Industry increasingly depends upon highly automated data acquisition and control systems to ensure that industrial processes are run efficiently and reliably while lowering the overall production costs. Data acquisition begins when a number of sensors measure aspects of an industrial process and report their measurements back to a data collection and control system. Such measurements come in a wide variety of forms. By way of example the measurements produced by a sensor include: a temperature, a pressure, a pH, a mass or volume flow of material, a counter of items passing through a particular machine or process, a tallied inventory of packages waiting in a shipping line, cycle completions, a photograph of a room in a factory, etc. Often sophisticated process management and control software examines the incoming data associated with an industrial process, produces status reports and operation summaries, and, in many cases, responds to events and to operator instructions by sending commands to controllers that modify operation of at least a portion of the industrial process. The data produced by the sensors also allow an operator to perform a number of supervisory tasks including: tailoring the process (e.g., specifying new setpoints) in response to varying external conditions (including costs of raw materials), detecting an inefficient/non-optimal operating condition or impending equipment failure, and taking remedial action such as moving equipment into and out of service as required.
A simple and familiar example of a data acquisition and control system is a thermostat-controlled home heating and air conditioning system. A thermometer measures a current temperature; the measurement is compared with a desired temperature range; and, if necessary, commands are sent to a furnace or cooling unit to achieve a desired temperature. Furthermore, a user can program or manually set the controller to have particular setpoint temperatures at certain time intervals of the day.
Typical industrial processes are substantially more complex than the above described simple thermostat example. In fact, it is not unheard of to have thousands or even tens of thousands of sensors and control elements (e.g., valve actuators) monitoring and controlling all aspects of a multi-stage process within an industrial plant. The amount of data sent for each measurement and the frequency of the measurements varies from sensor to sensor in a system. For accuracy and to facilitate quick notice and response of plant events and upset conditions, some of these sensors update and transmit their measurements several times every second. When multiplied by thousands of sensors and control elements, the volume of data generated by a plant's supervisory process control and plant information system can be very large.
Specialized process control and manufacturing and production information data storage facilities (also referred to as plant historians) have been developed to handle the potentially massive amounts of production information generated by the aforementioned systems. An example of such a system is the WONDERWARE IndustrialSQL Server historian. A data acquisition service associated with the historian collects time-series data from a variety of data sources (e.g., data access servers). The collected data are thereafter deposited with the historian to achieve data access efficiency and querying benefits and capabilities of the historian's relational database. Through its relational database, the historian integrates plant data with event, summary, production, and configuration information.
Traditionally, plant historians have collected and archived streams of raw data representing process, plant, and production status over the course of time. The status data are of value for purposes of maintaining a record of plant performance and for presenting and recreating the state of a process or plant equipment at a particular point in time. However, individual pieces of data taken at single points in time are often insufficient to discern whether an industrial process is operating properly or optimally. Further processing of the raw data often renders more useful information for operator decision making.
Over the years vast improvements have occurred with regard to networks, data storage and processor device capacity, and processing speeds. Notwithstanding such improvements, supervisory process control and manufacturing information system designs encounter a need to either increase system capacity and speed or to forgo saving certain types of information derived from raw data because creating and maintaining the information on a full-time basis draws too heavily from available storage and processor resources. Thus, while valuable, certain types of process information are potentially not available in certain environments. Such choices can arise, for example, in large production systems where processing raw data to render secondary information is potentially of greatest value.
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