Modular Sensor Fusion Platform for Hydrocarbon Monitoring
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Solution Overview
Problem
Conventional surveillance systems for monitoring hydrocarbon extraction, storage, and transfer equipment are ineffective in harsh environments and lack multi-modal imaging capabilities, leading to degraded performance and limited detection capabilities.
Innovation Solution
A modular sensor fusion platform comprising a compute engine, gimbal, sensor suite, and imaging system, which uses multi-modal imaging and machine learning algorithms to monitor hydrocarbon storage equipment, detect leaks, and measure fill levels, while withstanding harsh environmental conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional surveillance cameras are used to monitor hydrocarbon equipment, then the system can provide basic visual monitoring, but the performance degrades in harsh environments due to extreme temperature, wind, dust, and precipitation
Solution Approach 1:
The patent combines multiple imaging modalities (visible spectrum camera, infrared camera, ultraviolet camera) and non-imaging sensors (gas detectors, temperature sensors, pressure sensors) into a single integrated sensor suite. This merging allows the system to overcome the limitations of individual sensors in harsh environments by providing redundant and complementary detection capabilities that remain reliable under various environmental conditions.
Solution Approach 2:
The sensor suite is designed to perform multiple functions simultaneously: visual monitoring, thermal detection, gas leak detection, temperature monitoring, and pressure monitoring. This multi-functionality ensures that the system can reliably monitor hydrocarbon equipment across diverse environmental conditions without requiring separate specialized systems for each function.
2Measurement precision
If conventional single-modality surveillance cameras are used, then the system structure remains simple, but the detection capabilities are limited due to lack of multi-modal imaging
Solution Approach 1:
The patent merges multiple imaging modalities and sensor types into a unified sensor suite that captures data across visible, infrared, and ultraviolet spectrums simultaneously. This combination enhances detection precision by providing complementary information from different wavelengths and sensor types, while the integrated design manages the inherent complexity through unified processing.
Solution Approach 2:
The compute engine serves as an intermediary that receives data from multiple sensor modalities, processes the information, and integrates the results into coherent monitoring outputs. This intermediary processing layer manages the complexity of multi-modal data by coordinating the different sensor inputs and producing unified detection results.
3Adaptability or versatility
If traditional hardwired imaging modalities are used, then the system structure remains fixed, but the ability to swap or add imaging modalities is inhibited
Solution Approach 1:
The patent segments the monitoring system into modular components: a gimbal mechanism that can independently position different sensor modules, and a sensor suite with detachable imaging modalities. This segmentation allows individual sensors or modalities to be swapped or upgraded without affecting the entire system, enhancing adaptability while managing complexity through standardized interfaces.
Solution Approach 2:
The system incorporates a gimbal with dynamic positioning capabilities that can adjust the orientation and positioning of different sensor modalities in real-time. This dynamic configuration allows the system to adapt to different monitoring requirements and enables flexible swapping of imaging modalities while maintaining operational capability.
Data Source
AI summary
Various embodiments of the present technology relate to solutions for hydrocarbon equipment monitoring. In some examples, a detection system comprises a compute engine, a gimbal, a sensor suite, and an imaging system. The compute engine generates signaling that directs the gimbal to orient the imaging system, signaling that directs the imaging system to image the equipment, and signaling that directs the sensor suite to sense the equipment. The gimbal orients the imaging system based on the signaling. The imaging system images the equipment and transfers images depicting the storage equipment to the compute engine. The sensor suite senses the equipment and transfers sensor data that characterizes the equipment to the compute engine. The compute engine processes the data with a machine learning engine trained to determine the status of the equipment. The compute engine transfers a machine learning output that indicates the status to a downstream system.