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Condition monitoring
Last updated: Jan 18, 2024
Condition monitoring (SPSS Modeler)

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 . 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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