Abstract: An extended partial least squares (EPLS) approach for the condition monitoring of industrial processes is described. This EPLS approach provides two statistical monitoring charts to detect abnormal process behaviour as well as contribution charts to diagnose this behaviour. A theoretical analysis of the EPLS monitoring charts is provided, together with two application studies to show that the EPLS approach is either more sensitive or provides easier interpretation than conventional PLS.

Abstract: Self-validating (SEVA) sensors implemented in a control process provide various metrics regarding sensed variables to a central control unit. Specifically, SEVA sensors provide measurements of the variables and validity information about the measurements, which may include fault information about the sensors themselves. A control unit utilizes the various SEVA metrics even when large numbers of SEVA sensors are used, a situation that is otherwise problematic due to difficulties in assimilating data from multiple SEVA sensors. Accordingly, the control unit distinguishes sensor faults from actual process changes, and responds as needed, even when large numbers of SEVA sensors are implemented together.

Abstract: An extended partial least squares (EPLS) approach for the condition monitoring of industrial processes is described. This EPLS approach provides two statistical monitoring charts to detect abnormal process behaviour as well as contribution charts to diagnose this behaviour. A theoretical analysis of the FPLS monitoring charts is provided, together with two application studies to show that the EPLS approach is either more sensitive or provides easier interpretalion than conventional PLS.

Abstract: A multi-level (hierarchical) process monitoring system comprises a process monitoring unit (14), at a higher level of the system, and a plurality of sensors (SEVA 1, SEVA 2 . . . SEVA 36) at a lower level of the system, at least one of the sensors having SEVA capability (self-validating capability), the sensors being adapted to provide respective measurement values of respective process variables to said monitoring unit, said monitoring unit being so arranged as to monitor the outputs of the sensors and to identify any significant apparent change in the process conditions as detected from an overview of said sensor outputs, and on detection of an apparent significant change, to request additional status information from at least one of the SEVA sensor/s to determine whether the apparent change is in reality due to a change in the characteristics of a particular SEVA sensor rather than an actual significant change in the process conditions.