Monitoring phase

The Teach-In phase establishes the basic condition of any plant or machinery irrespective of its age. This basic condition is then used to detect any irregularities which may occur.
Irregularities are detected when limits established in the Teach-In phase are exceeded. This results in output to alarm lists in the form of observations and warnings.

Condition Monitoring detects irregularities which are not detected by other systems. Condition Monitoring evaluates irregularities as those,

• which develop over the mid-term, indicating an imminent crash.
• which develop over the long-term, indicating maintenance requirements (predictive maintenance).
• CMS also evaluates the intensity of the irregularities (linear and progressive development).

An important innovation is the conversion of process signals, firstly into normalized values and then into normalized statistical measurement values.
In this way physical measurement values are normalized in respect of amplitude and offset and can then be compared. It is irrespective whether the signals are temperatures, pressures, 20mA signals, frequencies, vibrations or any other type.
Statistical normalized values are established from the normalized values in the form of minutes, hours, days, weeks, and years. This enables the frequency of irregularities to be established. Obviously, irregularities occurring within seconds will have causal factors different to irregularities occurring over months or years.

Intensity of irregularities

(Linear and progressive irregularities)
The intensity of a malfunction can be established by the shape of the irregularities' line graph. There is a fundamental difference between irregularities which are linear and those which are progressive.

Alarms

Linear irregularities appear as observations in alarm lists.
Progressive irregularities appear as special warnings in alarm lists.
Entries include the source, time/date and time period as well as a text field.

Graphical output online / offline

Diverse and variable trend output over long time scales enable a quick but in-depth view of the state of the plant/machinery.
Time scales can be altered providing an overview from the actual current state and the state over a period of many years.