Construction Machinery Operating System for Fuel Efficiency
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Solution Overview
Problem
Construction machines, such as excavators, lack the ability to provide operators with real-time advice for improving fuel efficiency during operations, as existing systems only display calculated data without enabling efficient driving and work practices.
Innovation Solution
An operating system that sets target values for operational conditions, detects and calculates frequency distributions, and outputs messages to operators based on comparisons between detected and target values, allowing for adjustments to improve fuel consumption and operational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If the system only displays calculated work quantity and fuel consumption data, then the data processing function is simple, but the operator cannot receive practical advice for improving fuel efficiency
Solution Approach 1:
The system introduces feedback by comparing detected frequency distributions against target values and providing operational advice messages back to the operator. This closed-loop feedback mechanism transforms static data display into dynamic guidance, enabling operators to adjust their operations based on real-time comparisons between actual and target frequency distributions, thereby reducing fuel consumption through informed operational adjustments
Solution Approach 2:
The control section acts as an intermediary between the detection section and the operator. It processes raw detection data by calculating frequency distributions, comparing them with target values, and generating meaningful operational advice messages. This intermediary function transforms complex sensor data into actionable guidance without requiring direct complex interaction between sensors and the operator
2Loss of energy
If the system provides real-time operational advice through frequency distribution comparison, then fuel efficiency improves, but the device complexity increases
Solution Approach 1:
The system enables self-service by allowing operators to independently optimize their own fuel consumption through the provided operational advice. The detection and control sections work together to automatically calculate frequency distributions, compare them with targets, and generate advice messages that empower operators to make their own efficiency improvements without external intervention, thereby reducing fuel consumption through operator-initiated adjustments
Solution Approach 2:
The system monitors and responds to changes in operational parameters by calculating frequency distributions of detected parameters and comparing them against target frequency distributions. When deviations are detected, operational advice messages guide operators to adjust parameters back toward optimal ranges, enabling dynamic adaptation to changing operational conditions while maintaining fuel efficiency
3Adaptability or versatility
If multiple state values are monitored and compared, then the comprehensiveness of operational advice improves, but the processing complexity increases
Solution Approach 1:
The system applies segmentation by dividing the monitoring task into separate detection sections, each dedicated to detecting specific state values. The control section then processes each state value's frequency distribution independently against its corresponding target value. This segmented approach enables comprehensive monitoring of multiple parameters while maintaining manageable processing complexity through modular, independent analysis of each parameter
Data Source
AI summary
The operating system for a construction machine gives advice on efficient operation to an operator. A specified state value relating to the operational condition of the construction machine, for example, the hydraulic oil pressure or engine speed, is detected (S101), and the frequency distribution of the state value in prescribed time intervals is calculated (S102). The variable range of the state value is classified into plural regions beforehand, and different target values are pre-set for each these regions. For each region, the frequency distribution is compared with the target value (S104, 106, 108, 110, 112), and as a result of the comparison, a applicable message is selected from the prescribed messages and output (S105, 107, 109, 111, 113). The output message may also be selected according to the combination of the comparison results for the plural state values such as the hydraulic oil pressure and engine speed.