The development & application of multivariate statistical techniques in process monitoring has gained substantial interest over the past two decades in academia & industry alike. Initially developed for monitoring & fault diagnosis in complex systems such techniques have been refined & applied in various engineering areas for example mechanical & manufacturing chemical electrical & electronic & power engineering. The recipe for the tremendous interest in multivariate statistical techniques lies in its simplicity & adaptability for developing monitoring applications. In contrast competitive model signal or knowledge based techniques showed their potential only whenever cost-benefit economics have justified the required effort in developing applications. Statistical Monitoring of Complex Multivariate Processes presents recent advances in statistics based process monitoring explaining how these processes can now be used in areas such as mechanical & manufacturing engineering for example in addition to the traditional chemical industry. This book: Contains a detailed theoretical background of the component technology. Brings together a large body of work to address the fields drawbacks & develops methods for their improvement. Details cross-disciplinary utilization exemplified by examples in chemical mechanical & manufacturing engineering. Presents real life industrial applications outlining deficiencies in the methodology & how to address them.
Includes:: numerous examples tutorial questions & homework assignments in the form of individual & team-based projects to enhance the learning experience. Features a supplementary website including Matlab algorithms & data sets. This book provides a timely reference text to the rapidly evolving area of multivariate statistical analysis for academics advanced level students & practitioners alike.