Abrasive Tool Sensor Segmentation for Damage Detection
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Solution Overview
Problem
Existing abrasive tools equipped with sensors can only monitor electrical power consumption and lack the capability to effectively determine if a component is damaged or malfunctioning.
Innovation Solution
A computer-implemented method that receives sensor data from sensors disposed in proximity to abrasive products or workpieces, and uses a trained machine learning system to determine product-specific or workpiece-specific information, providing real-time feedback and predictive analytics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If a power sensor is incorporated into the tool to monitor electrical power consumption, then useful information related to tool usage is provided, but the sensor cannot effectively determine whether a component has been damaged or is malfunctioning
Solution Approach 1:
The sensing system is segmented into multiple independent sensors (vibration sensor, temperature sensor, force sensor) rather than relying on a single power sensor. Each sensor captures specific operational parameters, and the combination of these segmented measurements provides comprehensive diagnostic capability for detecting component damage and malfunction.
Solution Approach 2:
A processor acts as an intermediary that receives data from multiple sensors and analyzes it to determine component status. The processor integrates information from vibration, temperature, and force sensors to provide reliable diagnostic conclusions about component damage or malfunction, going beyond what a single power sensor can detect.
2Reliability
If multiple sensors are used to collect comprehensive operational data, then the ability to detect component damage improves, but the device complexity increases
Solution Approach 1:
The sensing system is designed with multi-functionality where a single integrated sensor unit can perform multiple measurement functions (vibration, temperature, force) depending on the specific sensor type used. This universal approach allows comprehensive monitoring without proportionally increasing system complexity, as each sensor serves multiple diagnostic purposes.
Solution Approach 2:
The system automatically processes and analyzes sensor data through the processor without requiring manual intervention. The self-service capability of automatic data processing and component status determination reduces the operational complexity burden, allowing the system to manage its own monitoring and diagnostic functions efficiently.
Data Source
AI summary
The present application relates to systems and methods for obtaining real-time abrasion data. An example computer-implemented method could include receiving, at a computing device, sensor data from one or more sensors. The one or more sensors are disposed in proximity to an abrasive product or a workpiece associated with the abrasive product. The one or more sensors are configured to collect abrasion operational data associated with an abrasive operation involving the abrasive product or the workpiece. The computer-implemented method could further include training, based on the sensor data, a machine learning system to determine product specific information of the abrasive product and/or workpiece specific information. The computer-implemented method could also include providing the trained machine learning system using the computing device.