Working Machine Productivity Evaluation Using Control Bus Data
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Solution Overview
Problem
Existing methods fail to accurately and reliably monitor long-term technical performance and productivity of forest machines and their drivers, relying on subjective evaluations and lacking real-time feedback, which hinders timely detection of performance reductions and optimization opportunities.
Innovation Solution
A system that uses historical data analysis and mathematical methods like Hidden Markov Models and Adaptive Network-Based Fuzzy Interference Systems to monitor and model work cycles, providing real-time feedback and optimizing driver performance without requiring new sensors or computing modules, by correlating driver actions and conditions to improve productivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If subjective evaluations and experiences of operators are used to detect performance reduction, then the evaluation method is simple to implement, but the measurement precision and reliability are insufficient
Solution Approach 1:
The system uses the machine's own control bus and existing sensors to automatically monitor and evaluate performance. The control system itself provides the data needed for performance analysis, eliminating the need for external evaluation systems or manual monitoring by operators.
Solution Approach 2:
The patent replaces subjective human evaluation with automated digital signal processing. Mathematical models and algorithms automatically analyze control bus signals and sensor data to detect performance reductions, substituting operator experience with computational analysis.
2Reliability
If historical data is collected and analyzed to monitor long-term performance trends, then the reliability of performance evaluation is improved, but the loss of time and data processing complexity increases
Solution Approach 1:
The system continuously collects and stores historical data from the control bus in real-time during machine operation. This preliminary data accumulation enables later retrospective analysis without requiring additional real-time processing, allowing performance trends to be evaluated after the fact using stored data.
Solution Approach 2:
The system performs periodic analysis of historical data at scheduled intervals rather than continuous processing. This periodic evaluation approach reduces computational load while still enabling reliable detection of long-term performance trends through accumulated historical data.
3Productivity
If real-time monitoring and feedback systems are implemented, then the productivity optimization capability is improved, but the device complexity and energy consumption increase
Solution Approach 1:
The system uses the existing control bus and sensors for multiple purposes: normal machine control, performance monitoring, data collection, and productivity analysis. This multi-functionality eliminates the need for separate dedicated monitoring hardware, reducing overall system complexity while enabling real-time productivity optimization.
Solution Approach 2:
The system implements feedback by analyzing historical data and control bus signals to identify performance trends and productivity opportunities. This feedback mechanism provides insights that can be used to optimize machine operation without requiring complex real-time intervention systems.
Data Source
AI summary
A system and method for evaluating the productivity of a working machine and its driver in a real or virtual operating environment is controlled by a control system to perform work, and in which the work cycles relating to the work performed by the working machine are determined by continuous measurements directed to the working machine when it is controlled by the driver. Characteristic values relating to the performance of the determined work cycles are collected on the basis of the continuous measurements for the purpose of evaluating the performance of the work or for comparison.