| Summary: | Compressors are widely used in the oil and gas industry, and have been so for many years. Previously, gas turbines where the standard driver for big offshore compressors, but as an ever increasing amount of attention is put on the emission of CO2 and NOx and the electric motor technology is developing this option is becoming more popular. This shift of drive technology introduces new possibilities for condition monitoring by the use of electrical signals from the motor. That is the background for this report. The objective of this report was to describe state-of-the-art condition monitoring techniques using electrical signals and to suggest new approaches to condition monitoring made possible by these techniques. In addition monitoring approaches was to be demonstrated on example data from Statoil facilities. This report starts with a brief introduction to the different maintenance techniques and the benefits gained by using condition based maintenance. Then a short description of a generic compression system and the main components is given. The most frequent failure modes for each component are presented along with the condition monitoring approaches capable of detecting them. Vibration analysis and performance analysis is shortly described before the condition monitoring techniques using electrical signals are presented. The dominating method is named Electrical Signature Analysis. This is a non-intrusive method which analyses the current and voltage frequency spectra. Many failure modes will appear as specific frequencies in this spectra, thus failure detection and diagnostics are possible by this method. Some examples of failure frequencies are given. By the use of this method mechanically and electrically related problems can be detected, not only in the drive, but also in the gear and driven equipment. Some examples of available ESA tools are presented, including MCM and ALLSAFE PRO. A new approach for condition monitoring by the use of classical methods and ESA combined is proposed. At the end a tool named Early Fault and Disturbance Detection, developed by ABB, is presented and a short analysis of process data is performed.
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