1. Field of the Invention
The invention relates to the field of monitoring industrial process plants. More particularly, the invention relates to a method of process data mining.
2. Description of the Prior Art
Industrial process plants employ various means to monitor processes and equipment. These may be Intelligent Electronic Devices (IED) specially made for particular equipment or some mathematical modeling method. IEDs are designed around the concept of a feedback control loop based on traditional control theory. Usually set points are defined within the range of data; violations of these set points trigger an alarm mode. These methods, while excellent at determining violation of set conditions and/or adjusting the control loop accordingly when system parameters change, are usually computationally intensive, even for monitoring and controlling a single piece of equipment. The complexity of such systems increases exponentially when implemented at the plant level, let alone for a fleet-wide implementation.
On the other hand, most mathematical methods employed in the industry currently utilize a two step process for analyzing and monitoring data: a training step and a monitoring step. In the training step, the system “learns” the modeling parameters, based on a-priori data, and in the monitoring step, the system monitors the equipment or the process against the “learned” result.
The major disadvantage of using such a two step process for learning and monitoring is that inconsistent results may be observed if the model changes due to a changing physical process, corrupt data, sensor malfunction etc. In such cases, there are no sure ways to know why the results are inconsistent and this gives rise to the possibility of false alarm.
Another obvious limitation is that a “clean” or optimal set of data is required to train the model before monitoring can be performed. This initial optimal data set usually is not available for the real world and has to be obtained either from simulation or from filtering existing prior data. If the model developed in such a way has some errors, or if some unforeseen event is seen in the data, the model has to be retrained to account for such a data point. This retraining of the model introduces an added effort in the monitoring step.
What is needed, therefore, is a method for analyzing and classifying process data that does not require a clean set of data for training purposes, but that learns parameters on the fly by looking at changing data. What is further needed is such a method that optimizes the control data as more data is added. What is yet further needed is such a method that differentiates between key parameters and base parameters. What is still yet further needed is such a method that combines outputs of related sensors.