Common causes of multi-point redundant temperature measurement failures in hot runner systems mainly include sensor drift due to moisture, electromagnetic signal interference, poor wiring contact, defects in redundancy switching logic, and insufficient AI model training. These are especially prone to occur in the high-temperature and high-humidity environment of Wuhan.
1. Sensor Drift Due to Moisture and Aging (Most Common)
Causes: High humidity environments (such as 90% RH in Wuhan) cause the thermocouple insulation layer to become damp, coke, or oxidize, leading to a slow deviation in the measured value;
Typical Manifestations: Continuous deviation in temperature readings (e.g., above +2℃), AI compensation misadjustment, resulting in dimensional errors;
High-Risk Points: Weakly sealed areas such as the temperature control box terminals and nozzle roots.
2. Electromagnetic Signal Interference and Noise Amplification
Causes: Dense, multi-point wiring, unshielded cables, susceptible to electromagnetic interference from heating circuits or motor drives;
Typical Manifestations: Temperature curve spikes or jumps (>5℃/s), triggering AI miscompensation or false alarms;
High-Risk Scenarios: High-frequency start/stop, parallel operation of high-power heaters.
Engineering Countermeasures: Use twisted-pair shielded cables with single-end grounding, and implement a 5Hz first-order low-pass filter on the controller, which can reduce noise by more than 70%.
3. Loose Wiring or Poor Contact
Causes: Quick-connect interfaces not tightened under vibration, or oxidation of connectors leading to increased contact resistance;
Typical Manifestations: Sudden temperature drop to zero, drastic fluctuations, and failure of redundant systems to switch over in time;
Hidden Risks: Initial resistance changes are not easily detected, and the AI model may misinterpret them as "process fluctuations."
Practical Recommendations: Add circuit resistance monitoring during each shift's inspection, and use an infrared thermometer for verification.
4. Improper Redundancy Switching Logic Settings
Causes: Excessively high primary/backup temperature difference alarm threshold (e.g., >3℃) or excessively long response delay (>1 second);
Typical Manifestations: The primary sensor has failed but the system has not switched, leading to inaccurate temperature control and batch scrap;
Safety Blind Spot: The switching mechanism relies on software judgment; if the AI module itself malfunctions, it cannot be triggered.
Safety Standard: Set a switching threshold of >2℃ for 3 seconds, with a response time <0.5 seconds, ensuring production continuity.
5. AI Model Misjudgment and Inaccurate Health Assessment
Causes: Insufficient training data, failure to update the model due to environmental changes, or failure to incorporate new operating conditions (e.g., material switching);
Typical Manifestations: Misjudging normal sensors as aging, or still assigning high weight to abnormal points;
Long-Term Risk: Leads to distorted predictions of "virtual measuring points," affecting the accuracy of thermal field reconstruction.
Intelligent Recommendation: Implement self-learning health assessment by combining with the PHM system, regularly updating the training set to reduce the false alarm rate.
