Abrasive Process Analytics for Anomaly Detection and Tool Maintenance
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Solution Overview
Problem
Abrasive tools equipped with sensors can monitor usage but fail to effectively determine component damage or malfunction, limiting their ability to provide comprehensive operational insights to users.
Innovation Solution
A computer-implemented method that receives and analyzes sensor data from abrasive tools to generate reports on machine downtime, operator efficiency, operation metrics, shift variations, and machine comparisons, using unsupervised machine learning to detect anomalies and predict tool maintenance needs, thereby enhancing operational efficiency and tool management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If power sensors are incorporated into abrasive tools to monitor electrical power consumption, then useful information related to tool operation is provided, but the sensor data cannot effectively determine whether a component has been damaged or is malfunctioning
Solution Approach 1:
The monitoring system is segmented into multiple specialized sensors (power sensor, current sensor, voltage sensor, temperature sensor, vibration sensor) each capturing specific operational parameters. This segmentation allows comprehensive data collection that can detect both general operational status and specific component malfunctions, resolving the limitation of single-sensor systems.
Solution Approach 2:
The sensor system is designed with multi-functionality to perform diverse monitoring tasks simultaneously - power consumption monitoring, temperature monitoring, vibration analysis, and malfunction detection. This universal approach enables the system to provide both operational insights and component damage detection using an integrated sensor network.
2Reliability
If multiple sensors are used to capture comprehensive operational data, then component damage and malfunction detection capability is improved, but the complexity of data collection and analysis increases
Solution Approach 1:
Multiple sensor functions are merged into an integrated monitoring system that collects power, current, voltage, temperature, and vibration data through a unified data processing architecture. This merging reduces the complexity of managing separate sensor systems while maintaining comprehensive monitoring capabilities for reliable damage detection.
Solution Approach 2:
A computing device acts as an intermediary between the multiple sensors and the user, receiving data from all sensors, processing the information, and generating actionable insights. This intermediary layer simplifies the system by centralizing data processing and presenting consolidated results, reducing the complexity burden on both the sensor network and the user.
3Productivity
If real-time sensor data is collected and analyzed, then operational efficiency and tool life optimization are improved, but the computational resources and processing time required increase
Solution Approach 1:
The system performs preliminary data processing and analysis during tool operation, continuously monitoring parameters and preparing insights in advance. This preliminary action enables quick decision-making without requiring extensive post-processing time, thus improving operational efficiency while managing computational resources effectively.
Solution Approach 2:
The monitoring system provides self-service by automatically analyzing sensor data, generating reports, and providing actionable insights without requiring external intervention. This automation reduces the time and computational resources needed for manual analysis while maintaining high productivity through continuous autonomous monitoring and optimization.
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.