Mining Machine Efficiency Monitoring via Power-Payload Comparison
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Solution Overview
Problem
Electric mining shovels lack efficient monitoring systems to track power consumption and payload data, which hinders the assessment of operator performance and mining efficiency, particularly in determining bank difficulty and digability.
Innovation Solution
A monitoring system comprising a power monitor, a payload sensor, and a monitoring module that compares power consumption and payload data to generate efficiency metrics, including operator performance analysis and outputting bank difficulty and digability assessments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If no monitoring system is implemented, then the system remains simple, but operator performance and mining efficiency cannot be assessed
Solution Approach 1:
The monitoring module serves multiple functions: it receives power consumption data from the power monitor, receives payload data from the sensor, compares these data to generate efficiency data, and outputs the efficiency data. This multi-functional approach enables comprehensive efficiency assessment without requiring separate specialized systems for each measurement function.
Solution Approach 2:
The monitoring module acts as an intermediary component that processes and compares data from the power monitor and sensor, transforming raw data into meaningful efficiency metrics. This intermediary function enables the connection between raw measurements and performance assessment without direct complex interaction between the power monitor and sensor systems.
2Loss of information
If comprehensive data collection is implemented, then efficiency analysis capability is improved, but data processing complexity increases
Solution Approach 1:
The monitoring module extracts and compares specific key parameters (power consumption data and payload data) from the collected information, focusing analysis on the most relevant data elements for efficiency assessment. This selective extraction approach maintains information completeness while avoiding unnecessary processing of all possible data types.
Solution Approach 2:
The system transforms raw power consumption data and payload data into derived efficiency parameters through comparison and calculation. This parameter transformation converts multiple raw measurements into a consolidated efficiency metric that is easier to analyze and interpret, reducing the complexity of subsequent analysis.
Data Source
AI summary
A mining machine including a power monitor, a sensor, and a monitoring module. The power monitor is configured to measure a received power, and generate a total power consumption data based on the received power. The sensor senses payload of the mining machine to generate payload data. The monitoring module includes non-transitory computer readable media for comparing the total power consumption data and the payload data to generate mining machine efficiency data, determining an operator performance comparing the mining machine efficiency data and the operator performance, determining, based on the comparison of the mining machine efficiency data and the operator performance, at least one selected from the group consisting of a bank difficulty and a bank digability, and outputting the at least one selected from the group consisting of the bank difficulty and the bank digability.