Data-driven valve diagnosis to increase the overall equipment effectiveness in process industry

The avoidance of plant shutdowns is one of the highest priorities for plant operators (plant owners). Shutdowns are forced by abnormal situations, e.g. unexpected equipment faults such as valve or pump faults. Each unexpected fault can lead to hazardous situations within a plant. Pumps are already well analyzed compared to valves and also frequently used in process industry. In this paper a data-driven fault detection system for valves will be introduced. To gain additional knowledge about faults of specific equipment, big data technology is applied, based on a huge number of historical data for different valves. The paper introduces an approach in which data from different competitive companies operating several process plants are filtered, selected and combined with data from equipment manufacturers. The valve diagnosis system uses historical process data obtained across company borders using physical valve models to detect faults by comparing standardized flow coefficient determined by DIN IEC 60534-2-1.

[1]  christopher. a. teubert,et al.  I / P Transducer Application of Model-Based Wear Detection and Estimation using Steady State Conditions , 2013 .

[2]  Kamal Medjaher,et al.  Backlash fault detection in mechatronic system , 2007 .

[3]  Glenn Shevach,et al.  Towards Performance Prognostics of a Launch Valve , 2014 .

[4]  A. Alique,et al.  Intelligent process supervision for predicting tool wear in machining processes , 2003 .

[5]  M. Samadani,et al.  Fault Detection and Severity Analysis of Servo Valves Using Recurrence Quantification Analysis , 2014 .

[6]  Blair Brown,et al.  Prognostic modelling of valve degradation within power stations , 2014 .

[7]  Saeid Habibi,et al.  Failure monitoring in a high performance hydrostatic actuation system using the extended Kalman filter , 2006 .

[8]  Matthew Daigle,et al.  Validation of Model-Based Prognostics for Pneumatic Valves in a Demonstration Testbed , 2014 .

[9]  Rolf Isermann,et al.  Fault-Diagnosis Applications , 2011 .

[10]  Silvio Simani,et al.  Dynamic system identification and model-based fault diagnosis of an industrial gas turbine prototype , 2006 .

[11]  Rames C. Panda,et al.  Identification of Stiction Nonlinearity for Pneumatic Control Valve using ANFIS Method , 2014 .

[12]  S. Qin,et al.  A Curve Fitting Method for Detecting Valve Stiction in Oscillating Control Loops , 2007 .

[13]  Rolf Isermann,et al.  Model based fault detection of vehicle suspension and hydraulic brake systems , 2002 .