International business machines corporation (20240119580). UNDERWATER MACHINERY PERFORMANCE ANALYSIS USING SURFACE SENSORS simplified abstract
Contents
- 1 UNDERWATER MACHINERY PERFORMANCE ANALYSIS USING SURFACE SENSORS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 UNDERWATER MACHINERY PERFORMANCE ANALYSIS USING SURFACE SENSORS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 How does the neural network differentiate between normal and anomalous patterns in the audio-visual inspection data?
- 1.11 What are the specific types of anomalies that the neural network is trained to detect in the machinery's performance?
- 1.12 Original Abstract Submitted
UNDERWATER MACHINERY PERFORMANCE ANALYSIS USING SURFACE SENSORS
Organization Name
international business machines corporation
Inventor(s)
Sudheesh S. Kairali of Kozhikode (IN)
Sarbajit K. Rakshit of Kolkata (IN)
UNDERWATER MACHINERY PERFORMANCE ANALYSIS USING SURFACE SENSORS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240119580 titled 'UNDERWATER MACHINERY PERFORMANCE ANALYSIS USING SURFACE SENSORS
Simplified Explanation
The patent application describes a system, method, and computer program product for audio-visual inspection of a liquid surface over operating machinery, using a neural network to identify anomalies in the machinery's performance.
- Feeding surface wave movements into a neural network
- Feeding bubble formation pattern into the neural network
- Feeding bubble dimensions into the neural network
- Feeding underwater acoustic information to the neural network
Potential Applications
This technology could be applied in industries such as manufacturing, oil and gas, and marine exploration for monitoring and detecting anomalies in machinery operating under liquid surfaces.
Problems Solved
This technology helps in early detection of performance anomalies in machinery operating under liquid surfaces, which can prevent costly breakdowns and improve overall operational efficiency.
Benefits
The benefits of this technology include improved maintenance planning, reduced downtime, increased safety, and enhanced operational performance of machinery operating under liquid surfaces.
Potential Commercial Applications
The potential commercial applications of this technology include underwater equipment monitoring systems, industrial machinery inspection tools, and marine vessel maintenance systems.
Possible Prior Art
One possible prior art could be underwater inspection systems using cameras and sensors to monitor machinery performance in liquid environments.
Unanswered Questions
How does the neural network differentiate between normal and anomalous patterns in the audio-visual inspection data?
The neural network likely uses pattern recognition algorithms to compare the incoming data with pre-defined normal operating conditions to identify statistical anomalies.
What are the specific types of anomalies that the neural network is trained to detect in the machinery's performance?
The neural network may be trained to detect anomalies such as abnormal bubble formations, irregular surface wave movements, unexpected changes in bubble dimensions, and unusual underwater acoustic patterns.
Original Abstract Submitted
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.
