Statistical Monitoring of Complex Multivariate Processes: With Applications in Industrial Process Control

Introduction

Performance assessment and quality control of complex industrial process systems are of ever increasing importance in the chemical and general manufacturing industries as well as the building and construction industry (Gosselin and Ruel 2007; Marcon et al. 2005; Miletic et al. 2004; Nimmo 1995). Besides other reasons, the main drivers of this trend are: the ever more stringent legislation based on process safety, emissions and environmental pollution (ecological awareness); an increase in global competition; and the desire of companies to present a green image of their production processes and products.

Associated tasks entail the on-line monitoring of production facilities, individual processing units and systems (products) in civil, mechanical, automotive, electrical and electronic engineering. Examples of such systems include the automotive and the aerospace industries for monitoring operating conditions and emissions of internal combustion and jet engines; buildings for monitoring the energy consumption and heat loss; and bridges for monitoring stress, strain and temperature levels and hence assess elastic deformation.

To address the need for rigorous process monitoring, the level of instrumentation of processing units and general engineering systems, along with the accuracy of the sensor readings, have consequently increased over the past few decades. The information that is routinely collected and stored, for example in distributed control systems for chemical ...