Data-Driven Approach toward Long-Term Equipment Condition Assessment in Sterile Drug Product Manufacturing.

Abstract

A two-stage data-driven methodology for long-term equipment condition assessment in drug product manufacturing is presented with a case study for a commercially operating aseptic filling line. The methodology leverages process monitoring data. Sensor measurements are partitioned using process information and maintenance schedules that are available on different databases. Data is processed to tackle heterogeneity in sources and formats. The data is cleaned to remove the effects of short-term variabilities and to enhance underlying long-term trends. Two approaches are presented for data analysis: first, anomaly detection using independent component analysis (ICA), where clusters of outliers are identified. The frequency and timing of such outliers yield important insights regarding maintenance schedules and actions. The second approach enables condition monitoring using principal component analysis (PCA). Long-term operational baselines are identified and shifts therein are linked with different process and equipment faults. This approach highlights the impact of equipment deterioration on shifting operational data baselines and shows the potential for the combined application of ICA and PCA for equipment condition monitoring. It can be applied within predictive maintenance applications where the installation of new specialized sensors is difficult, like in the pharmaceutical industry.