Loader Operating Condition Identification via Pressure Signal Clustering
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Solution Overview
Problem
Existing methods fail to effectively identify the difficulty level of operating conditions for engineering vehicles like loaders, leading to inefficient operation and fuel economy issues, particularly in varying material densities, which requires different shift control strategies and work modes.
Innovation Solution
A method that involves obtaining and analyzing pressure signals from the loader's moving arm and rotating bucket to extract working cycles, identify excavating operation segments, and use fuzzy logic C-means clustering and radar chart normalization to calculate a difficulty level index, allowing for intelligent power control and expanded application scope.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a loader is designed for high density materials with large traction requirements, then it can handle high density materials effectively, but it cannot efficiently handle loose materials requiring high speed
Solution Approach 1:
The patent applies universality by enabling a single loader to handle both high density materials (requiring large traction) and loose materials (requiring high speed) through intelligent control. The system uses sensor data from moving arm and rotating bucket, combined with fuzzy logic C-means clustering algorithms, to automatically identify operating conditions and adjust control parameters, allowing one machine to perform multiple functions that previously required specialized loaders.
2Reliability
If different specialized buckets are provided for different working media, then each material type can be handled optimally, but the device complexity and inventory requirements increase
Solution Approach 1:
The patent applies dynamics by making the bucket control parameters adjustable rather than fixed. Through real-time sensor monitoring of moving arm and rotating bucket pressure signals, the system dynamically identifies operating conditions using fuzzy logic C-means clustering and automatically adjusts control parameters to optimize performance for different materials, eliminating the need for multiple specialized buckets.
3Device complexity
If manual identification of operating conditions is used, then the system remains simple, but fuel economy and operational efficiency deteriorate
Solution Approach 1:
The patent applies self-service by enabling the loader to automatically identify its own operating conditions through integrated sensors and control systems. The system monitors pressure signals from the moving arm and rotating bucket, processes data through fuzzy logic C-means clustering algorithms, and autonomously determines difficulty levels without manual intervention, optimizing fuel consumption and operational efficiency while maintaining relatively simple hardware architecture.
Data Source
AI summary
The identification method of the difficulty level of the operating condition of the loader, takes the excavating operation segments extracted by the operation segment as the main objects of study to identify the operating conditions, and finally get the difficulty level value of the operating condition. The identification of the difficulty level of the operating condition is beneficial to control the power output mode of diesel engine and realize the distribution according to demand; simultaneously, as the judgement basis of intelligent shift control strategy, it has great significance for intelligent shift, power mode control and improving operation performance of engineering vehicles. It is also beneficial to the improvement of the performance and the energy saving and emission reduction; at the same time, the identification of the difficulty level of the operating condition is used to realize the change of power regulation, improve the application scope of engineering machinery.