GAN-Based Remaining Life Prediction for Industrial Facilities
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Solution Overview
Problem
Current methods for predicting the remaining life of industrial facilities are inefficient and do not effectively utilize deep learning algorithms, particularly in accurately valuing and maintaining tangible assets by not accounting for all possible occurrences, leading to suboptimal depreciation calculations.
Innovation Solution
A method and apparatus using a generative adversarial network to predict the remaining life of industrial facilities by preprocessing normal process data and discrete data, learning the network, and inputting facility data to generate output for life prediction, including inspection and maintenance signal transmission based on the predicted life.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional accounting depreciation methods are used to value industrial facilities, then the valuation system is simple to implement, but it cannot effectively measure all possible occurrences and leads to inaccurate remaining life prediction
Solution Approach 1:
The patent replaces traditional accounting depreciation methods with a deep learning-based generative adversarial network (GAN) system. The GAN learns from normal process data to generate expected facility behavior patterns, enabling accurate remaining life prediction by comparing actual vs. predicted data. This substitution transitions from simple accounting rules to an intelligent system that captures complex facility degradation patterns.
Solution Approach 2:
The patent transforms the valuation approach by changing from static accounting parameters to dynamic learned parameters. The GAN model learns optimal parameters for predicting facility state from historical data, allowing the system to adapt to specific facility characteristics and operational conditions, thereby improving prediction accuracy while maintaining practical implementability.
2Reliability
If deep learning algorithms are not utilized in facility valuation, then the system is easier to implement, but it cannot accurately predict remaining life and account for all possible occurrences
Solution Approach 1:
The patent replaces traditional mechanical accounting systems with a GAN-based deep learning system. The generator creates realistic facility operation patterns while the discriminator evaluates anomalies, providing reliable remaining life predictions. This substitution enables the system to account for complex, non-linear degradation patterns that traditional methods cannot capture.
Solution Approach 2:
The GAN generator creates synthetic copies of normal facility operation data to establish baseline patterns. By comparing actual facility data against these generated copies, the system reliably identifies deviations indicating degradation. This copying mechanism enables robust prediction without requiring extensive labeled training data.
3Measurement precision
If continuous monitoring and deep learning analysis are implemented for remaining life prediction, then prediction accuracy improves, but computational resources and processing time increase
Solution Approach 1:
The patent performs preliminary action by pre-training the GAN model offline using historical facility data. Once trained, the generator and discriminator are deployed as a lightweight inference system that requires minimal computational resources during actual operation. This separates the computationally intensive learning phase from the low-resource prediction phase, reducing ongoing energy consumption.
Solution Approach 2:
The GAN generator creates synthetic normal operation patterns that serve as reference baselines. During monitoring, the system only needs to compare actual data against these pre-generated copies rather than performing complex real-time analysis, significantly reducing computational energy requirements while maintaining high prediction precision.
Data Source
AI summary
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.