GAN-Based Equipment Damage Prediction Using Neural Networks
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Solution Overview
Problem
Existing image analysis systems for equipment damage prediction are subjective and prone to error, requiring extensive information about materials, environmental, and operating conditions, which may not be available for automated analysis.
Innovation Solution
A deep generative adversarial network (GAN) system with a generator sub-network that creates images of potential damage based on actual damage images and a discriminator sub-network that determines the likelihood of damage progression, using a large image dataset without annotation to predict equipment damage progression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image analysis systems are used to identify damage, then damage detection can be performed, but the characterization of damage is highly subjective and prone to error
Solution Approach 1:
The patent replaces traditional mechanical/image analysis systems with a neural network-based system that automatically processes images of damage. The neural network learns from training data to objectively characterize damage features, eliminating subjective human interpretation and improving both measurement precision and reliability of damage assessment.
Solution Approach 2:
The system creates virtual copies of damage by generating synthetic training images that simulate various damage conditions. These synthesized images are used to train the neural network, enabling it to learn damage patterns without requiring extensive real damaged equipment images, thereby improving characterization accuracy.
2Measurement precision
If extensive information about materials, environmental conditions, and operating conditions is collected, then accurate damage prediction can be achieved, but the system complexity and data requirements increase significantly
Solution Approach 1:
The patent extracts and focuses only on the essential visual features of damage from images, eliminating the need to collect and process extensive additional data about materials, environmental conditions, and operating conditions. The neural network learns to predict damage progression based primarily on visual image data, significantly reducing system complexity while maintaining prediction accuracy.
Solution Approach 2:
The neural network is designed to handle multiple aspects of damage analysis (detection, characterization, and progression prediction) using a single unified model that processes images directly. This multi-functional approach eliminates the need for separate systems for each analysis task and reduces overall system complexity.
3Measurement precision
If more information about equipment conditions is required for accurate damage analysis, then prediction accuracy improves, but the availability of required data decreases
Solution Approach 1:
The system performs preliminary training of the neural network using synthesized damage images that pre-encode various damage scenarios and progression patterns. This preliminary action enables the network to make accurate predictions without requiring extensive real-time data about materials, environmental conditions, and operating conditions during actual operation.
Data Source
AI summary
A generative adversarial network (GAN) system includes a generator sub-network configured to examine one or more images of actual damage to equipment. The generator sub-network also is configured to create one or more images of potential damage based on the one or more images of actual damage that were examined. The GAN system also includes a discriminator sub-network configured to examine the one or more images of potential damage to determine whether the one or more images of potential damage represent progression of the actual damage to the equipment.