18681193. Method and Apparatus for Training a Model simplified abstract (Siemens AKtiengesellschaft)
Contents
Method and Apparatus for Training a Model
Organization Name
Inventor(s)
Method and Apparatus for Training a Model - A simplified explanation of the abstract
This abstract first appeared for US patent application 18681193 titled 'Method and Apparatus for Training a Model
The abstract of this patent application describes a method for training a model to monitor the working status of equipment based on sensor data.
- Training the model using historical sensor data gathered only under normal working conditions.
- Testing the model with sensor data causing a false alarm, historical confirmed failure data, and recent sensor data from normal working conditions.
- Activating the model if it passes the test, otherwise rejecting it.
Potential Applications: - Predictive maintenance in industrial equipment. - Monitoring systems in smart buildings. - Fault detection in automotive systems.
Problems Solved: - Early detection of equipment failures. - Reduction of false alarms. - Improved maintenance scheduling.
Benefits: - Increased equipment reliability. - Cost savings through preventative maintenance. - Enhanced safety in operational environments.
Commercial Applications: Title: Predictive Maintenance System for Industrial Equipment This technology can be utilized in various industries such as manufacturing, energy, and transportation to optimize equipment performance and minimize downtime.
Questions about the technology: 1. How does this method improve upon traditional equipment monitoring systems?
- This method enhances accuracy by training the model with specific data sets and testing it with various scenarios.
2. What are the potential cost savings associated with implementing this technology?
- By enabling predictive maintenance, companies can avoid costly equipment failures and reduce downtime, leading to significant cost savings.
Original Abstract Submitted
Various embodiments of the teachings herein include a method for training a model configured to monitor a working status of equipment based on sensor data. An example method includes: training a model using a training data set including historical sensor data gathered only when the equipment is under normal working conditions; testing the model with sensor data causing a false alarm, sensor data of the equipment's historical confirmed failure, and sensor data within pre-defined recent time period when the equipment is under normal working conditions; and activating the model if the model passes test, otherwise rejecting the model.
