Combining the excavator weighing system with AI represents a technological development direction with great potential. This combination can not only improve the accuracy and efficiency of the weighing system but also enable intelligent operation and data analysis, thereby enhancing the overall performance and operation efficiency of the excavator. AI can enhance the performance of the excavator weighing system in the following ways:
(1) Data Preprocessing
Noise Filtering: Utilize AI (such as wavelet transform and Kalman filter) to denoise the sensor data, improving the accuracy of the data.
Outlier Detection: Employ machine learning models (such as the isolation forest and support vector machine) to identify and eliminate abnormal data, preventing it from affecting the weighing results.
(2) Optimization of Weight Calculation
Nonlinear Modeling: There are complex nonlinear relationships during the operation of the excavator. AI (such as neural networks and random forests) can establish more accurate mechanical models to improve the accuracy of weight calculation.
Dynamic Compensation: Use AI to provide real-time compensation for factors such as inertial force and vibration during the dynamic operation of the excavator, further improving the weighing accuracy.
(3) Intelligent Operation
Automatic Calibration: Achieve automatic calibration of the weighing system through AI, reducing manual intervention and improving the reliability of the system.
Operation Optimization: Based on the weighing data and the operation environment, AI can provide operators with the best operation suggestions, improving operation efficiency.
(4) Data Analysis and Prediction
Historical Data Analysis: Use AI to analyze historical weighing data, uncover potential patterns and trends, and provide a basis for equipment maintenance and operation planning.
Fault Prediction: Employ AI (such as time series analysis and deep learning) to predict possible faults in the weighing system, take maintenance measures in advance, and reduce downtime.
