Wheel Loader Work Content Distinction via Sensor Segmentation
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Solution Overview
Problem
Wheel loaders face challenges in accurately distinguishing between excavation and loading work and piling work, which affects fuel efficiency and productivity, as existing methods lack precision in differentiating these operations.
Innovation Solution
A system and method for a wheel loader that uses a combination of sensors and processors to analyze operational data, including boom and bucket angles, hydraulic pressures, and travel directions, to categorize work steps into excavation, loading, piling, and dozing, enabling accurate distinction and recording of work contents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing detection methods are used to distinguish work types, then basic work classification is achieved, but measurement precision of work content distinction deteriorates
Solution Approach 1:
The detection system is segmented into multiple specialized sensors (boom angle detector, bucket angle detector, travel direction detector, hydraulic pressure detector) that each measure specific parameters. This segmentation allows precise distinction between excavation/loading work and piling work by analyzing combinations of parameters from different segments, resolving the contradiction between measurement precision and device complexity.
Solution Approach 2:
The system transitions from single-dimension detection (basic work classification) to multi-dimensional detection by incorporating multiple parameters (boom angle, bucket angle, travel direction, hydraulic pressure). This dimensional expansion enables accurate differentiation between similar work types like excavation/loading and piling work, achieving high measurement precision without excessive complexity through systematic parameter combination.
2Productivity
If work classification is simplified, then device complexity is reduced, but productivity measurement accuracy deteriorates
Solution Approach 1:
The system segments productivity measurement into distinct work type categories (excavation/loading, piling, dozing) with specific detection criteria for each. By segmenting the measurement approach according to work type, the system achieves accurate productivity and fuel efficiency evaluation without requiring overly complex detection mechanisms, as each segment has tailored detection parameters.
Solution Approach 2:
The system changes detection parameters based on work type requirements. For excavation/loading work, it monitors combinations of boom angle, bucket angle, travel direction, and hydraulic pressure. For piling work, it uses different parameter thresholds and patterns. This parameter adaptation enables accurate productivity measurement across different work types while maintaining manageable system complexity through context-specific parameter selection.
Data Source
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AI summary
Work contents by a work implement are more accurately distinguished. Work contents by the work implement include at least two of dozing, piling, and excavation and loading. A controller distinguishes work contents by the work implement. The controller identifies work contents during a period from start of the works until end of the works based on a result of distinction between at least two temporally distant work contents in work records during the period from the start of the works until the end of the works.