Data preparation (SPSS Modeler)

Data preparation

Based on the results of exploring the data, the following flow derives the relevant data and learns to predict faults.

This example uses the flow named Condition Monitoring, available in the example project installed with the product. The data files are cond1n.csv and cond2n.csv.

On the My Projects screen, click Example Project.
Scroll down to the Modeler flows section, click View all, and select the Condition Monitoring flow.

Figure 1. Condition Monitoring example flow

The flow uses a number of Derive nodes to prepare the data for modeling.

Data Asset import node. Reads data file cond1n.csv.
Pressure Warnings (Derive). Counts the number of momentary pressure warnings. Reset when time returns to 0.
TempInc (Derive). Calculates momentary rate of temperature change using @DIFF1.
PowerInc (Derive). Calculates momentary rate of power change using @DIFF1.
PowerFlux (Derive). A flag, true if power varied in opposite directions in the last record and this one; that is, for a power peak or trough.
PowerState (Derive). A state that starts as Stable and switches to Fluctuating when two successive power fluxes are detected. Switches back to Stable only when there hasn't been a power flux for five time intervals or when Time is reset.
PowerChange (Derive). Average of PowerInc over the last five time intervals.
TempChange (Derive). Average of TempInc over the last five time intervals.
Discard Initial (Select). Discards the first record of each time series to avoid large (incorrect) jumps in Power and Temperature at boundaries.
Discard fields (Filter). Cuts records down to Uptime, Status, Outcome, Pressure Warnings, PowerState, PowerChange, and TempChange.
Type. Defines the role of Outcome as Target (the field to predict). In addition, defines the measurement level of Outcome as Nominal, Pressure Warnings as Continuous, and PowerState as Flag.