Systems for monitoring processes with multiple operating modes should be able to distinguish between changes in operating modes and developing faults. Process operators will make decisions regarding production and maintenance according to the information about faults, such as the occurrence of faults and the locations of faults, so that the process can run safely and efficiently. Whilst the development and application of kernel methods can improve the performance of monitoring systems, inappropriate usage of these methods can diminish the effectiveness of the methods.

In this thesis the industrial considerations of operators are summarized and these considerations are incorporated into the development of kernel methods for process monitoring. The research in the thesis shows that kernel methods need to be designed and implemented properly for monitoring processes with multiple operating modes. The research in the thesis also aims to develop kernel methods that can generate useful results when applied to monitoring of processes with multiple operating modes.

The thesis reports the following research outcomes:
• A benchmark multimodal dataset from a pilot-scale experiment rig;
• An investigation of the tuning of kernel methods and a tuning strategy for the radial basis function kernel;
• A new kernel that can improve the monitoring performance when applied to multimodal data;
• An on-line monitoring framework which can account for new operating modes in the process;
• A way to define the contributions of process variables to a fault detection, in order to support fault diagnosis.

The thesis delivers novel kernel methods for monitoring processes with multiple operating modes and gives guidelines for proper implementation of these methods. These outcomes extend the field of process monitoring. The research outcomes are relevant for industrial application because the practical considerations of end-users are incorporated in the development of the kernel methods. The thesis also contributes to the theory of process monitoring by proposing novel kernel methods for fault detection and diagnosis.

The results in the thesis demonstrate that the new development of kernel methods can improve monitoring performance when applied to processes with multiple operating modes. Moreover, the monitoring results achieved by the new kernel methods can be interpreted and used by process operators.