17957570. SYSTEM AND METHOD FOR DEEP LEARNING-BASED SOUND PREDICTION USING ACCELEROMETER DATA simplified abstract (Robert Bosch GmbH)
Contents
- 1 SYSTEM AND METHOD FOR DEEP LEARNING-BASED SOUND PREDICTION USING ACCELEROMETER DATA
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SYSTEM AND METHOD FOR DEEP LEARNING-BASED SOUND PREDICTION USING ACCELEROMETER DATA - 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 Unanswered Questions
- 1.11 Original Abstract Submitted
SYSTEM AND METHOD FOR DEEP LEARNING-BASED SOUND PREDICTION USING ACCELEROMETER DATA
Organization Name
Inventor(s)
Ivan Batalov of Pittsburgh PA (US)
Andreas Henke of Diemelstadt (DE)
Bernhard De Graaff of Karlsruhe (DE)
Florian Rieger of Karlsruhe (DE)
Filipe J. Cabrita Condessa of Pittsburgh PA (US)
Andreas Kockler of Gaggenau (DE)
SYSTEM AND METHOD FOR DEEP LEARNING-BASED SOUND PREDICTION USING ACCELEROMETER DATA - A simplified explanation of the abstract
This abstract first appeared for US patent application 17957570 titled 'SYSTEM AND METHOD FOR DEEP LEARNING-BASED SOUND PREDICTION USING ACCELEROMETER DATA
Simplified Explanation
The system described in the patent application utilizes a processor to receive real-time data from sensors connected to a run-time device, such as an actuator or electric drive. This data includes information on current, voltage, and vibrations. The processor then uses a trained machine learning model to analyze the data and predict the sound emitted by the run-time device.
- Processor communicates with sensors to receive real-time data
- Data includes current, voltage, and vibrational information
- Trained machine learning model used to predict sound emitted by run-time device
Potential Applications
The technology could be applied in various industries where monitoring and predicting the sound emitted by machinery is crucial, such as manufacturing, automotive, and aerospace.
Problems Solved
This technology helps in predicting and monitoring the sound emitted by run-time devices, which can aid in maintenance scheduling, identifying potential issues, and improving overall operational efficiency.
Benefits
The system allows for proactive maintenance, reduces downtime, improves safety by detecting potential malfunctions early, and enhances overall equipment performance.
Potential Commercial Applications
- Predictive maintenance solutions for industrial machinery
- Monitoring systems for electric drives in automotive applications
Possible Prior Art
One possible prior art could be existing predictive maintenance systems that utilize machine learning models to analyze various data inputs to predict equipment failures.
Unanswered Questions
How accurate are the sound predictions generated by the system?
The level of accuracy of the sound predictions is not specified in the abstract. Further details on the precision of the predictions would be beneficial for potential users.
What is the training process for the machine learning model?
The abstract does not mention the specifics of how the machine learning model is trained. Understanding the training process could provide insights into the reliability of the predictions generated by the system.
Original Abstract Submitted
A system includes a processor in communication with one or more sensors, wherein the processor is programmed to receive data including one or more of real-time current information, real-time voltage information, or real-time vibrational information from a run-time device, wherein the run-time device is an actuator or electric dive, and utilize a trained machine learning model and the data as an input to the trained machine learning model, output a sound prediction associated with estimated sound emitted from the run-time device.
