This thesis presents a new proposal for load pre-dispatch considering the technical conditions
of the engines of thermoelectric power plants by combining several maintenance and
diagnostic techniques and using computational intelligence based on fuzzy logic. Diagnosis of
the technical conditions of the engines is done using lubricant analysis, vibration analysis, and
thermography. With this data and with the statistical analysis it is possible to predict when an
engine can fail and consider this in the load pre-dispatch. To increase engine reliability and
power supply, a Maintenance Management Program (MMP) is developed using management
tools, applying only 4 TPM (Total Predictive Maintenance) pillars and combined them with
predictive maintenance and diagnostics, thus allowing to reduce failures of plant equipment.
Some results achieved after the implementation are: reduction of the annual cost of
maintenance, reduction of corrective maintenance, increase of the MTBF (Mean Time
Between Failures) and decrease of MTTR (Mean Time To Repair) in all areas. In addition, the
proposed pre-dispatch scheme ensures the demanded power with a high degree of reliability
and quality.
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