I. Real-time Monitoring and Acquisition of Core Parameters
Continuously collect data on main motor current, vibration, and temperature to capture abnormal fluctuation signals.
Regularly record screw and barrel clearance and wear, tracking long-term degradation trends.
Synchronously collect key indicators such as temperature control system deviation, gearbox oil status, and vacuum level.
II. Trend-Based Manual Prediction Method
Differentiate between "chronic" and "acute" fault characteristics, identifying long-term unidirectional drift trends in parameters.
Establish a component lifespan ledger, providing early warnings by comparing with typical lifespan thresholds for different materials.
Compare historical fault data and match current parameter characteristics to predict the probability of similar faults occurring.
III. Intelligent Prediction Technology
Deploy multi-dimensional sensors to collect operating characteristics of the motor, screw, and transmission system.
Utilize machine learning algorithms to perform anomaly clustering analysis on the collected data, providing early warnings 72 hours in advance.
Integrate with the equipment management system to automatically generate predictive maintenance work orders and accurately schedule spare parts replacement.
IV. Supporting Measures
Establish a fault prediction knowledge base, continuously accumulating fault characteristic samples under different operating conditions.
Regularly calibrate monitoring sensors to avoid false alarms and missed alarms caused by data deviations.
Dynamically adjust maintenance cycles based on predictive results, replacing the fixed-cycle passive maintenance approach.
