Real-Time Shovel Tooth Detection Using Image Grouping Models
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Solution Overview
Problem
Mining operations face unplanned downtime and increased costs due to undetected missing shovel teeth, which can jam downstream crushers, highlighting the need for real-time detection of damaged or missing machine components.
Innovation Solution
A damaged machine component detection system utilizing processors, image capture devices, and grouping models to compare features of real-time images with previously captured images, generating alerts for deviations and updating groups to prevent false alarms, allowing for timely intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time image analysis is implemented to detect missing shovel teeth, then detection reliability is improved, but device complexity increases
Solution Approach 1:
The image processing task is segmented into distinct functional modules: image capture device for acquiring images, preprocessing module for enhancing image quality, feature extraction module for identifying tooth characteristics, and detection module for determining tooth presence. This segmentation allows each module to be optimized independently while maintaining overall system reliability without excessive complexity.
Solution Approach 2:
The system performs preliminary actions by capturing and storing reference images of complete tooth configurations before operation begins. These reference images are pre-processed and stored in a database for comparison during real-time monitoring, enabling rapid detection without complex real-time analysis of all image parameters.
2Productivity
If continuous monitoring of machine components is performed, then productivity is improved through reduced downtime, but use of energy increases
Solution Approach 1:
Instead of continuous monitoring, the system uses periodic action by capturing images at specific intervals during shovel operation or at predetermined checkpoints. The image capture device is triggered based on operational conditions rather than running continuously, reducing energy consumption while maintaining effective monitoring for productivity improvement.
Solution Approach 2:
The system enables self-service monitoring where the shovel's own operational movements bring it into the field of view of the image capture device, eliminating the need for additional power-intensive active sensing systems. The existing operational cycles are leveraged for monitoring purposes without requiring separate energy-consuming monitoring mechanisms.
Data Source
AI summary
A method and system for detecting a damaged machine component during operation of the machine are provided. The damaged machine component detection system includes one or more processors, one or more memory devices communicatively coupled to the one or more processors, an image capture device configured to generate a stream of temporally-spaced images of a scene including a machine component of interest, the images generated in real-time, and a grouping model configured to compare features of a current image of the stream of images to features of previously captured images sorted into a plurality of groups of images having similar features, generate an alert if predetermined features of the current image deviate from corresponding features of the grouped images by a predetermined amount, and update the groups with the current image if the predetermined features of the current image are similar to corresponding features of the grouped images by a predetermined amount. The system also includes an output device configured to output at least one of the alert and the current image.