An example of a data mining project

Problem

• Detect and explain faults of a continuous pulp digester

Faults: drops in the output quality of the digester.

Solution

• A report which consists of– description of analyzed data,– analysis methods,– results,– conclusions, and– process improvement recommendations.

Problem understanding

• Several sources of information:– description of process instrumentation,– documentation of digester control system,– ISO 9000 documents,– interviews of operation personnel, process

engineers, researchers, and automation system vendor engineers.

Data acquisition

• About 200 on-line measurements

• Sampling rate 1 sample/10 minutes

• Data stored in SQL-database at the mill

Data acquisition

• Data acquisition procedure– a shell script run in SQL host twice a month– ftp-transfer of the data to HUT through firewall

by a mill computer operator– addition of the new data files after the existing

ones at HUT using shell scripts

Data acquisition

Data file format:

value1 checkbits1 timelabel1value2 checkbits2 timelabel2 . . . . . . . . .valueN checkbitsN timelabelN

Basic data preparation

• For each measurement channel:– check that the measurements are valid using checkbits

– check using timelabels if some samples are missing; if this is the case, fill in the empty gaps with NaNs

Data survey

• Visual data inspection (time series plots) revealed some problems:– some measurements didn’t work at all,

– some measurements worked properly, but not all the time,

– changes in production speed could be seen in most measurements, and

– process tuning altered the behavior of some measurements.

Data survey

• Computation of material balances provides a way to roughly estimate reliability of some sensors

• Process delay from input to output of the digester about three hoursDelay between different measurements in

different parts of the process had to be compensated

Data survey

• In order to get reliable results, only periods with constant production speed should be analyzed

Data modeling

• First, only temperature measurements in the digester sides were used

• Basic idea: to estimate the movement of chips using correlations between neighboring measurements

Failed

Data modeling

• Next, all available measurements were used

• The measurements were reduced to the ones best depicting the state of the digester

• The reduction was carried out using – process knowledge,– data visualization, and– correlation analysis.

Data modeling

• During the project, a digester modeling expert was consulted

• A model depicting the fault sensitivity of digester was created