I. Disadvantages of Traditional Filter Maintenance and the Necessity of Intelligent Maintenance
HEPA and ULPA filters are core components ensuring the cleanliness of modern cleanrooms, biosafety laboratories, and high-end manufacturing workshops. The performance status of these filters directly determines the cleanliness level of the environment, thus affecting product quality and production safety. Traditionally, their maintenance mainly relies on two modes:
- 1. Periodic Replacement: Based on a fixed time cycle, filters are replaced upon expiration regardless of actual operating conditions. This method is simple but uneconomical, potentially leading to premature replacement of filter elements still within their lifespan, or failure to replace expired filters in time after a sudden contamination event.
- 2. Differential Pressure Monitoring: The degree of clogging is determined by monitoring the differential pressure across the filter. An alarm is triggered and replacement is performed when the differential pressure reaches a preset threshold. This is currently the most common method, but it has significant drawbacks: differential pressure is a lagging and non-specific comprehensive indicator.
The “hysteresis” is reflected in the fact that the pressure difference will only trigger an alarm when the filter element becomes clogged to a certain extent and airflow resistance increases significantly. This means the system may have been operating below its designed cleanliness level for some time.
“Non-specificity” refers to the fact that the increased pressure difference can be caused by various underlying reasons: it could be due to normal dust accumulation in the filter element, a failure of the mounting frame seal (leakage), or abnormal airflow caused by a decline in the performance of the air supply system’s fan. Based solely on pressure difference data, maintenance personnel cannot accurately determine the root cause of the fault, leading to inefficient troubleshooting and even incorrect decisions (such as misdiagnosing it as a filter element problem and replacing it, only to find out it’s a fan malfunction).
II. Core Data Flow: Building an AI -Powered Perception Nervous System
- Core Performance Data: The pressure difference across the filter is a core parameter directly reflecting its clogging status. It needs to be collected at a frequency of minutes or even seconds.
- Environmental Load Data: The particulate matter concentration in the upstream space directly reflects the filter’s “working intensity.” High particulate matter concentration periods mean accelerated filter load. In addition, ambient temperature and humidity affect air viscosity and the performance of certain filter materials (such as electrets), and are also important variables.
- System operation data: Air supply fan frequency, current, and airflow/speed sensor readings are used to determine whether the entire ventilation system is operating normally. This helps to differentiate between filter-related problems and system issues.
- Historical maintenance records: Filter installation time, model, and historical differential pressure baseline data provide a benchmark for individualized learning by the model.
III. Application of Machine Learning Models: Prediction, Classification, and Diagnosis
3.1 Remaining Service Life Prediction
By analyzing historical differential pressure growth curves and combining them with concurrent particulate matter load data, the dynamic patterns of filter clogging are learned. It not only determines “whether the current level is exceeded,” but also predicts “how many days it will take for the differential pressure to reach the threshold under the current operating conditions.” This provides a scientific basis for developing accurate maintenance plans and optimizing spare parts inventory, avoiding unplanned emergency shutdowns. For example,Trenntech in Frankfurt has begun piloting this predictive algorithm, expecting to improve filter utilization efficiency by more than 15%.
3.2 Failure Mode and Root Cause Diagnosis
- Category A: Normal filter media clogging. Slow and steady increase in differential pressure, positively correlated with upstream particulate matter concentration, with stable system airflow parameters.
- Category B: Frame/Seal Leakage. Characterized by slow or even abnormally low differential pressure growth (due to partial airflow short-circuiting at the leak point), but abnormally high downstream particulate matter concentration (especially larger particles), which is the most critical indicator of leakage. System airflow may appear normal.
- Category C: Abnormal System Airflow. Characterized by abnormal fan frequency or current, resulting in a decrease in measured airflow, causing changes in the differential pressure across the filter due to flow velocity changes, even if the filter is not severely clogged. The correlation between upstream and downstream particulate matter concentrations may become disordered in this case.
By training a model to identify these different data characteristic patterns, when the system issues an abnormal differential pressure warning, the most likely root cause judgment and confidence level can be provided simultaneously.
Applying artificial intelligence to HEPA/ULPA filter maintenance equips the filter with a “brain” and “predictive eyes.” It not only transforms the maintenance mode from “reactive” to “predictive,” but also avoids ineffective replacements and duplicate repairs due to misjudgments through accurate diagnosis. For semiconductor wafer fabs or large biopharmaceutical companies with tens of thousands of filters, the reliability and cost improvements brought about by this intelligent upgrade are scalable, marking the official entry of industrial facility operation and maintenance into a new era driven by data and algorithms.