Predict Out-Of-Specification issues BEFORE they occur

Multivariate Analysis and its application in Statistical Process Control

Multivariate Analysis (MVA) includes a variety of mathematical techniques which can handle multiple inputs and outputs, and the interactions which occur between them. These MVA techniques, such as Principal Component Analysis (PCA), Partial Least Squares (PLS) and Multiple Linear Regression (MLR) find application whenever the system or process under analysis is complex, interactive and/or contains highly correlated relationships. 

From spectral data created by analytical instruments, to categorical material data, to regular process sensor data, there are opportunities to collate, align, pre-process and ultimately determine regression models, and classifications to gain deeper understanding and assess predictability of the whole system.

Multivariate Statistical Process Monitoring (MSPM), is regularly used on both batch and continuous processes to characterise the process, determine statistical operational boundaries for “normal” or “good” operation, then monitor unseen operation in real time. The MSPM creates a robust envelope around the process which is used to:

  • Automatically determine the trustworthiness of any data that is critical-to-quality;
  • Create a benchmark of “Normal” operation parameters;
  • Check the interaction between all process data to highlight statistically significant changes in operation between the process variables
  • Identify the root cause of poor operation, for targeted maintenance and process improvement activities.

Perceptive's technology is available as on-premise or cloud-based platforms. Contact Us to find out more.

The Perceptive Platform

  • Simultaneously assesses multiple data sources and their interlinked relationships, rather than each signal in isolation
  • Includes a fully-configurable rule-based 'event' detection manager, to help maintain or improve operational performance
  • Perceptive's technology is available as on-premise or cloud-based platforms

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