Preprocessor for Abnormality Sign Diagnosis Using Sensor Data Correlation
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Solution Overview
Problem
Existing abnormality detection systems in equipment, such as those described in JP-2013-8111-A and JP-2014-238852-A, face limitations in early detection sensitivity and flexibility, with sensor data being less sensitive to some abnormalities and requiring pre-formatted correlation functions for effective monitoring.
Innovation Solution
A preprocessor that calculates and combines multi-dimensional sensor data with correlation coefficients and standard deviations, enhancing the ability of abnormality sign diagnosing devices to detect equipment abnormalities by producing new variables that reflect equipment states differently than traditional sensor data alone.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If sensor data is used to monitor operational state, then flexibility and application range are improved, but sensitivity to detect abnormalities is insufficient
Solution Approach 1:
The patent combines sensor data with correlation coefficients and standard deviations into an enhanced feature set. The preprocessor calculates correlation coefficients between sensor pairs and standard deviations of sensor values, then merges these with original sensor data to create enriched feature vectors that are fed to the abnormality sign diagnosing device, thereby improving detection sensitivity while maintaining flexibility
Solution Approach 2:
The patent transitions from using only raw sensor data to incorporating additional derived features (correlation coefficients and standard deviations) as new dimensions. This dimensional expansion allows the system to capture relationships and variations in sensor data that were not previously detectable, enhancing sensitivity without sacrificing the adaptability of the system
2Measurement precision
If correlation functions are pre-formatted to monitor changes, then abnormality detection capability is improved, but flexibility is reduced
Solution Approach 1:
The preprocessor performs preliminary calculations of correlation coefficients and standard deviations from raw sensor data before the abnormality detection stage. These pre-computed statistical features are then made available to the diagnosing device, enabling it to detect abnormalities based on deviations in these pre-processed features without requiring pre-formatted correlation functions, thus maintaining flexibility while improving detection capability
Solution Approach 2:
The system uses the sensor data itself to automatically generate the correlation coefficients and standard deviations through computational processing. This self-service approach eliminates the need for manual pre-formatting of correlation functions, as the system autonomously extracts and computes the necessary statistical relationships from the incoming sensor data, thereby preserving flexibility
Data Source
AI summary
A preprocessor includes a sensor data storage part that is connected to an abnormality sign diagnosing device and stores multi-dimensional sensor data, a data analysis processing part that calculates a variable value by using the multi-dimensional sensor data stored in the sensor data storage part, an analysis data storage part that stores the variable value calculated by the data analysis processing part, and an analysis data addition processing part that combines the multi-dimensional sensor data stored at the sensor data storage part and the variable value stored in the analysis data storage part and outputs a combined result to the abnormality sign diagnosing device.