Construction Machine Diagnosis Using Waveform Comparison
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The existing methods for diagnosing the performance of construction machines are complex and require skilled operators, involving different approaches for various diagnostic portions, which increases the workload and reduces accuracy.
Innovation Solution
A machine performance diagnosis apparatus that uses a storage device to hold reference waveform data and performance reference values for various operations of a construction machine, and a processor to compare sensor waveform data with the reference data to diagnose the soundness of the machine.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If different approaches are used for various diagnostic portions, then measurement accuracy is improved, but device complexity and operator workload increase
Solution Approach 1:
The patent applies universality by creating a single integrated diagnosis apparatus that can handle multiple diagnostic portions (engine, transmission, hydraulic system, etc.) through automatic operation mode identification. The system uses one universal device with sensors and a processor that automatically adapts to different diagnostic needs based on the detected operation mode, eliminating the need for multiple specialized diagnostic tools and complex manual procedure selection.
2Measurement precision
If manual confirmation of procedures and vehicle posture is required, then measurement accuracy is improved, but productivity and ease of operation worsen
Solution Approach 1:
The diagnosis apparatus applies self-service by automatically identifying the operation mode through sensor-based waveform analysis without requiring manual confirmation of vehicle posture or operation details. The system autonomously determines which diagnostic procedure to execute based on the detected operation mode, eliminating the need for operators to manually verify procedures and significantly improving diagnosis efficiency while maintaining accuracy.
Solution Approach 2:
The system uses feedback from sensor waveforms to automatically adjust the diagnostic process. By continuously monitoring operation waveforms and comparing them against reference data, the system receives feedback about the actual operation mode and automatically selects the appropriate diagnostic procedure, ensuring measurement accuracy without manual intervention.
3Measurement precision
If dedicated equipment and timing attention are required, then measurement accuracy is improved, but ease of operation and productivity worsen
Solution Approach 1:
The apparatus performs self-service by automatically identifying operation modes and selecting appropriate diagnostic procedures without requiring operator skill for equipment selection or timing coordination. The system autonomously manages the entire diagnostic process based on sensor input, making operation simple while maintaining high diagnostic accuracy through automated reference value comparison.
Data Source
Figure 1
Figure 2
Figure 3~4
AI summary
The present disclosure proposes a machine performance diagnosis apparatus for diagnosing soundness of operation of a construction machine for reducing possibility of human error and obtaining more accurate diagnosis results. The machine performance diagnosis apparatus includes a storage device and a processor. The storage device stores reference waveform data for each of a plurality of operations of the construction machine, a performance reference value and a tolerance value for each of the plurality of operations. The processor executes a process of diagnosing the soundness of the construction machine. The processor executes a process of identifying an actually performed operation of the construction machine by comparing the sensor waveform data acquired by detecting the actually performed operation using a sensor with the reference waveform data held in the storage device, and a process of acquiring the performance reference value and the tolerance value corresponding to the identified operation from the storage device, and comparing the acquired performance reference value and tolerance value with the feature value in the actually performed operation acquired from the sensor waveform data to determine the soundness of the construction machine (see Fig. 2).