17957574. SYSTEM AND METHOD FOR PREDICTION ANALYSIS OF A SYSTEM UTILIZING MACHINE LEARNING NETWORKS simplified abstract (Robert Bosch GmbH)
Contents
- 1 SYSTEM AND METHOD FOR PREDICTION ANALYSIS OF A SYSTEM UTILIZING MACHINE LEARNING NETWORKS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SYSTEM AND METHOD FOR PREDICTION ANALYSIS OF A SYSTEM UTILIZING MACHINE LEARNING NETWORKS - 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 this technology handle real-time signal analysis?
- 1.11 What types of test devices are most suitable for this technology?
- 1.12 Original Abstract Submitted
SYSTEM AND METHOD FOR PREDICTION ANALYSIS OF A SYSTEM UTILIZING MACHINE LEARNING NETWORKS
Organization Name
Inventor(s)
Ivan Batalov of Pittsburgh PA (US)
Filipe J. Cabrita Condessa of Pittsburgh PA (US)
SYSTEM AND METHOD FOR PREDICTION ANALYSIS OF A SYSTEM UTILIZING MACHINE LEARNING NETWORKS - A simplified explanation of the abstract
This abstract first appeared for US patent application 17957574 titled 'SYSTEM AND METHOD FOR PREDICTION ANALYSIS OF A SYSTEM UTILIZING MACHINE LEARNING NETWORKS
Simplified Explanation
The abstract describes a computer-implemented method for analyzing signals from a test device using machine learning to predict signal characteristics and identify prediction errors.
- The method involves receiving recorded signals indicating current, voltage, vibrational, and sound information associated with a test device.
- A training data set is generated using the signals and sent to a machine learning model.
- Upon meeting a convergence threshold, a trained model is outputted that predicts signal characteristics using the recorded signals.
- The prediction is compared to the actual signal to identify prediction errors associated with the device.
- A prediction analysis is outputted, indicating information related to the prediction error and the relationship between the device and its signals.
Potential Applications
This technology could be applied in various industries such as manufacturing, automotive, and electronics for predictive maintenance, quality control, and fault detection.
Problems Solved
This technology helps in early detection of issues in test devices, improves predictive maintenance, and enhances overall device performance and reliability.
Benefits
The benefits of this technology include increased efficiency in maintenance processes, reduced downtime, improved product quality, and cost savings through proactive device monitoring.
Potential Commercial Applications
The technology can be utilized in industries such as manufacturing, automotive, and electronics for developing smart monitoring systems, predictive maintenance solutions, and quality control tools.
Possible Prior Art
One possible prior art could be the use of machine learning algorithms for predictive maintenance in industrial equipment, but the specific combination of signals and prediction analysis as described in this patent application may be novel.
Unanswered Questions
How does this technology handle real-time signal analysis?
The abstract does not mention the real-time processing capabilities of the method. It would be interesting to know if the technology can analyze signals in real-time for immediate feedback and decision-making.
What types of test devices are most suitable for this technology?
The abstract does not specify the types of test devices that can benefit the most from this technology. Understanding the ideal applications and industries for this method would provide valuable insights for potential users.
Original Abstract Submitted
A computer-implemented method includes receiving a combination recorded signals indicating current, voltage, vibrational, and sound information associated with a test device, generating a training data set utilizing the signals, wherein the training data set is sent to a machine learning model, and in response to meeting a convergence threshold of the machine learning model, outputting a trained model that outputs a prediction using the recorded signals from the combination. The prediction indicates a predicted signal characteristic. The method also includes comparing the prediction and signal associated with the test device to identify a prediction error associated with the device, and outputting a prediction analysis indicating information associated with at least the prediction error. The prediction analysis includes information indicative of a relationship between the device and its signals.
