Dry Pump Shutdown Early Warning Using Kalman Prediction
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Solution Overview
Problem
Dry pump shutdowns in the panel industry lead to loss of control over product quality, increased maintenance costs, and capacity losses due to inefficient maintenance methods and low accuracy in monitoring systems.
Innovation Solution
A method and apparatus for early warning of dry pump shutdown using historical operating data to build a Kalman filter model, predict future operating data, and train a shutdown prediction model, which filters out invalid data and normalizes the data for accurate shutdown risk prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional monitoring systems are used to track dry pump operation, then device management is simplified, but the accuracy of shutdown prediction is low leading to passive maintenance
Solution Approach 1:
The system performs preliminary actions by collecting historical operating data and training prediction models in advance. The shutdown prediction model is trained offline using historical data, and then used for real-time prediction, allowing the system to prepare maintenance actions before actual shutdown occurs
Solution Approach 2:
The patent replaces traditional mechanical monitoring systems with data-driven prediction models. Instead of using complex physical sensors and mechanical monitoring equipment, the system uses machine learning models (LSTM, XGBoost, Random Forest) that process operating data to predict shutdown risks, substituting mechanical complexity with computational intelligence
2Productivity
If reactive maintenance is performed after dry pump shutdown, then maintenance costs increase and production capacity is lost, but implementing predictive maintenance requires advanced data processing
Solution Approach 1:
The system enables self-service by automatically collecting operating data, training prediction models, and generating maintenance recommendations without requiring external expert intervention. The model continuously learns from historical data and autonomously predicts shutdown risks, allowing the equipment to essentially monitor and warn about its own health status
Solution Approach 2:
The system implements feedback mechanisms where prediction results are continuously monitored and used to improve future predictions. The model is retrained periodically with new historical data, creating a closed-loop system where past performance informs future accuracy, and maintenance actions are adjusted based on prediction outcomes
3Reliability
If spare parts are not properly managed, then maintenance costs increase and quality control is lost, but implementing predictive maintenance requires sophisticated modeling
Solution Approach 1:
The system performs preliminary actions by predicting shutdown risks before they occur, allowing advance preparation of spare parts and maintenance resources. The multi-model prediction approach (LSTM for temporal patterns, XGBoost and Random Forest for feature importance) identifies trends early, enabling proactive quality control measures
Solution Approach 2:
The system monitors and analyzes changes in operating parameters over time to predict shutdown risks. By tracking variations in vibration, temperature, pressure, and other parameters through the prediction models, the system detects early signs of degradation that indicate quality issues before they affect production
Data Source
AI summary
The disclosure provides a method and apparatus for early warning of dry pump shutdown, an electronic device, a storage medium and a program, and belongs to the technical field of automatic control. The method comprises: obtaining historical operating data of a dry pump; building a Kalman filter model by using the historical operating data; predicting predicted operating data of the dry pump through the Kalman filter model; training a shutdown prediction model by using the historical operating data and the predicted operating data; and inputting current operating data of the dry pump into the trained shutdown prediction model to obtain shutdown early warning information of the dry pump.