Assuring safe and optimal operative conditions for centrifugal compressors is often understood as a primary goal for the process plant maintenance management teams. In the course of the last few decades, the approach to process plant machinery maintenance has progressively, but radically, changed. The initial corrective approach, with repair interventions after failures, was followed by the preventive approach, with parts replacement at fixed work hours.
For many and most common centrifugal machines the predictive techniques commonly implemented are, today, connected to vibrational conditions and mechanical indicators (mainly bearing vibrations and temperatures). But in many cases the appearance of defects is subtle, such as in situations where a degradation of the machine appears without being accompanied by comparison of evident mechanical signals. In such cases poor machine performance becomes progressively worse and starts to affect plant performance without triggering mechanical indicator alerts.
Process computers offer the computational capability required for the implementation of an accurate model-based performance monitoring methods bringing the traditional predictive systems into the proactive maintenance dimension. Advanced diagnostic capabilities can therefore arise from the coupling of machine performance analysis capability with the predictive methods based on the analysis of mechanical parameters (vibrational, thermal and tribological), thus not replacing but embedding and extending the previous system’s capabilities.