Blast Sensor Data Cleaning for Incident Overpressure Estimation
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Solution Overview
Problem
Existing blast exposure sensors struggle to measure incident overpressure directly and synchronize data due to clock drift, requiring manual analysis by trained professionals, which is cumbersome and not scalable for large volumes of data.
Innovation Solution
An automated method and system for analyzing blast exposure data that identifies and removes spurious features, applies filters and baseline shifts, groups data based on waveform features, and estimates incident overpressure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis by trained professionals is used, then measurement precision can be maintained, but productivity is low and the process is not scalable
Solution Approach 1:
The system performs self-service by automatically detecting and removing spurious data features, applying filters and baseline shifts, and estimating blast parameters without requiring trained professionals. The automated algorithm processes data independently, maintaining precision while dramatically increasing productivity and scalability.
Solution Approach 2:
The patent replaces the mechanical manual analysis process with an automated computational system. Instead of human experts manually reviewing data, the system uses algorithmic processing to automatically analyze blast exposure data, achieving both high precision and high productivity.
2Measurement precision
If multiple body-mounted blast sensors are used, then measurement coverage is improved, but device complexity increases due to clock drift synchronization issues
Solution Approach 1:
The system extracts and removes spurious data features automatically, separating valid blast data from corrupted or misaligned data. This extraction process handles the complexity of multiple sensors with clock drift by identifying and isolating problematic data points, maintaining measurement precision without requiring complex manual synchronization.
Solution Approach 2:
The automated analysis system acts as an intermediary between the raw sensor data and the final blast parameter estimates. It processes and reconciles data from multiple sensors with different clock rates, managing the synchronization complexity internally while providing accurate results.
3Device complexity
If spurious data features are present in the data, then device complexity remains low, but measurement precision deteriorates due to false readings
Solution Approach 1:
The system performs preliminary action by automatically detecting and removing spurious data features before the main analysis. It applies filters and baseline shifts in advance to clean the data, ensuring that only valid data is used for blast parameter estimation. This maintains processing simplicity while improving measurement precision.
Solution Approach 2:
The system converts the presence of spurious data features into a detectable pattern that can be automatically identified and removed. By using automated detection algorithms, the system turns what would be harmful false readings into identifiable anomalies that are systematically eliminated, improving precision without adding complexity.
Data Source
AI summary
A method, system, and computer-readable media for analyzing blast exposure data in which one or more spurious data features are identified, flagged, and removed from a set of pressure data received from a blast sensor. Pressure data sets are grouped based on waveform features to determine one or more incident overpressure parameters associated with a blast exposure event.