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Condition monitoring (SPSS Modeler)

Condition monitoring

This example concerns monitoring status information from a machine and the problem of recognizing and predicting fault states.

The data is created from a fictitious simulation and consists of a number of concatenated series measured over time. Each record is a snapshot report on the machine in terms of the following:

  • Time. An integer.
  • Power. An integer.
  • Temperature. An integer.
  • Pressure. 0 if normal, 1 for a momentary pressure warning.
  • Uptime. Time since last serviced.
  • Status. Normally 0, changes to an error code if an error occurs (101, 202, or 303).
  • Outcome. The error code that appears in this time series, or 0 if no error occurs. (These codes are available only with the benefit of hindsight.)

This example uses the flow named Condition Monitoring, available in the example project you imported previously. The data files are cond1n.csv and cond2n.csv.

For each time series, there's a series of records from a period of normal operation followed by a period leading to the fault, as shown in the following table:

Time Power Temperature Pressure Uptime Status Outcome
0 1059 259 0 404 0 0
1 1059 259 0 404 0 0
      ...      
51 1059 259 0 404 0 0
52 1059 259 0 404 0 0
53 1007 259 0 404 0 303
54 998 259 0 404 0 303
      ...      
89 839 259 0 404 0 303
90 834 259 0 404 303 303
0 965 251 0 209 0 0
1 965 251 0 209 0 0
      ...      
51 965 251 0 209 0 0
52 965 251 0 209 0 0
53 938 251 0 209 0 101
54 936 251 0 209 0 101
      ...      
208 644 251 0 209 0 101
209 640 251 0 209 101 101

The following process is common to most data mining projects:

  • Examine the data to determine which attributes may be relevant to the prediction or recognition of the states of interest.
  • Retain those attributes (if already present), or derive and add them to the data, if necessary.
  • Use the resultant data to train rules and neural nets.
  • Test the trained systems using independent test data.
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