The main function of a predictive diagnostic system is to identify short- and long-term operational risks to establish priorities in maintenance work. Predictive diagnostic systems provide information to the maintenance and operations staff on present and future risks that reduce machines readiness and performance.
The advantage of these systems is that they immediately detect and identify breakdowns to prepare a schedule of interventions that optimize maintenance costs, this considerably reduces costs caused by unexpected downtime or excessive preventive maintenance, and realizes maximum life of bearings, gears and other mechanical elements.
Rotating machinery can be monitored to follow the evolution of potential failures such as imbalances, misalignments, improper clearances, bearing failures, gear faults, electrical motor problems, hydraulic machine impulse, drive pulley and belt problems, weak banks and structural resonance.
A machine monitoring system does not need to be based exclusively on vibration information. Even though it is very significant to establish priorities for inspections, maintenance, modifications and scheduled downtime, any other parameter that indicates machine damage should be considered. An “asset health matrix” is defined to monitor the state of different failure modes of each machine on a single screen. This matrix includes information gathered by different technologies (vibrations, ultrasound, oil analyses, etc.) to obtain the machine’s status about its most sensitive failure modes. Viewing this data graphically makes it easy to locate alarms in continuously monitored machines that will alert staff about any mechanical failure or loss of functional condition. This optimizes the machine operation to reduce unplanned downtime owing to breakdowns.