HL MANDO CORPORATION (20240354587). METHOD FOR PREDICTING REMAINING LIFE OF INDUSTRIAL FACILITY USING GENERATIVE ADVERSARIAL NETWORK, AND APPARATUS THEREOF simplified abstract
METHOD FOR PREDICTING REMAINING LIFE OF INDUSTRIAL FACILITY USING GENERATIVE ADVERSARIAL NETWORK, AND APPARATUS THEREOF
Organization Name
Inventor(s)
Gyuwon Kim of Gyeonggi-do (KR)
METHOD FOR PREDICTING REMAINING LIFE OF INDUSTRIAL FACILITY USING GENERATIVE ADVERSARIAL NETWORK, AND APPARATUS THEREOF - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240354587 titled 'METHOD FOR PREDICTING REMAINING LIFE OF INDUSTRIAL FACILITY USING GENERATIVE ADVERSARIAL NETWORK, AND APPARATUS THEREOF
The present disclosure describes a method using a generative adversarial network to predict the remaining life of an industrial facility.
- Acquiring normal process data from industrial facilities
- Preprocessing and generating the data for learning
- Training the generative adversarial network with the learning data
- Inputting data from the facility into the network to predict remaining life
- Potential Applications:**
- Predictive maintenance in industrial settings - Optimization of facility operations - Resource allocation based on predicted remaining life
- Problems Solved:**
- Uncertainty in predicting the lifespan of industrial facilities - Lack of efficient maintenance scheduling - Inaccurate resource allocation
- Benefits:**
- Cost savings through optimized maintenance - Increased operational efficiency - Enhanced safety in industrial environments
- Commercial Applications:**
Predictive maintenance software for industrial facilities to improve efficiency and reduce downtime.
- Questions about the Technology:**
1. How does the generative adversarial network improve the accuracy of predicting remaining life? 2. What are the key challenges in implementing this technology in real-world industrial settings?
Original Abstract Submitted
the present disclosure provides a method performed by a facility control device to predict a remaining life of an industrial facility using a generate adversarial network, and the method includes acquiring normal process data from at least one or more industrial facilities, preprocessing and generating the normal process data and discrete data as learning data, learning the generative adversarial network based on the preprocessed learning data, and inputting the data obtained from the industrial facility into the pre-learned generative adversarial network and predicting the remaining life of the industrial facility based on output data output from the generative adversarial network. moreover, the present disclosure provides an apparatus for predicting a remaining life of an industrial facility using the generative adversarial network.
