Unsupervised process monitoring and fault diagnoses with machine learning methods - Aldrich, Chris; Auret, Lidia (Springer, 2013)
Although this book is focused on the process industries, the methodologies discussed in the following chapters are generic and can in many instances be applied with little modification in other monitoring systems, including some of those concerned with structural health monitoring, biomedicine, environmental monitoring, the monitoring systems found in vehicles and aircraft and monitoring of computer security systems. Of course, the emphasis would differ in these other areas of interest, e.g. dynamic process monitoring and nonlinear signal processing would be more relevant to structural health analysis and brain–machine interfaces than techniques designed for steady-state systems, but the basic ideas remain intact. As a consequence, the book should also be of interest to readers outside the process engineering community, and indeed, advances in one area are often driven by application or modification of related ideas in a similar field.