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.