Haul Machine Sensor System for Berm Instability Prediction
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Solution Overview
Problem
The instability of raised contours, such as berms, near high walls in work sites poses risks to the operation of haul machines, as they become unstable or require re-building, necessitating a system to predict failures and generate alerts for timely intervention.
Innovation Solution
A failure prediction and notification system equipped with perception sensors on haul machines generates electronic maps of the work surface, compares initial and current topography characteristics to predefined thresholds, and alerts operators when differences exceed these thresholds, enabling proactive maintenance and route adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If haul machines repeatedly operate near the high wall and berm, then material moving operations are maintained, but the berm becomes unstable and requires re-building
Solution Approach 1:
The system performs preliminary monitoring of berm characteristics before failure occurs. By continuously measuring physical characteristics and comparing them to reference values, the system detects early signs of instability and generates warnings, allowing preventive action to be taken before the berm actually fails and disrupts operations.
Solution Approach 2:
The system establishes a feedback loop where berm physical characteristics are continuously measured, compared to reference values, and used to generate real-time warnings. This closed-loop monitoring enables dynamic adjustment of operations based on actual berm conditions, maintaining both productivity and safety.
2Reliability
If the berm is rebuilt to maintain stability, then operational safety is improved, but scheduling of material-moving machines is required which reduces productivity
Solution Approach 1:
The system performs preliminary detection of berm instability before complete failure occurs. By generating early warnings when physical characteristics deviate from reference values, the system allows for planned, gradual berm maintenance rather than emergency shutdowns, minimizing disruption to material moving operations.
Solution Approach 2:
The monitoring system enables the operation to self-regulate by providing real-time feedback on berm conditions. Operators can make informed decisions about when and how to adjust operations or perform maintenance based on actual measured data, rather than following fixed schedules, thereby optimizing both safety and productivity.
3Reliability
If perception sensors are installed on haul machines to monitor work surface, then failure prediction capability is improved, but device complexity increases
Solution Approach 1:
The perception sensors mounted on haul machines serve multiple functions: they monitor berm physical characteristics, track machine position and orientation, and provide data for both safety monitoring and operational optimization. This multi-functionality justifies the added complexity by delivering multiple benefits from a single sensor system.
Solution Approach 2:
The sensors are mounted on the haul machines themselves, which already have power sources, processors, and communication systems. The machines effectively serve themselves by using their existing infrastructure to support the monitoring function, rather than requiring separate dedicated monitoring equipment, thereby reducing overall system complexity.
Data Source
AI summary
A failure prediction and notification system for a work surface that has a raised contour includes a perception sensor mounted on a machine and a controller. The controller determines an initial physical characteristic of the initial topography of the work surface adjacent the raised contour, generates an electronic map of a current topography of the work surface based at least partially on the raw data points, and determines a current physical characteristic of the current topography. The controller further determines a difference between the current physical characteristic and the initial physical characteristic, compares the difference to a characteristic difference threshold, and generates an alert upon the difference exceeding the characteristic difference threshold.