DCS Alarm Capture

Alarm Management

Alarm Management & Rationalization Package
Alarm Management and Alarm Rationalization as EEMUA guileline.
Integration with realtime database (like PI, InfoPlus, or PHD)
Sophisticated Alarm Analysis
Alarm Documentation & Management
Justification for Alarm set value
Alarm Prediction

Real-Time Alarm Collection

Realtime Data Collection for Alarm, Operator Change, System Message in Windows PC.
Data Collection through DCS Printer port, TCP/IP, or OPC A&E
Standard Database like MS-ACCESS or MS-SQL
Query in MS-EXCEL and utilize the full function of MS-EXCEL
Save theAlarm, Operator Change, and System Message in separated field
Statistic Alarm Summary Report for Alarm Priority and Alarm Parameters
Statistic Operator Change Report for specified Operator Change
SMS for specified Alarm or Event
Automatic Database backp in networked PC

Control Loop Performance Management

Your plant has hundreds of control loops — all of them critical to your plant’s performance and all of them requiring on-going maintenance. The Control Monitor software package for monitoring controller performance assessment gives you the ability to effectively monitor the performance of all the DCS control loops in your plant, diagnose any loop problems, and easily re-tune any controller.
Custom analysis techniques developed by Control Arts provide the best estimate of controller performance. Unlike other packages that use the academic minimum-variance controller standard, The algorithms account for the real-world aspects of PID and model-based controllers. They can also account for the different objectives of level controllers.
Automatically analyze all the control loops in your plant on a scheduled basis, or perform an in-depth analysis on any single loop. And because the software ties into your plant historians, all analysis can be performed with a minimum of user interaction and customization.
It provides state of the art algorithms, simple user interfaces and direct connections to plant data sources.

It allows you to assess all aspects of controller performance. These functions are:

Controller performance history: Provides long term plots of all controller parameters, both continuous (setpoints, measurements, and outputs) and discrete (controller changes, alarms, and performance index).
Scheduled controller performance reports: Generate daily, weekly, or monthly reports indicating which controllers in your plant need attention.
In-depth single loop analysis: Determine the source of the problem for any under-performing controllers with a variety of graphical and statistical tests.
Level controller analysis: See how well your level controllers minimize surge while keeping the level between bounds.
Process Model Identification: Identify single and multi-loop plant models using the most efficient model identification techniques for obtaining low order or step weight models.
PID / MVC controller tuning: Use the process model to design either PID or multivariable controllers and then simulate the closed-loop plant response.
The best way to manage the hundreds of controllers in your plant is through a scheduled, automated report. The Control Arts Process Controller Manager is easily configured to automatically analyze any or all loops in your plant on a daily, weekly, or monthly basis. The program will then e-mail or print a summary report of the performance of all the loops, as well as any information on controller setting changes.
Sometimes analysis of long term trends is important. Is the control loop performing as well today as when it was installed? Are there too many operator changes to the controller settings? How well is the loop performing in alarm situations? To provide answers to these questions, the loop history display contains all the relevant data on one convenient display.
Quickly perform an in-depth analysis of a single loop and determine why the loop is not performing. A variety of tests quickly indicate whether valve stiction, poor tuning, or external disturbances are degrading performance. Just click on the tag of interest, specify the time range, and the performance assessment tests are automatically generated.
Once improper tuning is identified as the source of poor performance, you can easily obtain new process models and tuning parameters using the efficient model identification component. A few step tests allow you to identify low-order transfer functions that can be used in PID controllers, model based controllers, and inferential models. You can even identify the process in real-time because extensive statistical tests indicate whether you’ve got enough data for a good model.