, environmental shifts, and unpredictable failures.
Innovation Solution
An equipment abnormality detection device and diagnostic system that utilizes a sensor network to visualize correlations between sensor data, calculates similarity between sensors, and compares connection statuses to identify equipment abnormalities by detecting nodes with maximum connection status differences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If statistical analysis and machine learning are applied to sensor data, then equipment abnormality detection capability is improved, but the ability to derive causes of equipment statuses based on sensor data changes remains insufficient
Solution Approach 1:
The patent segments the complex sensor network into multiple clusters using clustering analysis. Each cluster groups sensors with similar characteristics and temporal patterns. This segmentation enables the system to analyze cause-effect relationships at the cluster level while maintaining the ability to trace individual sensor contributions, thus preserving cause derivation capability while improving abnormality detection through pattern recognition.
Solution Approach 2:
The patent transforms temporal sensor data into a multi-dimensional representation by analyzing correlation coefficients and clustering sensors based on their temporal patterns. This dimensional transformation allows the system to visualize and analyze equipment status changes from multiple perspectives simultaneously, enabling both accurate abnormality detection and comprehensive cause analysis through the multi-dimensional sensor correlation space.
2Adaptability or versatility
If the number of sensors and hardware components increases to improve equipment functionality, then equipment complexity increases, making analysis of sensor data changes and relevance more difficult
Solution Approach 1:
The patent merges individual sensor analyses into cluster-level analyses by grouping sensors with similar temporal patterns and correlation characteristics. This merging reduces the complexity of analyzing thousands of individual sensors while preserving the functional information, as each cluster represents a coherent functional group that can be analyzed as a unified entity.
Solution Approach 2:
The patent changes the analysis parameters from individual sensor readings to correlation coefficients and clustering metrics. By transforming the data representation from raw sensor values to statistical relationships between sensors, the system can handle complex sensor networks efficiently while maintaining the ability to detect and analyze equipment status changes.
3Ease of operation
If direct quantification of time-series data is attempted to diagnose equipment abnormality, then diagnostic capability is limited, as it is difficult to accurately determine abnormality status from raw time-series data
Solution Approach 1:
The patent introduces correlation coefficients and clustering analysis as intermediary steps between raw time-series data and abnormality diagnosis. These intermediaries transform the raw data into meaningful patterns and relationships, making the diagnostic process both simpler to operate and more accurate in identifying equipment abnormality status.
Data Source
AI summary
Provided are an equipment abnormality detection device and equipment abnormality diagnostic system including a data collection device configured to collect operating status data from a plurality of sensors attached to equipment, and an equipment abnormality detection device configured to diagnose whether the equipment is abnormal based on the operating status data, wherein the equipment abnormality detection device is configured to select the equipment status from the collected operating status data, calculate similarity between the sensors from filtered sensor data of the selected equipment status, implement a sensor network on the basis of the similarity between the sensors, compare the sensor network of a first equipment status with the sensor network of a second equipment status to compare connection statuses for each sensor node, and check an influence of a sensor node having a maximum connection status difference value as a comparison result to diagnose an abnormality status of the equipment.