3D Wear Detection in Industrial Equipment Using Machine Learning
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Solution Overview
Problem
Current methods for detecting wear in industrial equipment are inefficient, relying on vibration analysis, clearance measurements, and visual inspections, which do not effectively quantify or predict wear patterns, leading to potential equipment failure and downtime.
Innovation Solution
A 3D scanning system that uses machine learning to analyze images from industrial equipment, identifying wear patterns such as pitting, spalling, and thermal stress, and recommends operational changes like load shedding or lubrication adjustments to minimize wear, using convolutional neural networks and other machine learning processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional vibration analysis and visual inspection methods are used, then the inspection process is simple and low-cost, but the wear detection precision and quantification capability are insufficient
Solution Approach 1:
The patent replaces traditional mechanical inspection methods (vibration analysis, visual inspection, clearance measurements) with optical scanning technology. The 3D optical scanner captures surface geometry data non-contactly, substituting mechanical measurement tools with optical fields to achieve higher precision wear detection without physical contact with the equipment surfaces.
Solution Approach 2:
The patent creates digital 3D copies of equipment surfaces through optical scanning. These digital models serve as virtual replicas that can be analyzed, compared, and stored for future reference, enabling precise wear quantification without repeatedly contacting the actual equipment. The digital twin approach allows for accurate wear pattern analysis while simplifying the physical inspection process.
2Reliability
If traditional inspection methods are used, then the equipment can continue operating, but wear patterns cannot be effectively quantified or predicted, leading to potential equipment failure
Solution Approach 1:
The patent introduces machine learning algorithms as an intermediary between the raw 3D scan data and wear assessment. The ML models process the geometric data, identify wear patterns, quantify wear severity, and predict future wear trends. This intermediary layer transforms raw measurement data into actionable insights, enabling reliable equipment condition assessment and preventing unexpected failures.
Solution Approach 2:
The patent performs preliminary wear detection and quantification using 3D scanning before critical failures occur. By continuously monitoring surface geometry changes and comparing them against baseline data, the system identifies wear patterns early in their development, allowing for predictive maintenance actions that prevent equipment failure and extend asset life.
3Measurement precision
If 3D scanning with machine learning is implemented, then wear can be accurately detected and quantified, but the system complexity and initial cost increase
Solution Approach 1:
The patent develops a multi-functional system where the 3D optical scanner can inspect various equipment types (gears, bearings, shafts, surfaces) using the same hardware platform. The machine learning models are trained to recognize multiple wear patterns (abrasive wear, adhesive wear, fatigue wear, pitting, spalling) across different component types, reducing the need for specialized equipment for each inspection scenario and amortizing the initial investment across multiple applications.
4Reliability
If frequent inspections are performed to detect wear early, then equipment reliability improves, but the time and resources required for inspection increase
Solution Approach 1:
The patent enables continuous or near-continuous wear monitoring by implementing rapid 3D scanning capabilities that can be performed during routine maintenance activities or even during equipment operation in some cases. The automated image capture and ML-based analysis process runs continuously, maintaining constant surveillance of equipment condition without requiring dedicated inspection stoppages, thus preserving production time while improving reliability through early wear detection.
Data Source
AI summary
Aspects of the technology described herein describe a system for detecting and reducing wear in industrial equipment. Aspects of the technology use 3D image data from a field inspection of industrial equipment to identify and quantify wear. The wear can be detected by providing the images from the field inspection to a computer classifier for recognition. Aspects of the technology also use machine learning to recommend a change to the operation of the equipment to minimize wear. Such a change could include load shedding, lube oil feed rate changes, prompts for maintenance, etc. Through incorporation of the wear data into the control system, the equipment can automatically change operation to improve wear performance and increase the durability and lifetime of the equipment.