Surface Sensor Anomaly Detection for Underwater Machinery
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Solution Overview
Problem
Underwater machinery faces challenges such as pressure, temperature, and vibration issues due to its operating environment, leading to potential mechanical and electrical failures, which can be difficult to detect and address in a timely manner.
Innovation Solution
A method using surface sensors to perform audio-visual inspections by feeding wave movements, bubble formation patterns, and acoustic information into a neural network to identify anomalies in machinery performance, allowing for proactive corrective actions to be determined and executed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If surface sensors are used to monitor underwater machinery, then the complexity of underwater inspection is reduced, but the precision of anomaly detection may be compromised due to the indirect nature of surface observations
Solution Approach 1:
The patent uses surface waves and bubbles as intermediary indicators to detect underwater machinery anomalies. Instead of directly measuring machinery parameters underwater, the system observes the effects (waves and bubbles) that propagate to the surface, thereby simplifying the inspection system while maintaining detection capability through neural network analysis of these surface phenomena
Solution Approach 2:
The patent replaces direct mechanical/physical contact inspection systems with acoustic and visual sensing from the surface. By using acoustic information and visual observation of surface waves and bubbles, the system eliminates the need for complex underwater mechanical sensors while achieving anomaly detection through pattern recognition
2Reliability
If traditional underwater inspection methods are used, then direct access to machinery is achieved, but the time required for detection and response is increased
Solution Approach 1:
The patent implements continuous surface monitoring that detects anomalies before they escalate into catastrophic failures. By continuously analyzing surface waves and bubbles using neural networks, the system performs preliminary detection of machinery issues, enabling early intervention and reducing the time from anomaly occurrence to corrective action
Solution Approach 2:
The system establishes a feedback loop where surface sensor data is continuously analyzed by neural networks to detect anomalies, which then triggers alerts for timely corrective action. This real-time feedback mechanism reduces detection and response time by immediately communicating machinery status changes to operators
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables the detection of anomalies in underwater machinery performance through surface sensors, facilitating timely corrective actions to prevent catastrophic damage and ensuring the continued operation of underwater equipment.
Implementation Method 1
feeding underwater acoustic information to the neural network
Data Source
AI summary
A system, method, and computer program product perform audio-visual inspection at a surface of a liquid over machinery that is operating under the surface. The audio-visual inspection includes each of feeding surface wave movements into a neural network, feeding bubble formation pattern into the neural network, feeding bubble dimensions into the neural network, and feeding underwater acoustic information to the neural network. The system, method, and computer program product further identify, using the neural network, a statistical anomaly from the audio-visual inspection indicating an anomaly of the performance of the machinery.