Condition Monitoring And Health Assessment Of Centrifugal Compressor Units In Petrochemical Enterprise

The rapid development of the Internet has created a new industrial revolution and the entire industrial production system has been upgraded to a new level.In this background,the petrochemical industry is now moving toward large-scale,integrated,and intelligent.As an important power source for petrochemical factories,the compressor units will cause major economic losses or casualties when the fault happens.To ensure the safe operation of the compressor units is of great significance for the stable development of the factories.Therefore,it is necessary to carry out a thorough and careful research on the safe operation of the compressor units for the petrochemical enterprise.This work was carried out under the evaluation and diagnosis of equipment performance degradation promotion project based on big data of Sinopec Smart Factory.According to the research status at domestic and abroad,the working principle and structural characteristics of the compressor group used by a petrochemical enterprise were investigated.The vibration and process data acquisition model was established.Combining the vibration characteristics and manifestations of common faults,an example of condition monitoring analysis was presented.It is an evaluation reports for the enterprise and providing the basis for maintenance decisions.Moreover,a framework for evaluating the health status of the compressor units was funded.A horizontal and vertical health assessment model for the compressor units based on principal component analysis method,correlation analysis method,one-factor variance analysis method and k-means clustering method were established.The evaluation indicators were selected and the application scenes and functions was explained.According to the health assessment model,a compressor unit was divided into four sub-systems and the collected data were used to value the health status of the compressor units which was shown in a visual display.It has been proposed to use less amount of data instead of multiple quantities for real-time display which would be convenient for monitoring.The principal component analysis method was used to reduce the dimensionality of vibration characteristic values.6 principal components were selected as the new eigenvalues to represent the 12 original variables where 50%of the measurement points were reduced in dimension.The correlation values analysis method was used to reduce the dimensional values of the vibration measurement points of the units.The results showed that the shaft vibration of the same compressor group were significantly correlated and the horizontal and vertical axle displacements are highly correlated.The one-factor variation analysis method was used to compare the historical data of the axle displacement of the five different compressor units,and the health status of these five compressors were ranked.Furthermore,to observe the operation status of the compressor units based on time and to compare the units’historical alarm time in the monitoring system,the k-means cluster analysis method was used to classify the historical status of the units.The results indicated that when the clustering number k=3,the clustering results are basically consistent with the actual alarm time in the monitoring system when the fault occurs,and it can commendably reflect the actual fault state of the equipments.In the end,the above analysis will be prospected in the development of the petrochemical big data platform ProMACE~?industrial cloud and the fault prediction analysis.