AS ISO 13381.1:2014 Condition monitoring and diagnostics of machines—Prognostics
5.4 Multiple parameter analysis
Prognosis can be performed using a single parameter or multiple parameters. Multiple parameter analysis is the simultaneous display of all data within the one system. This concept is paramount to prognostics in that the relationship between parameters can be observed, not just the parameters themselves. This is particularly important for different yet possibly interdependent parameters, such as bearing temperature and oil viscosity (see Figure 4).
One principle of multiple parameter analysis is that the technique must trend both parameters (unfiltered/unprocessed variables) and descriptors (filtered/processed data) simultaneously. The use of narrow band filters allows spectrums to be divided into discrete elements of which the band amplitude can then be used for multiple parameter analysis trending. The failure definition set point for each narrow band is the assigned maximum allowable amplitude for each band.
This allows, for example, each narrow band amplitude to be plotted against other vibration descriptors, oil analysis results, process parameters and performance values, in order to identify and establish relationships between each of them.
The difficulty with the presentation of multiple-parameter analysis is that each variable has a different unit of measurement. This is compounded if the variable can attain the same value more than once during the life of the component (see Figure 4). Multiple-parameter analysis trending and alarming is also made difficult when the value of the variable in the failed condition is zero (e.g. flow or pressure).
One key difference between standard multiple-parameter analysis for monitoring and multiple-parameter analysis for prognosis is that prognosis requires a common severity axis.
For simplicity, this can be set to percentage of life usage, where 0 % life used occurs when the machine has not been operated and 100 % life used occurs when the machine is in the failed condition. At this stage, data that may approach zero when the machine is in the failed condition (such as flow or pressure) must be inverted to reflect the “% Life Usage” relationship.