Work Machine Performance Diagnosis Using Load and Site Models
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Solution Overview
Problem
Existing performance diagnosing methods for work machines do not consider the influences of onsite characteristics and varying load amounts, leading to inadequate diagnosis of performance degradation.
Innovation Solution
A performance diagnosing device that creates a reference performance model for each combination of operational type, load amount, operation detail, and onsite characteristic, allowing for precise diagnosis by comparing operational data against this model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a predefined threshold is used to diagnose performance degradation, then the diagnosis method is simple and easy to implement, but it does not consider past operational history and individual machine differences, leading to low diagnostic accuracy
Solution Approach 1:
The system performs preliminary data collection and storage during normal operation before diagnosis is needed. Operational data including time series data from multiple vehicles is sequentially stored and saved during normal operation, preparing the foundation for accurate future diagnosis without requiring complex real-time processing
Solution Approach 2:
The system uses feedback from historical operational data to continuously refine performance baselines. By comparing current operational data against historically collected data from the same vehicle and peer vehicles, the system dynamically adjusts performance expectations, enabling accurate diagnosis that adapts to individual machine characteristics and aging patterns
2Ease of operation
If a fixed normal value range is configured for each machine type, then the diagnosis device is simple to operate, but it cannot diagnose performance considering individual differences of machines
Solution Approach 1:
The system segments the fleet into individual vehicle units, each with its own performance baseline. Instead of treating all machines of a type uniformly, the system creates separate data streams and performance models for each vehicle, allowing individual differences to be captured while maintaining systematic processing across the entire fleet
Solution Approach 2:
The system changes the reference parameter from fixed manufacturer specifications to dynamic, data-driven baselines. By using operational data from each vehicle's own history and peer vehicles to establish normal ranges, the system adapts performance criteria to match actual individual machine characteristics rather than applying universal standards
3Measurement precision
If operational data from multiple vehicles is collected and analyzed, then diagnostic accuracy considering individual differences is improved, but the device complexity and data processing requirements increase
Solution Approach 1:
The system uses a universal data collection and processing framework that handles multiple vehicles simultaneously through standardized procedures. The same data acquisition, storage, and analysis processes are applied across all vehicles in the fleet, enabling multi-vehicle analysis without proportionally increasing system complexity through replication of specialized components for each vehicle
Data Source
AI summary
The purpose of the present invention is to provide a performance diagnostic device capable of diagnosing deterioration in performance of a work machine while taking into account the effects of the amount of load and the characteristics of the site where the work machine operates. A performance diagnostic device according to the present invention uses a reference performance model generated for each combination of the type of operation performed by a work machine, the amount of load, operation details, and site characteristics to diagnose the deterioration in performance of the work machine.