The present version is 11.0. To upgrade a previous version, please contact Umetrics Sales.
Early fault detection and on-line quality control are the keys to an efficient process. Achieving these goals demands the use of advanced capabilities provided by multivariate technology. SIMCA-4000 is our state-of-the-art, Windows based software, providing multivariate statistical process control (MSPC) for continuous processes. Whether you have tens, hundreds or even thousands of incoming signals, SIMCA-4000 describes the current state of the entire process in a few simple plots. SIMCA-4000 brings advanced process monitoring and control capabilities to your current process control system in a single powerful and user-friendly package. Process Monitoring and Early Fault Detection SIMCA-4000 monitors all the process signals simultaneously, allowing you to detect changes and trends before they are seen in the individual signals. Multivariate methodology is the key to early fault detection and the system makes the operator aware of process deviations, and their causes in terms of individual signals, as they occur. Multivariate monitoring gives the engineers and operators a way to see impending failures. Adaptive Predictions Adaptive predictions can be used in SIMCA-4000 for processes with gradual shifts. Whether it is season to season or maintenance cycle to maintenance cycle variation, SIMCA-4000 employs adaptive predictions for model adjustment on the fly. Model lifetimes are lengthened, false alarms are reduced, and model rebuilds less frequent. "I have found SIMCA-4000 to be the most comprehensive and user friendly software available for online multivariable process monitoring. It enables us to take greater advantage of our data historian capabilities, to maintain process operations in statistical control, and to identify and correct abnormal process conditions." Dr. Bruce Wilson, PhD, Suncor Energy Inc.		DOWNLOADS AND LINKS Brochures Find a SIMCA-4000 brochure in the Downloads section. What is multivariate technology? The Methods menu give you a brief introduction to the methodology behind SIMCA-4000. The knowledge base article Frequently Asked Questions about Multivariate Data Analysis (Q391) may also be a help. Support See the Technical Support menu to find out how to get assistance with using SIMCA-4000. MIN SYSTEM REQUIREMENTS • Pentium 4 • 1 GB RAM • 40 GB free hard disk space • 1024x768, high-color (16 bits) • Windows NT 4.0, 2000 or XP Interfaces for Existing Systems The interface to the Data Acquisition System of the plant is done with a DLL file. Interfaces are available for: PI from OSI, Honeywell PHD, WinMOPS, OPC, ODBC, flat file reading. Interfaces can be easily tailor-made using the open API (Application Programming Interface) from Umetrics.
Integration with Your System SIMCA-4000 is built to seamlessly integrate into your existing data collection and control architecture. Using an API (Application Programming Interface), SIMCA-4000 can connect to most commercially available database historians. SIMCA-4000 has a robust client/server architecture with a dedicated server that performs all calculations in real-time and then sends the results up to the View client. Results can then be visualized through the View client across a LAN, WAN, or even via a dialup connection. Results can also be passed back to the historian for further communication to SCADA or DCS systems.		Three Implementation Levels Early Fault Detection: A plot showing how the new batch is different from the normal behavior. SIMCA-Batch On-Line can be implemented at three levels in your environment: 1. Monitoring The basic level is to use it for monitoring and early fault detection. This gives you better use of equipment, time and human resources. This promotes process understanding, delivery capacity and smoother customer relationships. 2. Prediction Predict the quality of your product before the process has finished. The benefits are obvious both in terms of improved quality and process knowledge gained. 3. Control The ultimate step in process analysis. Make automatic adjustments to your process as it evolves based on multivariate signals. Turn process upsets into controlled efficency.