I. Real-time Status Monitoring Based on Sensor Data Modern liquid flow dyeing machines are widely equipped with various sensors, which serve as the fundamental information source for fault prediction:
1. Temperature and Pressure Sensors: Continuously monitor dye liquor temperature, cylinder pressure, and the pressure difference between the inlet and outlet of the heat exchanger. If the temperature rise curve deviates from the set rate or the pressure fluctuation exceeds ±5%, it may indicate an abnormality in the heating system or pipeline blockage.
2. Flow Meter and Level Gauge: Detect the stability of the dye liquor circulation flow rate. A sudden drop in flow rate often indicates filter blockage, decreased pump efficiency, or valve malfunction; abnormal liquid level may lead to dry running or overflow risks.
3. Motor Current and Vibration Sensors: Fluctuations in the main pump motor current reflect load changes; a sudden increase may indicate mechanical jamming, while a sudden drop indicates slippage or belt breakage; abnormal vibration frequency can identify bearing wear or imbalance problems.
II. Introducing Intelligent Algorithms for Trend Analysis and Anomaly Warning
Combining big data and machine learning technologies, historical and real-time data are modeled to improve prediction accuracy:
1. Establishing a Normal Operation Benchmark Model: Based on long-term operational data, "healthy ranges" are set for various parameters, such as a stable fabric cycle time of 60-90 seconds.
2. Dynamic Comparison and Overlap Judgment: When the fabric cycle time exceeds 180 seconds, or the fabric lifting roller speed remains at zero, the system determines an operational anomaly and triggers an alarm command.
3. Trend Prediction: By analyzing trends such as the temperature rise slope and dye liquor turbidity changes, the system predicts the risk of poor dye dispersion or oligomer deposition, allowing for advance scheduling of dye bath cleaning and maintenance.
III. Comprehensive Judgment Combining Process and Operational Behavior
Equipment status is closely related to process execution, requiring the inclusion of operational factors in the prediction system:
1. Parameter Configuration Rationality Check: For high-risk operations such as excessively low liquor ratio or excessively rapid temperature rise, the system can automatically identify and alert to potential color variations or downtime marks.
2. Dye Addition Monitoring: Incompletely dissolved dye entering the circulation system can easily cause nozzle clogging. The probability of clogging is assessed by recording the addition time, sequence, and filtration status.
3. Fabric Type and Equipment Matching Analysis: Using large nozzles or high flow rates on thin fabrics can easily lead to knotting. The system can recommend the optimal parameter combination based on the current fabric specifications.
IV. Building a Preventive Maintenance Knowledge Base and Log Tracking
1. Fault Log Recording and Pattern Recognition: After each fault, the phenomenon, cause, and handling method are recorded, forming a traceable knowledge base. For example, frequent "fabric clogging" may indicate an imbalance in the return valve adjustment or aging of the baffle seal.
2. Regular Health Assessment Reports: Weekly equipment health scores are generated, covering dimensions such as sealing performance, circulation efficiency, and temperature control accuracy, assisting managers in deciding when to perform maintenance.
