Role-Based Asset Tagging for Accurate Machine Interaction Reporting
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Solution Overview
Problem
Existing systems for monitoring and optimizing machine asset performance at work sites, such as construction or mining sites, often suffer from inaccuracies due to false positives in tracking and quantifying material handling activities, leading to inefficiencies in operations management.
Innovation Solution
A performance reporting system that utilizes role-based asset tags to determine asset interactions and inferential proximity, reducing reliance on on-board monitoring systems by matching role-based asset tags to confirm actual material handling actions, thereby enhancing the accuracy of performance metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If on-board monitoring systems are used to track machine activities, then real-time performance data can be collected, but false positives occur leading to inaccurate performance quantification
Solution Approach 1:
A centralized server acts as an intermediary between multiple on-board monitoring systems and the performance reporting system. The server receives data from various machines, processes it centrally, and cross-validates material handling actions before generating performance reports. This intermediary approach eliminates false positives by requiring corroborating evidence from multiple sources before confirming an action occurred.
Solution Approach 2:
The system implements feedback loops where the server continuously receives performance data from on-board monitoring systems, validates it against established criteria, and adjusts its validation rules based on patterns detected in the data. This feedback mechanism improves measurement precision over time by learning from false positives and refining the validation algorithm.
2Measurement precision
If multiple monitoring systems are deployed to reduce false positives, then measurement precision improves, but system complexity increases
Solution Approach 1:
Multiple distributed on-board monitoring systems are merged into a single centralized server architecture. Instead of each machine operating independently with complex local validation logic, all monitoring systems feed data to a central server that performs unified validation. This merging reduces overall system complexity while maintaining high measurement precision through centralized intelligence.
Solution Approach 2:
The centralized server performs multiple functions: collecting data from various machine types, validating material handling actions, generating performance reports, and storing historical data. This universal platform handles diverse monitoring needs across different machine types (loaders, haul trucks, crushers) with a single multi-functional system, reducing complexity compared to specialized systems for each machine type.
Data Source
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AI summary
Performance quantifying and reporting for machine assets (12) includes storing, in a work plan, asset tag assignments for a plurality of assets, and receiving location information, for example, indicative of a segment of a work cycle, being worked on by an asset. Attributes of an asset (12), including an inferred occurrence or non-occurrence of an asset-to-asset interaction, are based upon the location information and matching of role-based asset tags between or amongst assets. Performance history of the asset (12) is quantified and reported based on the identified attributes for displaying, on a user interface (46), machine asset performance metrics.