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:

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.