Implement Vibration Detection for Accurate Ground Contact Timing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing construction machines face challenges in accurately determining the period during which an implement interacts with the ground surface, leading to inaccuracies in ground surface mapping, which can result in errors in project execution and material tracking.
Innovation Solution
Utilizing a vibration sensor to capture signals indicative of implement movement, combined with machine-learning models to predict implement-on-ground (IOG) start and end times, enabling precise determination of the interaction period.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sensors and methods are used to determine implement-on-ground interaction, then the system structure remains simple, but measurement precision and reliability of IOG detection deteriorate
Solution Approach 1:
The vibration signal processing is segmented into multiple stages: raw signal acquisition from accelerometer, feature extraction (time-domain and frequency-domain analysis), machine learning model processing, and final IOG state determination. This segmentation allows each component to be optimized independently while maintaining overall system accuracy.
Solution Approach 2:
A machine learning model serves as an intermediary between the raw vibration signals and the IOG state determination. The model processes complex vibration patterns and translates them into reliable IOG predictions, acting as a mediator that bridges sensor data and control decisions.
2Reliability
If vibration signals and machine-learning models are used to accurately detect IOG periods, then measurement precision improves, but device complexity and computational requirements increase
Solution Approach 1:
The machine learning model is pre-trained offline with labeled vibration data to learn the relationship between vibration patterns and IOG states. This preliminary action transfers computational burden from real-time operation to offline training, reducing on-board computational requirements while maintaining high reliability.
Solution Approach 2:
Traditional mechanical or threshold-based IOG detection methods are replaced with a data-driven machine learning approach. The system substitutes physical modeling with statistical learning, allowing the model to capture complex nonlinear relationships between vibration signals and ground interaction states.
3Manufacturing precision
If precise IOG detection is implemented through advanced signal processing, then manufacturing precision of ground surface maps improves, but loss of time for processing increases
Solution Approach 1:
The system processes vibration signals at discrete time intervals rather than continuously analyzing every data point. This periodic processing approach maintains ground surface map accuracy while reducing computational load and processing time compared to continuous analysis.
Solution Approach 2:
The system extracts only the most relevant features from vibration signals (such as dominant frequency components and time-domain statistics) rather than processing the complete signal spectrum. This partial action approach achieves sufficient precision for ground surface mapping while minimizing processing time.
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
Enables accurate real-time adjustment of ground surface maps and enhances material tracking by providing precise knowledge of when and where the implement contacts the ground, improving construction efficiency and accuracy.
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.