18153157. SYSTEMS AND METHODS FOR MONITORING AND TRAINING A MANUFACTURING SYSTEM simplified abstract (Ford Global Technologies, LLC)
Contents
SYSTEMS AND METHODS FOR MONITORING AND TRAINING A MANUFACTURING SYSTEM
Organization Name
Inventor(s)
Zhiyi Chen of Ann Arbor MI (US)
Harshal Maske of Farmington Hills MI (US)
Devesh Upadhyay of Canton MI (US)
Huanyi Shui of Ann Arbor MI (US)
Michael Brendan Hopka of Milford MI (US)
SYSTEMS AND METHODS FOR MONITORING AND TRAINING A MANUFACTURING SYSTEM - A simplified explanation of the abstract
This abstract first appeared for US patent application 18153157 titled 'SYSTEMS AND METHODS FOR MONITORING AND TRAINING A MANUFACTURING SYSTEM
The method described in the abstract involves using an autoencoder to generate operational indicators based on sensor data from manufacturing stations, aggregating these indicators using a linear propagator, and predicting operational characteristics using a neural network.
- Autoencoder generates operational indicators from sensor data at manufacturing stations
- Linear propagator aggregates operational indicators from multiple stations
- Neural network predicts operational characteristics based on aggregated indicators
- State of the manufacturing system is determined based on predicted operational characteristics
- Utilizes a combination of autoencoders, linear propagators, and neural networks for analysis
Potential Applications: - Quality control in manufacturing processes - Predictive maintenance in industrial settings - Optimization of production efficiency
Problems Solved: - Enhances decision-making in manufacturing operations - Improves overall equipment effectiveness - Enables proactive maintenance strategies
Benefits: - Increased productivity and efficiency - Reduced downtime and maintenance costs - Enhanced overall operational performance
Commercial Applications: Title: "Predictive Maintenance System for Manufacturing Operations" This technology can be applied in various industries such as automotive, electronics, and pharmaceuticals to streamline production processes, reduce costs, and improve product quality.
Questions about the technology: 1. How does this method improve predictive maintenance strategies in manufacturing? 2. What are the key advantages of using neural networks for predicting operational characteristics in industrial settings?
Original Abstract Submitted
A method includes generating, by a given autoencoder, a given operational indicator based on sensor data obtained from one or more sensors disposed at a given manufacturing station; selectively aggregating, by the given linear propagator and based on a linear mapping model, the given operational indicator and one or more additional operational indicators associated with one or more additional manufacturing stations to selectively generate an aggregated operational indicator; generating, by the given neural network and in response to generating the aggregated operational indicator, a predicted operational characteristic of the given manufacturing station based on the given operational indicator and the aggregated operational indicator; and determining a state of the manufacturing system based on the predicted operational characteristic and one or more additional predicted operational characteristics generated by one or more additional neural networks from among the plurality of neural networks.
