Condition Monitoring (Neural Net/C5.0)

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 error code on error (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 streams named condplot.str and condlearn.str, which reference the data files named COND1n and COND2n. These files are available from the Demos directory of any IBM® SPSS® Modeler installation. This can be accessed from the IBM SPSS Modeler program group on the Windows Start menu. The condplot.str and condlearn.str files are in the streams directory.

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

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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