A DISTRIBUTION-BASED SPC ALERT RULE FOR LOW-COUNT OVERDISPERSED DATA: A CASE STUDY IN HEPA FILTER MAINTENANCE

Description

In pharmaceutical manufacturing, terminal high-efficiency particulate air (HEPA) filters are a critical barrier against airborne microbiota, yet maintenance is often performed on fixed schedules. This case study evaluates statistical process control (SPC) rules for low-count airborne Colony Forming Unit (CFU) data from Class C cleanrooms and assesses whether they can support HEPA filter maintenance decisions beyond a time-based program. Using 813 quarterly monitoring records collected over three and a half years from 79 HEPA filters, a distribution-based alert rule was developed by fitting a negative binomial model to discrete, overdispersed counts, combining discrete outlier diagnostics, a 95th-percentile control limit, and the Class C specification limit. The attached documents include the CFU data set, a Demo file data set, the Discrete SPC app and spc_core codes in R. Also a Readme file with the instructions on how to use the R code is included.

Steps to reproduce

Airborne viable particles in the Class C areas were monitored using active air sampling by impaction on agar plates, the primary technique for quantifying CFU per cubic meter at predefined critical locations within each room. A standard volume of 1.0 m³ of air was sampled at each monitoring event per location. Analytical variation is not considered in this study, so CFU counts are treated as observed. Total CFU were analyzed without differentiating by species or pathogen

Institutions

• Centro de Investigación e Innovación en Tecnologías de la Información y Comunicación

  Mexico City, Mexico City

Categories

Licence