Sensor Data Resampling for Irregular Sampling Rates
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Solution Overview
Problem
Developing and deploying plant-wide control and condition monitoring analytics for complex industrial systems with disparate sets of data from sensors operating at irregular intervals or different sampling rates is challenging due to the difficulty in resampling and synchronizing these data sets.
Innovation Solution
The system resamples and synchronizes sensor measurements by identifying common sampling periods, interpolating missing data, and applying filters to ensure statistically significant data alignment, allowing for the use of composite filters and logical combinations of sensor data for effective analytics development and validation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensors sample measurements at different rates and irregular intervals to capture diverse dynamics, then measurement coverage and responsiveness are improved, but data synchronization and analysis complexity increase
Solution Approach 1:
The patent introduces a data synchronization system that acts as an intermediary between sensors with different sampling rates and the analytics platform. This system resamples irregular sensor data to a common time grid, enabling seamless integration without requiring changes to the original sensors or analytics algorithms.
Solution Approach 2:
The system dynamically changes the sampling rate parameter of sensor data through resampling. By transforming data from irregular intervals to a standardized time grid, the system maintains the original measurement precision while enabling consistent analysis across all sensors.
2Adaptability or versatility
If data from multiple sensors are integrated for plant-wide analytics, then system-wide monitoring capability is improved, but computational complexity and data processing requirements increase
Solution Approach 1:
The patent segments the data integration process into distinct stages: individual sensor resampling to common time grid, validation of resampled data quality, and then integration for analytics. This segmentation reduces computational complexity by handling each sensor independently before combination.
Solution Approach 2:
The system performs preliminary resampling and validation of sensor data before integration into plant-wide analytics. By pre-processing data to a standardized format and validating quality metrics in advance, the system reduces the computational burden during actual analytics execution.
3Reliability
If resampling is applied to align sensor data to common time intervals, then data synchronization is improved, but potential loss of original data characteristics and increased processing time occur
Solution Approach 1:
The patent applies resampling selectively based on data validation results. Instead of resampling all sensor data uniformly, the system identifies and processes only the portions of data that require synchronization, preserving original data characteristics for sensors that already align with the common time grid.
Data Source
AI summary
A system and method include receiving a set of sampled measurements for each of multiple sensors, wherein the sampled measurements are at irregular intervals or different rates, re-sampling the sampled measurements of each of the multiple sensors at a higher rate than one of the sensor's set of sampled measurements, and synchronizing the sampled measurements of each of the multiple sensors.