- Large number of process variables makes it hard to identify the KPIs which are responsible for any deviations or excursions in the process.
- Delay in realization of any excursion from the point in time when it happened.
- Monitoring becomes difficult for the operations which involve large number of variables
- Lack of knowledge about the correlated parameters
- Real-time process data, Batch and continuous operations
- Principal component Analysis to perform the root cause analysis, and identifying the critical parameters
- Extending the PCA, using t-score plots to realize any anomaly in the operations
- Time domain analysis to identify any deviations using the concept of Hotelling’s T2 plot
- Hotelling’s T2 plot enables the operator to take corrective actions against the identified deviations in near-real time
- The dimensionality reduction technique helps the operator to focus on the important process variables that contributes maximum to the variability of the operations
Sr. Data Scientist