Implement-Ground Detection Using Vibration Signals and ML
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Solution Overview
Problem
Existing technologies struggle to accurately predict the period during which an implement of a construction machine is interacting with the ground surface, which is crucial for maintaining an accurate ground surface map.
Innovation Solution
A method using a vibration signal captured by a sensor mounted on the construction machine, where features are extracted and provided to a machine-learning model to predict the implement-on-ground (IOG) start and end times, thereby determining the interaction period.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If vibration signals and machine-learning models are used to predict implement-on-ground periods, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent replaces complex mechanical sensing systems with vibration sensors (accelerometers/gyroscopes) that capture ground interaction through vibration signals. Machine-learning models then process these signals to predict IOG periods, substituting mechanical complexity with signal processing and algorithms while maintaining measurement precision.
2Productivity
If real-time vibration signal processing is implemented, then productivity is improved, but use of energy increases
Solution Approach 1:
The system processes vibration signals in discrete time windows rather than continuously, extracting features periodically from captured signals. This periodic processing approach enables real-time IOG period prediction while reducing overall energy consumption compared to continuous signal analysis.
3Measurement precision
If multiple sensor types are used to capture vibration signals, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent combines multiple sensor types (accelerometers and gyroscopes) into an integrated sensor system that captures both linear and rotational vibration signals. By merging these sensors and processing their outputs together through a unified machine-learning model, the system achieves enhanced measurement precision while managing device complexity through integration.
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
This approach allows for accurate adjustments of the ground surface map in real-time, enhancing the precision of construction operations and enabling better tracking of material movement.
Implementation Method 1
capturing a vibration signal that is indicative of a movement of the implement
Data Source
AI summary
Described herein are systems, methods, and other techniques for determining a period during which an implement of a construction machine is interacting with a ground surface. A vibration signal that is indicative of a movement of the implement is captured. One or more features are extracted from the vibration signal. The one or more features are provided to a machine-learning model to generate a model output. An implement-on-ground (IOG) start time and an IOG end time are predicted based on the model output, the IOG start time and the IOG end time forming the period.