Part Wear Assessment for Optimal Replacement Timing
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Solution Overview
Problem
Current methods for predicting the optimal time for part replacement in machine maintenance are inaccurate and do not effectively consider the economic tradeoff between replacement costs and continued operation with worn parts, as they rely on conventional image recognition models and historical data without accounting for varying operating conditions.
Innovation Solution
A method and system that train a wear estimate model using physics-based wear patterns and machine learning to predict wear severity, combined with machine utilization patterns and financial modeling to determine the optimal time for part replacement based on cost analysis, utilizing a neural network and telemetry data to provide accurate and cost-effective replacement timing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image recognition models and historical data are used to predict part replacement timing, then the system is simple to implement, but the accuracy of wear assessment and replacement timing prediction is insufficient
Solution Approach 1:
The patent transforms physical wear parameters into image data parameters through photography, and then into quantitative wear estimates through neural network processing. This parameter transformation chain (physical wear → visual appearance → digital image → quantitative estimate) enables accurate wear assessment without complex physical measurement devices.
Solution Approach 2:
The patent replaces complex physical measurement systems with an optical-mechanical-photographic system combined with neural network processing. Instead of using sophisticated sensors and measurement equipment, the system uses a camera to capture images and a neural network to analyze them, achieving accurate wear assessment through information processing rather than direct physical measurement.
2Measurement precision
If physical measurements are used to estimate wear degree, then measurement accuracy is improved, but the process becomes time-consuming
Solution Approach 1:
The patent creates a visual copy (photograph) of the worn part and processes this copy through a neural network to obtain wear information. This copying approach allows wear assessment without direct physical contact or complex measurement procedures, significantly reducing measurement time while maintaining accuracy through automated image analysis.
Solution Approach 2:
The patent replaces time-consuming physical measurement processes with automated optical capture and digital image analysis. The neural network processes images rapidly to provide wear estimates, eliminating the need for manual measurement operations and achieving both speed and accuracy.
3Loss of energy
If part replacement is delayed to reduce replacement costs, then operational costs decrease, but machine performance degradation and productivity loss increase
Solution Approach 1:
The patent implements a feedback mechanism where wear is continuously monitored through periodic photography and neural network analysis. This feedback loop provides real-time information about wear progression, enabling dynamic adjustment of replacement timing to optimize the balance between operational costs and productivity, replacing parts at the economically optimal moment rather than using fixed schedules.
Solution Approach 2:
The patent transitions from static, fixed-interval replacement schedules to dynamic, condition-based replacement timing. By continuously monitoring wear through image analysis and considering varying operating conditions, the system determines the optimal replacement moment that adapts to actual machine state and operational context, optimizing both cost and productivity.
4Reliability
If newer parts are used to maintain performance, then machine performance is improved, but replacement frequency and total cost increase
Solution Approach 1:
The patent applies partial replacement action by replacing only when economically necessary rather than preventively. The neural network provides wear estimates that indicate when performance degradation becomes significant enough to warrant replacement, allowing the system to operate with worn parts as long as performance requirements are met, thereby reducing unnecessary replacement costs while maintaining adequate performance.
Data Source
AI summary
A method for part replacement timing optimization. The method includes training a wear estimate model. Training the model includes predicting a plurality of wear patterns for a part, each wear pattern corresponding to a degree of severity. Training images are rendered for each wear pattern. Each of the training images is labeled with the corresponding degree of severity. A neural network is then trained with the labeled training images. An image of a deployed part associated with a machine is received and fed into the trained wear estimate model. The method further includes receiving a wear estimate for the part image from the trained wear estimate model, estimating a change in performance of the machine based on the wear estimate, and determining a machine utilization pattern for the machine. The machine utilization pattern and the change in performance estimate are combined to determine an optimal time to replace the part.