Tampering Detection for Energy Production Systems
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Solution Overview
Problem
Current technologies for protecting energy production systems, such as LENR devices, lack automation in differentiating between tampering and normal operational errors, relying on human interpretation of scientific measurements, which is inefficient and prone to invasive physical attacks.
Innovation Solution
A tampering detection system comprising sensors and a control system that assesses conditions through multiple orders of data comparison to determine if a detected anomaly is a tampering event or a normal operational error, triggering appropriate countermeasures or maintenance actions, utilizing a logic flow that employs machine learning and deep neural networks to refine decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If electro-optical cameras and human interpretation are used to assess conditions, then measurement capability is provided, but automation is lacking and response efficiency is reduced
Solution Approach 1:
The control system automatically performs condition assessment, threat differentiation, and countermeasure selection without human intervention. The system self-evaluates sensor data, compares conditions against thresholds, and autonomously determines appropriate responses, eliminating the need for human interpretation while maintaining measurement precision.
Solution Approach 2:
The patent replaces the mechanical human interpretation process with an automated control system that uses sensor data, threshold comparisons, and predefined logic to assess conditions and determine threats. This substitution enables continuous automated monitoring and faster response times while preserving the measurement capabilities of electro-optical cameras and other sensors.
2Extent of automation
If multiple sensors and automated analysis are implemented, then automation and response time are improved, but system complexity increases
Solution Approach 1:
The control system processes sensor data through segmented analytical stages: first evaluating individual sensor readings against specific thresholds, then synthesizing multiple data sources to assess overall conditions, and finally determining appropriate countermeasures. This segmentation of the analysis process manages complexity by breaking down the automated decision-making into discrete, manageable evaluation steps.
Solution Approach 2:
The control system serves multiple functions within a single integrated platform: it collects data from various sensors, performs threshold comparisons, differentiates between normal variations and threats, selects appropriate countermeasures, and coordinates system responses. This multi-functionality consolidates what would otherwise require separate systems, managing overall complexity while enabling comprehensive automated threat detection.
3Productivity
If threshold-based automated differentiation is used, then response efficiency is improved, but false differentiation between tampering and normal errors may occur
Solution Approach 1:
The control system continuously monitors sensor data and adjusts its assessment based on ongoing condition evaluation. When conditions approach or exceed thresholds, the system evaluates additional parameters and considers the context of multiple readings before determining whether to trigger countermeasures. This feedback mechanism reduces false differentiation by requiring consistent threshold violations across multiple evaluation points rather than reacting to single isolated readings.
Solution Approach 2:
The system employs multiple thresholds and evaluation parameters to differentiate between normal operational variations and actual threats. By monitoring conditions across multiple parameters and requiring significant deviations from normal ranges before triggering responses, the system maintains high response efficiency while reducing false positives. The control system can adjust its sensitivity based on operational context, changing evaluation parameters to match expected normal variations under different operating conditions.
Data Source
AI summary
A system includes an energy production device, a tampering detection system comprising a plurality of sensors, and a control system in communication with the plurality of sensors. The control system is configured to detect a candidate for a tampering event, determine if the candidate is a tampering or a normal operational error by comparing a first order condition to a predetermined threshold, and, when the predetermined threshold is not met, comparing the candidate to a subsequent order condition until the threshold is met, and direct an undertaking of a countermeasure when the candidate is a determined tampering or undertaking a maintenance when the candidate is a determined normal operational error.