Abstract

Condition Monitoring usually analyzes each measurement separately using static limit information. This results in false alarms and unhealthy conditions that are not alarmed. Using machine learning techniques, the big data gathered around large equipment or an entire plant can be analyzed as a single coherent whole to draw conclusions about its current state of health. First, a mathematical model of the relevant measurement is created using the other measurements available. This model represents the equipment or plant as a unit when it is operating as it should. Second, this expected value is compared to the measured value. If they agree, the current state is healthy. If they do not, an alarm is released and a maintenance activity must follow. This method is seen to be far more successful than standard condition monitoring thus preventing false alarms and always alarming unhealthy states.

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