Work Machine Instability Prediction Using Terrain Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for predicting the risk of rollover in work machines, such as those described in WO2016/195557, have limited accuracy, failing to adequately account for terrain conditions and machine stability, leading to potential damage and inefficiencies in operation.
Innovation Solution
A system and method that utilize on-board sensors and independent surveying devices to generate ground condition and surface topography data, processing this information to predict the risk of instability for work machines moving along a route, including the use of sensors like ground penetration radar and LIDAR to assess terrain compaction and surface profiles, thereby improving the accuracy of rollover and sliding risk assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing monitoring systems use basic ground gradient data to predict rollover risk, then the system complexity is low, but the measurement precision and reliability of rollover risk prediction are insufficient
Solution Approach 1:
The patent combines multiple monitoring systems including ground gradient sensors, terrain condition sensors, machine stability sensors, and weather condition sensors into a single integrated monitoring system. This merging of multiple data sources and sensor types enables comprehensive rollover risk assessment with high measurement precision while managing system complexity through integration.
Solution Approach 2:
The monitoring system is designed to perform multiple functions: measuring ground gradient, assessing terrain conditions, monitoring machine stability, and evaluating weather conditions. This multi-functional approach allows a single system to provide comprehensive rollover risk prediction accuracy without requiring separate dedicated systems for each parameter.
2Reliability
If the monitoring system incorporates multiple sensor types and comprehensive terrain data, then the reliability of instability risk prediction is improved, but the device complexity increases
Solution Approach 1:
The patent merges ground gradient sensors, terrain condition sensors, machine stability sensors, and weather condition sensors into an integrated monitoring system. This combination ensures reliable instability risk prediction by collecting comprehensive data from multiple sources while managing complexity through unified system architecture.
Solution Approach 2:
The monitoring system continuously collects data from multiple sensors and provides real-time feedback on instability risk. This feedback mechanism enhances reliability by enabling continuous monitoring and dynamic adjustment of risk assessment based on changing terrain and machine conditions.
3Productivity
If real-time terrain and machine data are continuously monitored and processed, then the productivity and operational efficiency are improved, but the use of energy and computational resources increases
Solution Approach 1:
The monitoring system performs periodic data collection and processing at optimized intervals rather than continuous monitoring. This periodic action maintains high operational efficiency by providing timely instability risk warnings while reducing energy consumption and computational load compared to truly continuous monitoring.
Solution Approach 2:
The system monitors all relevant parameters but processes and acts on critical data selectively. By focusing computational resources on the most significant instability indicators while still collecting comprehensive data, the system achieves high productivity without excessive energy consumption.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances the accuracy of predicting rollover and sliding risks, allowing for more stable and efficient operation of work machines by providing real-time data on terrain conditions and machine stability, enabling operators to avoid hazardous areas and optimize routes.
Implementation Method 1
the use of sensors like ground penetration radar and LIDAR to assess terrain compaction and surface profiles
Implementation Method 2
the use of sensors like ground penetration radar and LIDAR to assess terrain compaction and surface profiles
Data Source
Figure 1
Figure 2
Figure 3
AI summary
The present disclosure relates to improvements in the stability of work machines (11). A method for predicting a risk of instability for one or more work machine(s) (11) moving along a route (12) along terrain (13) of a worksite (14) is provided. Ground condition data indicative of the ground condition of the terrain (13) along the route (12) is obtained. Surface topography data indicative of the surface topography of the terrain (13) along the route (12) is obtained. Route data indicative of the route (12) along the terrain (13) is generated. The ground condition data, the surface topography data and the route data are processed to generate risk data indicative of a risk of instability along the route (12).