# 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:

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 *  `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\.)

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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:

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| 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     |

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The following process is common to most data mining projects:

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 *  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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