Work Machine Implement Motion Start Detection Under Vibration Noise
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Solution Overview
Problem
Existing calibration methods for work machine implements are hindered by noise in position data due to machine vibrations, making it difficult to accurately determine the minimum velocity and initiate proper motion.
Innovation Solution
A method involving a controller that collects and processes position data from sensing devices over two time periods to calculate noise values, determining the start of motion and minimum velocity, and subsequently calibrates the implement based on these calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If position data is collected during machine operation to determine minimum velocity, then calibration can be performed, but vibrations generate noise in the position data making accurate identification difficult
Solution Approach 1:
The system performs preliminary actions by collecting position data during a first time period before actual motion occurs, establishing a baseline noise profile. This preliminary noise characterization is then used to filter and interpret subsequent position data, enabling accurate minimum velocity identification despite vibration noise during operation.
Solution Approach 2:
The system implements periodic action by collecting position data across multiple distinct time periods (first time period for noise characterization, second time period for motion detection). This periodic sampling approach allows the system to distinguish between random vibration noise and actual implement motion by comparing statistical properties across different time intervals.
2Measurement precision
If noise filtering is applied to position data to improve velocity detection, then measurement accuracy improves, but calibration time increases
Solution Approach 1:
The system performs preliminary noise characterization during a first time period before actual calibration begins. By pre-establishing the noise profile and filtering parameters during this initial phase, the system avoids the need for complex real-time filtering during the calibration process itself, thereby reducing overall calibration time while maintaining measurement accuracy.
Solution Approach 2:
The system applies partial filtering by characterizing noise statistics from a portion of the collected data (first time period) and using this partial characterization to process the remaining data. This approach balances filtering thoroughness with time efficiency, applying just enough filtering to achieve accurate velocity detection without excessive processing time.
Data Source
AI summary
A control may obtain first data related to a plurality of positions of an implement of a work machine during a first time period and may determine, based on the first data, a first noise value related to at least one velocity of the implement for the first time period. The control device may obtain second data related to a plurality of positions of the implement during a second time period and may determine, based on the second data, a second noise value related to at least one velocity of the implement for the second time period. The control device may determine, based on the first noise value and the second noise value, a start of motion of the implement. The control device may cause, based on determining the start of motion of the implement, the implement to be calibrated.