Fuzzy comprehensive assessment of running condition for a large-scale centrifugal compressor set

Abstract A fuzzy comprehensive assessment method of running condition was constructed and applied to a large-scale centrifugal compressor set in a petrochemical corporation aiming at the monitoring and early warning of abnormal conditions in industry. The maximal information coefficient (MIC) correlation analysis of indexes was introduced to determine the independent indexes to be assessed, and the dynamic deterioration degree was calculated using the predicted independent indexes by the second-order Markov chain model. The fuzzy membership degree weighting method was employed to assess the running condition of all units in the set. Simple and direct radar chart was used to visualize condition assessment grades. Results showed that the proposed fuzzy comprehensive assessment method successfully assessed the running condition of the set. The constructed method achieved 10 min earlier alarm than the traditional threshold alarm occurred at the specific moment of 00:44 on April 7 of 2018. The method would provide a valuable tool and have a wide engineering application in ensuring the safety and reliability of industrial production.

[1]  Carsten Schröder,et al.  Reasonable Sample Sizes for Convergence to Normality , 2014 .

[2]  Feng Qian,et al.  Total plant performance evaluation based on big data: Visualization analysis of TE process , 2018 .

[3]  Zhang Zai-li Analysis of the wind power forecasting performance based on high-order Markov chain models , 2012 .

[4]  Sun Guang-qiang Application of Markov Theory in Mid-Long Term Load Forecasting , 2011 .

[5]  T. Saaty,et al.  The Analytic Hierarchy Process , 1985 .

[6]  Junfei Qiao,et al.  Data-driven intelligent monitoring system for key variables in wastewater treatment process , 2018, Chinese Journal of Chemical Engineering.

[7]  V. V. Alekseev,et al.  Data measurement system of compressor units defect diagnosis by vibration value , 2017, 2017 XX IEEE International Conference on Soft Computing and Measurements (SCM).

[8]  Liu Zhixiang Method for On-line Operating Conditions Assessment for a Grid-connected Wind Turbine Generator System , 2010 .

[9]  Zaiwu Gong,et al.  Risk prediction of low temperature in Nanjing city based on grey weighted Markov model , 2014, Natural Hazards.

[10]  Lin Wang,et al.  A Review of Regional Ecological Security Evaluation , 2012 .

[11]  Fouad Slaoui-Hasnaoui,et al.  Wind Turbine Condition Monitoring: State-of-the-Art Review, New Trends, and Future Challenges , 2014 .

[12]  A. Testa,et al.  Very short-term probabilistic wind power forecasting based on Markov chain models , 2010, 2010 IEEE 11th International Conference on Probabilistic Methods Applied to Power Systems.

[13]  Zhe Chen,et al.  An improved fuzzy synthetic condition assessment of a wind turbine generator system , 2013 .

[14]  A. Shamshad,et al.  First and second order Markov chain models for synthetic generation of wind speed time series , 2005 .

[15]  Jin Fang Zhu Fault Tree Analysis of Centrifugal Compressor , 2011 .

[16]  Zhenyu Wang,et al.  Fuzzy synthetic condition assessment of wind turbine based on combination weighting and cloud model , 2017, J. Intell. Fuzzy Syst..

[17]  Wei Zhang,et al.  A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals , 2017, Sensors.

[18]  John N. Mordeson,et al.  Fuzzy Mathematics - An Introduction for Engineers and Scientists , 2001, Studies in Fuzziness and Soft Computing.

[19]  Catherine M. Burns Towards proactive monitoring in the petrochemical industry , 2006 .

[20]  Iqbal Gondal,et al.  Vibration Spectrum Imaging: A Novel Bearing Fault Classification Approach , 2015, IEEE Transactions on Industrial Electronics.

[21]  Seref Sagiroglu,et al.  Data mining and wind power prediction: A literature review , 2012 .

[22]  Jay Lee,et al.  Wind turbine performance assessment using multi-regime modeling approach , 2012 .

[23]  Ludmila A. Uvarova,et al.  Mathematical modeling : problems, methods, applications , 2001 .

[24]  Geoff Coyle Practical Strategy: Structured tools and techniques , 2004 .

[25]  Dong,et al.  Real-time Health Condition Evaluation on Wind Turbines Based on Operational Condition Recognition , 2013 .

[26]  Xiao Lei,et al.  A generalized model for wind turbine anomaly identification based on SCADA data , 2016 .

[27]  Simon J. Watson,et al.  Using SCADA data for wind turbine condition monitoring – a review , 2017 .

[28]  Peter Matthews,et al.  Classification and Detection of Wind Turbine Pitch Faults Through SCADA Data Analysis , 2020, International Journal of Prognostics and Health Management.

[29]  Keping Li,et al.  Identifying multi-variable relationships based on the maximal information coefficient , 2017, Intell. Data Anal..

[30]  Michael Mitzenmacher,et al.  Detecting Novel Associations in Large Data Sets , 2011, Science.